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Heterogeneity in the reporting of mortality in critically ill patients during the 2009-10
Influenza A (H1N1) Pandemic: A systematic review and meta-regression exploring the
influence of patient, healthcare system and study-specific factors.
by
Abhijit Duggal
A thesis submitted in conformity with the requirements for the degree of Master of Science,
Clinical Epidemiology and Health Care Research
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON,
Canada.
© Copyright by Abhijit Duggal 2015
ii
Heterogeneity in the reporting of mortality in critically ill patients during the 2009-10
Influenza A (H1N1) Pandemic: A systematic review and meta-regression exploring the
influence of patient, healthcare system and study-specific factors.
Abhijit Duggal
Master of Science, Clinical Epidemiology and Health Care Research
Institute of Health Policy, Management and Evaluation, University of Toronto
2015
Abstract:
Abstract:
Introduction: A systematic review with meta-regression to determine heterogeneity in reported
mortality associated with critical illness during the 2009-2010 Influenza A (H1N1) pandemic.
Results: We identified 219 studies from 50 countries that met our inclusion criteria. There were
significant differences in the reported mortality based on the geographic region and economic
development of a country. Mortality for the first wave of the H1N1 pandemic was non-
significantly higher than wave 2. In our hierarchical model the reported mortality was heavily
influenced by the need for mechanical ventilation.
Conclusion: While patient-based factors are influential in determining outcomes during
outbreaks and pandemics, the region and system of care delivery also influence survival.
Outcomes from a relatively small number of patients, early in an outbreak and from specific
regions may lead to biased estimates of outcomes on a global scale. This may have important
implications for global disease outbreak responses.
iii
Acknowledgements
The research included in this thesis could not have been performed if not for the support of many
individuals. I would like to express my sincere gratitude to my thesis mentor Dr. Rob Fowler, for
his immense support, patience, motivation. He has helped me through challenging times over the
course of the analysis and the writing of the dissertation I sincerely thank him for his confidence
in me. I could not have asked for a better mentor and advisor.
I would additionally like to thank Dr. Gordon Rubenfeld for his encouragement, insightful
comments, and his support in both the research and especially the revision process for this thesis.
I would also like to extend my appreciation to Ruxandra Pinto who has been an immense help
with the statistics and methodology of this thesis.
I would also thank my colleagues both at University of Toronto and Cleveland Clinic who have
provided valuable insight, stimulating discussions and have supported me through this process.
Finally I would like to extend my deepest gratitude to my family without whose love,
support and understanding I could never have completed this degree.
iv
Table of Contents
Acknowledgements………………………………………………………………………...……iii
Table of contents……………………………………………………………………………..….iv
List of abbreviations……………………………………………………………………………ix
List of tables……………………………………………..……………………………...….……xi
List of figures………………...………………………………………………………………….xii
List of appendices……………………………………..……………………………………….xiii
Chapter 1: Thesis overview……………………………..……………………………………….1
1.1 Problem statement……………………………………………………………………….1
1.2 Overview of the thesis………………………………………………………..…………..2
Chapter 2: Introduction…………………….......……………………………………………….3
2.1 Outbreaks, Epidemics and Pandemics……………………………………………..3
2.1.1 Major disease outbreaks during ancient times ………………………….3
2.1.2 Influenza outbreaks and pandemics of the twentieth century ………….4
2.1.3 Influenza outbreaks and pandemics of the twenty-first century….…….4
2.1.3.1 Severe acute respiratory syndrome (SARS)……….. ………….4
2.1.3.2 Influenza A (H1N1) pandemic…………………..………………5
v
2.1.3.2.1 World Health Organization definitions……...……….5
2.1.3.2.2 Critical illness during the H1N1 pandemic…………..6
2.1.3.2.3 Global disease burden associated with the H1N1…....6
2.1.3.2.4 Waves of the H1N1 pandemic……………...………….7
2.1.3.3 Middle East Respiratory Syndrome (MERS)……… ………….7
2.1.3.4 Influenza A (H5N1) ……………….……………………………..7
2.1.3.5 Influenza A (H7N9)……………… …………….………………..8
2.1.3.6 Ebola ………………….………………………………………….8
2.2 Limitations of reporting outcomes during disease outbreaks and pandemics...…8
Chapter 3: Critical Illness……………………………………….…………………………….10
3.1 Critical illness: A global perspective…………...………………………………….10
3.1.1 Global differences in critical care services…………..………………….10
3.1.2 World-bank economic development………………….………………….11
3.1.3 Geographic regions of the world…………..…………………………….11
3.2 Disease syndromes commonly associated with critical illness……………..…….12
3.2.1 Acute Respiratory Distress Syndrome (ARDS)……. ………………….12
3.2.1.1 Mechanical ventilation……………………………….…………12
vi
3.2.1.2 Rescue therapies for acute respiratory distress syndrome…..13
3.2.2 Sepsis, severe sepsis and septic shock……………………………………14
3.2.3 Acute kidney injury………………………………………………………15
Chapter 4: Objectives, and Research questions…………………………………………...….16
4.1 Objectives …………………………………………………...……………………....16
4.2 Research questions…………………………………………...……………………..16
Chapter 5: Material and Methods……………………………………………………………..18
5.1 Search strategy……………………………………...………………………………18
5.2 Study selection and eligibility criteria……………………………………………..18
5.2.1 Inclusion criteria……………………………...………………………….18
5.2.2 Exclusion criteria……………………………..…………………………..19
5.2.3 Eligibility criteria for study sub-groups ………………………….…….19
5.3 Data extraction and study variables………………………………………………21
5.4 Outcomes……………………………………………………………………………22
5.5 Quality assessment………………………………………………………………….22
Chapter 6: Statistical analysis………………………………………………………………….24
6.1 Descriptive statistics…………………………………...……………………………24
vii
6.2 Meta-analysis………………………………………………………………….…….24
6.2.1 Random-effects model ………………………………………….………..24
6.2.2 Tests for statistical heterogeneity……………….……………………….25
6.2.3 Ascertainment of publication bias…………………….…………………25
6.3 Subgroup analysis and meta-regression……………………..……………………26
6.3.1 Time as a factor in the reporting of mortality…………………………..27
6.3.2 Geography and economic development as a factor in the reporting of
mortality……………………………………………………………...………….27
6.3.3 Influence of specific ICU populations on the reporting of mortality.…28
6.3.4 Age as a factor in the reporting of mortality…………....………………28
6.3.5 Influence of single center or multicenter studies on the reporting of
mortality………………………………………………………………...……….28
6.3.6 Influence of the number of patients in a study on the reporting of
mortality………………………...……………………………………………….28
6.3.7 Mortality in specific sub-groups of critically ill patients…..…………...28
6.4 Hierarchical meta-regression………………………………………………………29
Chapter 7:
Results…………………………..……………………………………………………………….31
viii
7.1 Description of included studies…………………………………….………………31
7.2 Quality of included studies……………………...……………………………….…35
7.3 Meta-analysis………………………………………………………………………..36
7.4 Meta-regression…………………………….……………………………………….38
7.4.1 Reported mortality over time ……………………………...……………39
7.4.2 Age and reported mortality ………………………..…………………….39
7.4.3 Geographical area of the study and reported mortality………………..39
7.4.4 Economic status of the country and reported mortality………...……..42
7.4.5 Reported mortality in specific ICU populations………………………..44
7.5 Hierarchical meta-regression…………………..…………………………………..46
Chapter 8: Discussion…………………………...…………………….……………………..…51
Chapter 9: Conclusions and suggestions for future research…………………………..……56
Appendix…………….…………………………………………………………………………..78
ix
List of Abbreviations:
ARDS: Acute Respiratory Distress Syndrome;
AKI: Acute Kidney Injury;
AIDS: Acquired Immunodeficiency syndrome;
CDC: Centers for Disease Control and Prevention;
CI: Confidence Interval;
CPAP: Continuous positive airway pressure;
ECMO: Extracorporeal Membrane Oxygenation;
ESRD: end stage renal disease;
ETT: Endotracheal tube;
FiO2: Fraction of inhaled oxygen;
GFR: glomerular filtration rate;
HFOV: High frequency oscillatory ventilation;
HIV: Human immunodeficiency virus;
ICU: Intensive Care Unit;
IQR: interquartile range;
MAP: mean arterial pressure;
x
MeSH: medical subject headings;
MERS: Middle East Respiratory Syndrome;
NOS: Newcastle-Ottawa Scale;
NPPV: Non-invasive positive pressure ventilation;
PaO2: Partial Pressure of Oxygen;
PEEP: Positive end expiratory pressure;
PRISMA: Preferred reporting items for systematic reviews and meta-analyses;
RIFLE: Risk, Injury, Failure, Loss, and End-stage renal disease
SARS: Severe Acute Respiratory Syndrome;
SBP: systolic blood pressure;
SCCM: Society for critical care medicine;
SD: standard Deviation;
WBC: white blood cell;
WHO: World Health Organization.
xi
List of Tables:
Table 1: System and study based characteristics described in 219 studies from 213 articles.
Values are numbers (percentages) unless stated otherwise.
Table 2: Description of patient characteristics, intensive care specific interventions and
outcomes from included studies compared to the studies selected for the meta-regression and
hierarchical model respectively.
Table 3: Newcastle-Ottawa Scale describing the mean quality of studies based on different sub-
groups used in the meta-regression.
Table 4: Tests for evaluation of asymmetry of funnel plot to study publication bias
Table 5: Meta-analysis comparing the reported mortality from “early enrollment” (the Wave 1
for each individual country) during the H1N1 pandemic with studies describing prolonged
enrollment from the same countries. We evaluated the differences in the reporting of mortality
among the individual countries by using both a fixed effect and a random effect model.
Table 6: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical
ventilation based on the World Bank economic development classification.
Table 7: Patient characteristics from included studies. Differences in baseline characteristics
based on the studies only describing unselected critically ill patients, studies describing patients
undergoing mechanical ventilation, and studies describing patients under consideration or
actually getting ECMO.
Table 8: Hierarchical model with 3 levels (specific patient variables [need for mechanical
ventilation are treated as fixed effects] and study and economic development of the country are
treated as random effects with studies clustered within the economic development.
Table 9: Hierarchical model with two levels specific patient variables [need for mechanical
ventilation] is treated as a fixed effect against studies clustered within the economic development
of a country
Table 10: Hierarchical model with 3 levels specific patient variables [need for mechanical
ventilation] is treated as fixed effects and study and economic development of the country are
treated as random effects with studies clustered within the economic development.
xii
List of Figures:
Figure1: Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow
diagram. Study identification and selection process.
Figure 2: Funnel plot to assess for risk of publication bias.
Figure 3: Funnel Plot with Trim and fill effect revealing missing studies
Figure 4: Reported mortality associated with 2009 Influenza A (H1N1) associated critical
illness. We describe the mortality based on temporal (early, late and prolonged enrollment),
study (study size, single center compared to multicenter and adults compared to pediatrics), and
the geographic location and economic development from the included studies. The black
squares represent the point estimate and 95% confidence intervals (CIs) around the mortality for
each subgroup. The black diamond is the summary or overall combined estimate of mortality
associated with the 2009 Influenza A (H1N1) pandemic.
Figure 5: Differences in reported mortality based on different geographic variables for the
included countries (hemisphere, continent and World Bank designated geographical region). The
black squares represent the point estimate and 95% confidence intervals (CIs) around the
mortality for each subgroup. The black diamond is the summary or overall combined estimate of
mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical
regions is associated with the best discriminative power to report the differences in mortality in a
global context.
Figure 6: Differences in reported mortality based on subgroups of patients with different
severity of illness (need for mechanical ventilation), critical illness associated organ failure
(ARDS; AKI) or co-presenting conditions (pregnancy). The black squares represent the point
estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black
diamond is the summary or overall combined estimate of mortality associated with the 2009
Influenza A (H1N1) pandemic
xiii
List of Appendices:
Appendix 1: MeSH terms used for the systematic review.
Appendix 2: Figure: Flowchart for the subgroups for analysis for the meta-regression and the
hierarchical meta-regression.
Appendix 3: Reported mortality associated with 2009 Influenza A (H1N1) associated critical
illness for the studies used in the hierarchical meta-regression models. We describe the mortality
based on temporal (early, late and prolonged enrollment), study (study size, single center
compared to multicenter and adults compared to pediatrics), and the geographic location and
economic development from the included studies. The black squares represent the point
estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black
diamond is the summary or overall combined estimate of mortality associated with the 2009
Influenza A (H1N1) pandemic.
Appendix 4: Differences in reported mortality based on different geographic variables for the
included countries (hemisphere, continent and World Bank designated geographical region) for
the studies used in the hierarchical meta-regression models. The black squares represent the point
estimate and 95% confidence intervals (CIs) around the mortality for each subgroup. The black
diamond is the summary or overall combined estimate of mortality associated with the 2009
Influenza A (H1N1) pandemic. The use of geographical regions is associated with the best
discriminative power to report the differences in mortality in a global context.
Appendix 5: Differences in reported mortality based on subgroups of patients with different
severity of illness (need for mechanical ventilation), critical illness associated organ failure
(ARDS; AKI) or co-presenting conditions (pregnancy) for the studies used in the hierarchical
meta-regression models. The black squares represent the point estimate and 95% confidence
intervals (CIs) around the mortality for each subgroup. The black diamond is the summary or
overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.
Appendix 6: System and study based characteristics described in 219 studies from 213 articles
compared to the studies selected for the meta-regression and hierarchical model respectively.
Values are numbers (percentages) unless stated otherwise.
Appendix 7: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical
ventilation based on the geographic distribution of the different studies.
Appendix 8: List of Excluded Studies.
Appendix 9: Case Report Form for Included Studies.
Appendix 10: Components of Newcastle-Ottawa Scale
1
Chapter 1: Thesis Overview
1.1 Problem Statement
The H1N1 literature that informed the response to the pandemic, focused on the initial waves of
influenza. However, the small numbers of patients, the narrow focus of both the clinical
questions and physiological parameters studied and both a limited and early pandemic time
frame used in these studies may have led to biased estimates of outcomes associated with the
H1N1 pandemic, over a broader time frame. Since the conclusion of the pandemic period, some
publications have reported on the second phase or entire time period of the pandemic. However,
they have been dominated by experiences from developed countries, leading to a still unclear
understanding of the global impact of the H1N1 pandemic. These studies also failed to fully
describe the utilization of critical care resources in different geographical settings – developed
versus developing or least developed countries - where there may be great differences in capacity
and utilization of critical care, and the potential for difference in outcomes.
An accurate global estimate of both burden of illness and outcomes, how these vary across
jurisdictions, over time, and patient populations is important to quantify and would aid in
understanding the differences between early, selected populations and those representing the
entire pandemic period. Exploring the differences in reporting over time, different geographies,
and economic development status will help us understand likely differences in critical care
resource utilization, to help guide appropriate response and allocation of resources during future
pandemics, and to determine which factors are associated with extreme or more accurate
estimates of pandemic characteristics.
2
1.2 Overview of the Thesis
Chapter 1 details the overview of the thesis. Chapter 2 provides a background to the thesis and
includes a discussion of the epidemiology of disease outbreaks, with a detailed discussion of the
clinical and public health impact of the 2009 Influenza a (H1N1) pandemic at a global level. We
also discuss the epidemiologic reporting of the pandemic, and detail the response to critical
illness during the H1N1 pandemic. Chapter 3 focuses on critical illness, and the disease
syndromes associated with critical care. We also discuss the challenges of reporting on critical
illness in a global context. Chapter 4 describes the objectives of this thesis and the research
questions addressed. Chapter 5 discusses the methods used for the search strategy, the study
selection, the data extraction and the quality assessment tools used in the systematic review.
Descriptions of the study populations are also provided. Chapter 6 provides a detailed description
of the methods used in the Meta-analysis and Meta-regression. It also discusses the detailed
statistical analysis used in our meta-regression and our hierarchical meta-analyses of studies
reporting mortality associated with critical illness due to the 2009 Influenza A (H1N1). Chapter
7 highlights our results. Chapter 8 discusses the major findings and includes a comprehensive
discussion of the thesis limitations, and the clinical, policy and global health systems
implications of the results. Chapter 9 details the conclusions based on this thesis and also
discusses recommendations for future research.
3
Chapter 2: Introduction
2.1 Outbreaks, Epidemics and Pandemics
A disease outbreak is defined as the occurrence of a new disease or the reporting of a higher
number of new cases of a disease than would be normally expected in a defined geographical
area or temporal period 1. Disease outbreaks can occur in restricted geographical areas, or can
involve several countries. Similarly, outbreaks can last for anywhere from a few days to several
years 1. An epidemic is an outbreak that affects a large population in a more expansive
geographical area, usually over a relatively short period of time 2. An understanding of the usual
prevalence of a disease is important before the determination of an epidemic is made 2.
Propagation of epidemics is dependent on an adequate number of susceptible hosts to an
infectious agent. Epidemics that spread over several countries or continents, usually affecting a
large population are called Pandemics 3. Pandemics frequently present in multiple waves of
infections, where the numbers of infections and deaths can present in well-separated temporal
peaks with a separation time-scale of months.
2.1.1 Major disease outbreaks during ancient times
One of the earliest accounts of a recorded pandemic was the plague of Justinian4. Recent studies
have revealed that Yersinia pestis likely caused this pandemic. 4 Within two years it affected all
the Mediterranean countries. It eventually involved parts of Asia, North Africa, and Went as far
north as Ireland4. Historians trace the remnants of this outbreak over the subsequent two
centuries, and up to 18 attributed waves.
The “Black Death” is one of the most well-known historical infectious disease outbreaks.
Through genetic sequencing it has also been proven to be caused by Yersinia pestis 5. The
4
outbreak originated in Central Asia, and various accounts have traced it back to India, China, or
even the Russian steppes 6 It engulfed most of continental Europe in less than three years and
was responsible for destruction of entire cities 5. Some estimates attribute 50% of all mortality
during this time in Europe directly to the plague 5.
2.1.2 Influenza outbreaks and pandemics of the twentieth century
Influenza pandemics occur when a new strain of the influenza virus emerges, usually through
antigenic shift, for which there is little or no immunity in the human population7. The twentieth
century saw three influenza pandemics beginning in 1918, 1957 and 1968 by different strains 8.
Despite its name the 1918-19 “Spanish Flu” originated in the United States 9. The predominant
antigenic subtype was Influenza A (H1N1), and it infected almost one third of the world’s
population. It was unusually virulent, and is thought to have caused approximately 50 million
deaths within 2 years9. An Influenza A (H2N2) stain outbreak in China in 1957-58 was the
second influenza pandemic of the 20th
century10
. The “Asian Flu” was thought to have caused
about 2 million deaths globally10
. The 1968-69 "Hong Kong Flu", was an Influenza A (H3N2)
outbreak and killed approximately one million people worldwide7.
2.1.3 Influenza outbreaks and pandemics of the twenty-first century
2.1.3.1 SARS
Severe acute respiratory syndrome (SARS) coronavirus was first reported in Southern China and
Hong Kong in early 200311
. The illness spread quickly to involve more than 35 countries
throughout the world 11
and was notable for substantial nosocomial transmission. SARS had a
mortality rate of 9-12% in all confirmed cases, but it went as high as 50% in the critically ill
5
elderly12
. SARS transmission was controlled within a year, and no further outbreaks associated
with this viral illness have since been reported
2.1.3.2 2009-10 Influenza A (H1N1) Pandemic
The 2009-2010 Influenza A(H1N1) pandemic was declared due to infections caused by a then
variant of the Influenza A virus, that originated from animal influenza viruses and was unrelated
to recent human seasonal influenza A(H1N1) viruses. The first cases of disease associated with
pandemic H1N1 virus were reported in April 2009 from Mexico and the Southwestern United
States13, 14
. The disease spread quickly through the rest of the world and by 11 June 2009, WHO
had declared a pandemic phase 6 alert 15
. The 2009 H1N1 variant of influenza was the first
recognized Pandemic of the 21st Century 15
2.1.3.2.1 World Health Organization definitions of H1N1 Pandemic
WHO and CDC developed specific case definitions for 2009 H1N1 influenza 15-17
a. Confirmed H1N1: An individual with an acute febrile respiratory illness and laboratory-
confirmed pandemic (H1N1) 2009 virus infection by one or more of the following tests: real-
time (RT)-PCR or viral culture; viral culture; 4-fold rise in pandemic (H1N1) 2009 virus-specific
neutralizing antibodies.
b. Probable: An individual with an acute febrile respiratory illness who is positive for influenza
A by influenza RT-PCR, but is un-typeable by regents used to detect different strains; or,
positive for influenza A by an influenza rapid test or an influenza immunofluorescence assay
(IFA) and meets criteria for a suspected case.
6
c. Suspected: An individual with acute febrile respiratory illness with onset within 7 days of
close contact with a person who is a confirmed case of influenza A (H1N1) virus infection, or
within 7 days of travel to a community either locally or internationally where there are one or
more confirmed influenza A (H1N1) cases, or resides in a community where there are one or
more confirmed influenza A (H1N1) cases.
2.1.3.2.2 Critical Illness during the H1N1 pandemic
The 2009 H1N1 pandemic was associated with a higher rate of critical illness than seasonal
influenza. Even though overall mortality was comparable to seasonal influenza, the rates of
respiratory failure, requiring ventilator support and the use intensive care resources were much
higher in this cohort of patients 18
. A large proportion of critically ill patients not only required
invasive mechanical ventilation for hypoxemic respiratory failure, but many developed severe
Acute Respiratory Distress Syndrome (ARDS) 19
. These patients frequently required the use of
Extracorporeal Membrane Oxygenation (ECMO), and other “rescue” therapies20,21
.
2.1.3.2.3 Global Disease burden associated with the H1N1 pandemic
The H1N1 pandemic had a significant impact on the attributable mortality, in particular of young
patients, on a global scale22,23
. However, most of the studies describing the outcomes associated
with H1N1 failed to fully describe the utilization of critical care resources in different
geographical settings – developed versus developing or least developed countries. Two large
observational studies examining the global impact of H1N1 using administrative databases
acknowledged that Asia and Africa were vastly under-represented in their samples 22,23
. With
almost 40% of the world’s population living in these two continents, it becomes important to
examine the impact of the H1N1 pandemic at a global scale.
7
2.1.3.2.4 Waves of the H1N1 pandemic
The first wave of the 2009 pandemic in the North America began in March 2009 and peaked in
late June and early July 2009 13,18
. There were markedly fewer cases throughout August, and the
second larger wave peaked in late October and, early November. The first wave in the Southern
Hemisphere occurred from May 2009 till August 2009. Also while many countries (e.g. United
States and Canada) experienced at least two waves of infections during the 2009 pandemic, other
countries (e.g. China) experienced only a single predominant wave of infection 24
.
2.1.3.3 MERS-CoV
Middle East Respiratory Syndrome (MERS) is viral respiratory illness caused by a coronavirus
25.The initial outbreak had a very high reported mortality, but as more detailed epidemiologic
data has come forward, the mortality associated with confirmed MERS-CoV infection is thought
to be around 30% 26
. The first cases were reported from Saudi Arabia in 2012, but as of the
beginning of 2015, this outbreak has been reported from 22 countries 27
. Almost all cases are
linked to the Gulf region 27
.
2.1.3.4 Influenza A (H5N1)
The H5N1 avian flu is a highly pathogenic virus that has been reported to have infected humans
in small clusters since 2003 28
. Most cases have originated in Asia and the Middle East, and the
transmission is through poultry. Sustained human-to-human spread has not been reported, but
initial epidemiologic surveillance has reported a very high mortality (60%) associated with this
virus 28
.
8
2.1.3.5 Influenza A (H7N9)
Initial outbreaks of avian influenza A(H7N9) in humans were reported from China in 2013 29
.
Infection due to this virus is associated with severe disease in humans, and most patients develop
respiratory failure 30
. Reported mortality is close to 30% 30
. No evidence of sustained person-to-
person spread of H7N9 has been found, though some evidence points to limited person-to-person
spread in rare circumstances. 29
2.1.3.6 2014 Ebola Pandemic
Ebola virus disease was first described in 1976, and has been implicated in multiple isolated, but
brief outbreaks in sub-Saharan Africa 31
. The 2014 outbreak has had a devastating effect on
multiple West African countries 32
. It is the most widespread epidemic associated with this
filovirus 33
. The initial reported case fatality rate is 60% 33,34
. This outbreak has also been
significant due to the impact of secondary infections in health care workers 35
. As of February 4,
2015, there have been 22,495 confirmed, probable or suspected cases of Ebola among 9
countries, with an estimated mortality rate of 40% 34
.
2.2 Limitations of reporting outcomes during disease outbreaks and pandemics
Many studies discussing the clinical characteristics, possible treatment options, at-risk
populations and clinical outcomes are published early in the course of any new disease outbreak.
Initial reports focus on a limited number of very sick patients. There is a high risk of introducing
a selection bias in reported outcomes, disease outbreaks are studied over short periods of time, or
focus on select groups of patients that are, initially, most readily detected due to severe illness
26,36. This phenomenon has been seen in most of the reported outbreaks over the last decade.
Initial reports of the MERS-CoV outbreak reported extremely high rates of mortality (50% for
9
MERS-CoV) 26
. These reported outcomes were based on case reports and small series associated
with these diseases. The H1N1 Pandemic is unique as it is one of the first disease outbreaks that
occurred during the modern epidemiologic surveillance times, reported at a global level. The
prolonged duration, and multiple waves also made this Pandemic an ideal outbreak for
epidemiologic reporting and analysis.
Critical illness associated with outbreaks is difficult to evaluate because of the inherent
difficulties with recognition of clinical syndromes such as sepsis and acute respiratory Distress
syndrome 37
Moreover there are no gold standards or global benchmarks for standardized
treatment of these patients 37
. Also most studies also fail to fully describe the utilization of
critical care resources in different geographical settings – developed versus developing or least
developed countries - where there may be great differences in capacity and utilization of critical
care. Similar to other disease outbreaks the reported mortality in critically ill patients during the
H1N1 pandemic was extremely variable - reported to be anywhere from 11% to 48% in different
studies 20,38-40
. Mortality was much higher in smaller case series based on initial experiences
from single centers, 36
and for cohorts of critically ill patients undergoing specific interventions
20,38 or diagnosis
39,40 associated with the H1N1 pandemic. These results may have led to biased
estimates of some patient characteristics and the outcomes associated with the H1N1 pandemic,
over a broader time frame.
10
Chapter 3: Critical Illness
There is no single accepted definition of critical illness. However, patients with critical illness
often (but not always) have high complexity of disease, associated with actual or a high risk of
organ dysfunction. Critical illness syndromes can be difficult to diagnose, often have a short
prodrome, and usually are associated with higher mortality than patients with similar spectra of
comorbid conditions and acute presentations without critical illness 41
. Disease syndromes such
as septic shock, and organ dysfunction such as acute respiratory distress syndrome, and acute
renal injury are closely associated with the development of critical illness.
3.1 Critical illness: A global perspective
Most chronic diseases including cancers, cardiovascular disease, and infectious outbreaks such
as tuberculosis and HIV/ AIDS have reliable global epidemiologic data 42,43
. This allows for an
attempt at assessment of differences in outcomes, and care delivery among patients all over the
world. Unfortunately comparative studies for critical illness syndromes are hampered by a
number of factors such as a lack of standardized definitions of disease syndromes, a heavy
reliance on resources for critical care services and a lack of trained personnel 37,41
.
3.1.1 Global differences in critical care services
Critical care services vary tremendously throughout the world 44
. The availability of resources,
the overall economic status of a country and its citizens and the systems in place for life-
sustaining therapies all impact the use of these services in different countries 45
. There are
significant challenges in defining and quantifying the capacity to provide critical care among
different countries. Studies have evaluated the differences in critical care services based on
geographic variables 44
. Most studies report on countries or continents when explaining the
11
differences in care for hospitalized or critically ill patients 44
.Socioeconomic status has also been
used to describe the differences in resources and outcomes in studies 45
. For the purpose of this
thesis we decided to use different geographic variables such as continents and hemisphere, as has
been used in previous studies. However, we also decided to compare the World Bank geographic
regions as they provide a mix of the socioeconomic and geographic variables and can be used a
more effective variable to describe differences in resource utilization and outcomes at a global
level 46
.
3.1.2 World Bank Economic Development
The World Bank classifies the vast majority of the world’s countries into one of four broad
categories based on the per capita income: low income economies, lower-middle income
economies, upper-middle income economies and high income economies. 46
The composition of
these groupings is intended to reflect basic economic country conditions
3.1.3 Geographic regions of the world
Most studies evaluating the global burden of disease describe differences between populations at
a country level 44
. It is difficult to accurately compare such differences in critical illness because
of the inherent differences in patients and resources in different countries 41
. A number of studies
have described these differences at the level of different regions and continents 44
. For this thesis
we explored the differences in outcomes at the level of continents, and then based on
geographical region of the included countries. We used geographical regions based on the World
Bank classification as follows: North America, Latin America and Caribbean (Mexico is
included in Latin America and not North America based on this classification), East Asia and
12
Pacific, Eastern Europe, Middle East and North Africa, Sub-Saharan Africa, South Asia,
Western Europe, Australia and New Zealand
3.2 Disease Syndromes associated with Critical illness
3.2.1 Acute Respiratory Distress Syndrome (ARDS)
We defined ARDS based on the Berlin definition 47
. Even though this definition was formulated
after the 2009 Influenza A (H1N1) pandemic, we decided to use this as it is the most appropriate
definition for a diagnosis of ARDS. ARDS was defined as: I. Bilateral opacities, unexplained by
nodules, atelectasis or effusion on either chest radiograph or CT scan; and II. New or worsening
respiratory symptoms or a clinical insult associated with ARDS within 7 days of diagnosis; and
III. Objective assessment of cardiac function with modalities such as echocardiography to
exclude cardiogenic pulmonary edema and; IV. Hypoxemia, with a PaO2/FiO2 ≤300 mm Hg
despite non-invasive or Invasive mechanical ventilation with a PEEP (Positive End Expiratory
Pressure) or Continuous Positive Airway Pressure (CPAP)≥ 5 cm H2O 47
.
3.2.1.1 Mechanical Ventilation
Mechanical ventilation is a method to mechanically assist spontaneous or absent breathing
attempts. It is the use of positive pressure to force a predetermined mixture of air into the central
airways and alveoli of the lungs. This positive pressure ventilation can be provided either
invasively (with the means of an endotracheal tube) or non-invasively (with the use of nasal, or
full face masks). For the purpose of this study we defined mechanical ventilation as the use of
any device used to provide positive pressure ventilation to the patients. We defined non-invasive
mechanical ventilation as the use of facemasks to provide non-Invasive positive pressure
ventilation (NPPV), bilevel pressure ventilation, or continuous positive airway pressure (CPAP).
13
Invasive mechanical ventilation was defined as the use of positive pressure ventilation with any
conventional or non-conventional mode of mechanical ventilation with the means of an
endotracheal tube (ETT)
3.2.1.2 Rescue therapies
Rescue therapies are defined as the use of adjunctive clinical strategies in patients with severe
hypoxemia. Rescue therapies include the following therapeutic interventions (prone position
ventilation, high-frequency oscillatory ventilation (HFOV), airway pressure release ventilation
(APRV) and extracorporeal membrane oxygenation (ECMO).
High-frequency oscillatory ventilation (HFOV)
High-frequency oscillatory ventilation (HFOV) provides pressure oscillations around a relatively
constant mean airway pressure at very high rates (3–15 breaths per second). As a result very
small tidal volumes are achieved with active inspiration and expiration. 48
Although commonly
used as a rescue therapy in 2009-2010, with the publication of recent clinical trials demonstrating
potential harm, HFOV is no longer widely recommended as a rescue strategy.
Airway pressure release ventilation (APRV)
Airway pressure release ventilation (APRV) is a form of pressure control intermittent mandatory
ventilation (PC-IMV) typically used in the setting of ARDS and severe hypoxemia. During
APRV, airway pressure is set at 2 levels, sometimes called for 2 time periods and effectively
raises the mean airway pressure, recruits and helps to maintain open alveoli that can then
participate in gas exchange. The effect on clinical outcomes of patients with ARDS is
uncertain49
.
14
Extracorporeal membrane oxygenation (ECMO)
Extracorporeal membrane oxygenation uses degrees of cardiopulmonary bypass technology to
provide gas exchange and to augment blood flow. In patients with severe hypoxemia this
modality can increase oxygenation and ventilation while allowing a lung protective ventilation
strategy with low tidal volume breaths. With the advent of new technology such as veno-venous
circuits and smaller cannulas, the use of ECMO has gained more acceptance as a therapy in
patients with ARDS. This trend was seen with the use of ECMO in patients with severe or
refractory hypoxemia associated with ARDS during the H1N1 pandemic 50
.
Prone Position Ventilation
Prone position ventilation is the use of invasive mechanical ventilation to patients in the prone
(lying on the chest and abdomen as opposed to lying on the back) position 51
. The use of this
intervention has been associated with a significant risk reduction in mortality in one clinical
trial52
.
3.2.2 Sepsis /Severe Sepsis and Septic Shock
Sepsis, severe sepsis and septic shock have been defined based on an international consensus
statement developed by the Society for Critical Care Medicine (SCCM) Surviving Sepsis.53
Sepsis is defined as the presence (probable or documented) of infection together with systemic
inflammatory manifestations. Severe sepsis is defined as sepsis plus sepsis-induced organ
dysfunction. Septic shock was defined as sepsis-induced hypotension persisting despite adequate
fluid resuscitation, which may be defined as infusion of 30 mL/kg of crystalloids bolus over 10-
15 minutes.53
15
Vasoactive Medications
Medications that induce vasoconstriction and thereby elevate mean arterial pressure (MAP) are
called vasopressors. Inotropes are medications that increase cardiac contractility 54,55
. Many
drugs have both vasopressor and inotropic effects. For the purpose of our study we defined the
use of the common vasopressors (e.g. norepinephrine, vasopressin, epinephrine, dopamine and
phenylephrine) or Inotropes (e.g. dobutamine, milrinone) as vasoactive medication use.
3.2.3 Acute Renal Failure
Acute Renal failure is defined as the worsening of serum creatinine and glomerular filtration rate
(GFR), a decrease in the urine output with a risk of progression to chronic renal insufficiency or
failure. Recently acute renal failure has been defined based on the Risk, Injury, Failure, Loss,
and End stage renal disease (RIFLE) Criteria. The changes in the serum creatinine, urine output
and glomerular filtration rate (GFR) help in defining the severity of disease. Worsening kidney
dysfunction is labeled as Risk, Injury, and Failure respectively. The RIFLE criterion uses short
and long term outcomes to define Loss and ESRD. 56
16
Chapter 4: Objective and Research Questions
4.1 Objective
The primary objective of this systematic review was to determine the mortality of critically ill
patients with Influenza A (H1N1) during the 2009-2010 pandemic.
Our secondary objective was to determine how patient, healthcare system and study-specific,
factors influence reported mortality. We examined the differences in outcomes based on the time
period of the study, the geographical location (the continent, the geographic region and the
specific hemisphere) of the study population, developed or developing country status based on
the World Bank designation, and whether the study included unselected critically ill patients, or
specific subgroups of critically ill patient populations. We also determined length of stay in ICU
and hospital, and the frequency and duration of mechanical ventilation, among appropriate
studies.
4.2 Research Questions
The following research questions, organized by topics are addressed by this thesis:
1. What is the most valid estimate of mortality associated with critical illness during the H1N1
pandemic?
2. Are there differences in reported mortality based on the time of enrollment of patients during
the H1N1 pandemic?
3. Are there differences in reported mortality based on patients with specific disease syndromes
(ARDS, AKI), therapeutic interventions (mechanical ventilation, ECMO) or co-presenting
conditions (pregnancy).
17
4. Are there differences in reported mortality trends based on different geographic region and
socioeconomic status of a country?
5. What is the combined impact of study, system and study level data pertaining to patient-
characteristics on the reported mortality during the H1N1 pandemic?
18
Chapter 5: Materials and Methods
5.1 Search Strategy
We searched Medline (January week 1, 2009 to June week 3, 2013), Embase Classic + Embase
(2009 week 1 to 2013 week 28), LILACS and African Index Medicus for studies that evaluated
mortality associated with critical illness in confirmed, probable or suspected cases of 2009-2010
Influenza A (H1N1) infection (For detailed search strategy see Appendix). We reviewed the
references of all retrieved studies and review articles to identify any additional studies. We
considered full text articles published in any language. We did not consider abstracts or other
material presented at medical conferences or unpublished data. The full text of any citation
considered potentially relevant was retrieved. The research and ethics committee of our
institution waived the need for patient-level consent for this study as only aggregate and
previously published data was collected.
5.2 Study Selection and Eligibility Criteria
5.2.1 Inclusion Criteria: We included studies that met the following a priori defined criteria: (1)
described confirmed, probable or suspected cases of 2009-2010 influenza A (H1N1) infection;
and, (2) described patient(s) who were critically ill. Critical illness was defined by
admission to an adult or pediatric intensive care unit (ICU) or area of the hospital where
critically ill patients routinely receive treatment; or, patients receiving invasive or non-invasive
mechanical ventilation; or, patients receiving continuous intravenous vasoactive medications; or,
another criteria with justification presented in the individual study to designate patients as
critically ill.
19
5.2.2 Exclusion Criteria: We excluded any study that met the following criteria: (1) case series
describing fewer than 5 patients; (2) studies that did not report mortality in critically ill patients;
57 studies that only described characteristics of patients who died.
A detailed flowchart based on Preferred reporting items for systematic reviews and meta-
analyses (PRISMA) guidelines 58
of the studies included in our systematic review is provided
(Figure 1).
5.2.3 Eligibility criteria for different sub-group of studies
We anticipated that many early and potentially smaller studies would describe patients
subsequently included in multicenter or national studies. To prevent non-independent reporting
of patient characteristics and outcomes, we included studies only representing unique patient
populations for the description of outcomes over different geographical or economic regions and
specific ICU populations; however we included studies with potentially duplicated patients for
description of outcomes over time, and for single versus multiple centers comparisons. One of
the key statistical challenges therefore was to ensure that our estimates were not affected by the
duplication of data due to multiple manuscripts describing the same patients. Therefore, we
divided all the manuscripts based on the country of enrollment of the patients. We then further
evaluated whether the manuscripts were a part of a national database, or not. If they were, we
recognized them as being non-duplicate only if they were reporting on cases from different time
periods of the pandemic. For studies that were performed in countries without a central data
collection mechanism, we reviewed the information on the included medical centers reported in
the manuscript, and a study was recognized as being a non-duplicate study only if the centers
were different, or if the same centers reported outcomes at different time points. Different
20
articles were used to describe the effect of system, study and patient based variables, so we have
described our methodology for all these groups in detail (Appendix):
Time as a factor in the reporting of mortality: For this analysis we excluded duplicate studies
(both databases, and studies with overlapping patients or reporting a similar time period) and
studies with only pediatric patients (as pediatric mortality was low and not comparable to adult
patients).
Geography and economic development as factors in the reporting of the mortality: We
collected the data on mortality from different countries; we excluded duplicate studies (from
similar databases, or reporting on overlapping patients during similar time period). As there was
significant heterogeneity in the severity of disease, occurrence of organ failure and the use of
ICU specific therapies in studies from different geographical domains, we also identified the
differences in mortality among “unselected” critically ill adults, to examine difference in the
reporting of mortality in a homogenous group of studies at a global level and to obtain the most
valid estimate of mortality among critically ill patients world-wide.
Influence of specific ICU population on the reporting of mortality: We excluded duplicate
studies (any study that might have reported similar patients were screened, and the only studies
describing patients over non-overlapping times for each country were included). We excluded
studies reporting on only pediatric populations.
Age as a factor in the reporting of mortality: We report mortality from non-duplicate studies
for pediatric, adult and both pediatric and adult cohorts.
Influence of single center or multicenter studies on the reporting of mortality: We excluded
duplicate studies (any study that could have reported similar patients was screened, and only the
21
study that reported on the most number of patients for the longest time period were included,
specific group of patients reported at different times for each country were included).
Influence of the number of patients in a study on the reporting of mortality: We included all
the studies that met our inclusion criteria
Mortality in specific sub-groups of critically ill patients: We selected non-duplicate studies in
adults.
Hierarchical meta-regression model: We excluded duplicate studies (any study that could have
reported similar patients were screened, and the studies that reported on patients at non-
overlapping times for each country were included). We also excluded studies reporting on only
pediatric populations.
5.3 Data extraction and study variables
Study characteristics and key results were abstracted by one author (AD) using a standardized
study report form. The primary outcome of mortality was abstracted from each study
independently by two authors (AD, RF). Disagreements were resolved by consensus. We
collected geographic (country, hemisphere, region and continent) variables and economic (World
Bank designation) designation for each country (Country and Lending groups, The World Bank);
whether the study included unselected (consecutive) or selective (non-consecutive) critically ill
patients, or specific patient populations (e.g. adults or pediatric patients, only mechanically
ventilated patients, only patients receiving rescue oxygenation therapy, only those with specific
organ injury such as ARDS or acute renal injury); the duration of the study (based on the months
and year of inclusion of the first and last patients of the study) and also whether the study period
reported on the region-specific first wave, second wave, third wave or more than one wave of the
22
pandemic. We also collected study level data pertaining to patients: severity of illness using the
Acute Physiology and Chronic Health Evaluation (APACHE) II/III/IV, Pediatric Risk of
Mortality (PRISM) II/III, sequential organ failure assessment (SOFA) or Simplified Acute
Physiology Score (SAPS)II/III; age (overall, and among adults and children <18 years); sex; co-
morbidities including obesity, diabetes, congestive heart failure, cerebrovascular disease,
neoplastic disorders, chronic liver, or renal diseases; and the presence of immunosuppression.
We collected data on co-presenting conditions such as pregnancy or post-partum status (detailed
definitions of all variables provided in Appendix).
5.4 Outcomes
The primary outcome of interest for this systematic review was to determine mortality of
critically ill patients with Influenza A (H1N1) during the 2009-2010 pandemic. As mortality was
variably reported using different time points in each study, we preferentially used the hospital,
then 1 month, then in-ICU mortality, whichever represented the longest period of follow-up.
5.5 Quality Assessment
We used the Newcastle-Ottawa scale (NOS) to assess the quality of included studies. 59,60
Newcastle-Ottawa Scale was developed to assess the quality of non-randomized studies (both
cohort and case-control) to help with the interpretation of meta-analytic results 61
. Observational
studies have specific challenges associated with their implementation and conduct. The NOS is
undergoing constant refinement, but its content validity has been established based on critical
review of the items by several experts in the field who evaluated its clarity and completeness for
the specific task of assessing the quality of studies to be used in a meta-analysis 61
. Its content
validity and inter-rater reliability have been established 61
. Its criterion validity with comparisons
23
to more comprehensive but cumbersome scales and its intra-rater reliability are currently being
examined. The scale allocates up to 9 points to evaluate the risk of bias in cohort or case-control
studies in 3 domains: selection of study groups (4 points), comparability of groups (2 points),
and ascertainment of either exposure or outcome (3 points). As we were not comparing two
distinct groups of patients we evaluated the risk for under- or over-reporting of mortality based
on the three domains of the scale. We used a modified NOS to assess the appropriateness of
selection, and follow up of these patients and defined the risk as being high for studies with a
score of 6 or lower.
24
Chapter 6: Statistical Analysis
6.1 Descriptive Statistics
We combined data from the included studies to estimate in-hospital mortality associated with the
H1N1 pandemic. Categorical variables are described as frequencies (percentages) and
continuous variables are described as median (interquartile range) unless stated otherwise. We
described the system based, temporal and geographical characteristics of all studies included in
our systematic review. We also described similar variables for studies included in our meta-
regression and our hierarchical model. We reported the length of stay and duration of mechanical
ventilation as median and interquartile ranges (IQR). The medians reported, are based on
combining the reported means or medians (mean of means, or mean of medians) in the included
studies
6.2 Meta-Analysis
6.2.1 Random-Effects Model
A fixed effect model assumes that all included studies have a common true effect size and the
observed effects are distributed around this value with a standard deviation. A random-effects
meta-analysis model allows the true effect to vary among studies. The random effects model
thus describes the average of the effects and the degree of heterogeneity among the included
studies66
. Due to the significant heterogeneity in our included studies we chose the random
effects model to incorporate the differences among our studies. We used a random-effects model
to obtain summary outcome point estimates and 95% confidence intervals 65
. We decided not to
use a fixed-effects model for our meta- analysis as there was likely to be significant statistical
heterogeneity among our included studies. The statistical heterogeneity in our included studies
25
was in part due to the clinical differences in the population among the included studies, so we
performed further sub-group analysis to explore these concerns 64
.
6.2.2 Tests for Statistical Heterogeneity
The heterogeneity among studies should be evaluated using specific statistical tests along with
the qualitative assessment of studies 62
. We determined statistical heterogeneity among studies
by using the using the Cochran Q statistic and I2 index.
63 The Q statistic is calculated as the
weighted sum of the square of differences between individual study effects, and their pooled
effect across the different studies 62
. This measure is a chi-square statistic which is dependent on
the number of studies and the corresponding degrees of freedom 64
. The Q statistic is a part of
the DerSimonian-Laird random effects method, and is useful for evaluating the heterogeneity in
meta-analysis with a large number of studies 62
. The I2 index is used to describe the variation (in
percent) across the studies in a meta-analysis due to heterogeneity 62
. The I2 index (I
2= 100%x
(Q-df)/Q) is not dependent on the number of studies in the meta-analysis, and is a much simpler
expression of inconsistencies among included studies in a meta-analysis 64
. Thresholds for the
interpretation of I2 can be misleading, since the importance of inconsistency depends on several
factors. A rough guide to interpretation is as follows (0% to 40%: might not be important; 30%
to 60%: may represent moderate heterogeneity; 50% to 90%: may represent substantial
heterogeneity; 75% to 100%: considerable heterogeneity).
6.2.3 Ascertainment of publication bias
A funnel plot is a simple scatter plot of the intervention effect estimates from individual studies
against some measure of each study’s size or precision. Effect estimates from small studies will
therefore usually scatter more widely at the bottom of the graph, with the spread narrowing
26
among larger studies. In the absence of bias the plot should approximately resemble a
symmetrical (inverted) funnel. Presence of bias usually leads to an asymmetrical appearance of
the funnel plot with a gap in one bottom corner of the graph 67
. Visual inspection of the funnel
plot symmetry provides this information. Egger’s test is most commonly used for the testing of
funnel plot asymmetry. Newer contemporary tests such as Begg’s correlation test, Macaskill’s
method and Peters’ regression have been described but they are not superior to the Egger test.
All these tests are designed to look at differences in effects in two distinct groups 68
and not at
logit proportion for one group, as is the case in our study. Tests for asymmetry should generally
be performed only if there are ten or more studies in the meta-analysis
6.3 Subgroup analysis and Meta-Regression
Subgroup analyses and meta-regression are methods to investigate differences between studies.
Statistical significance of the results within separate subgroup analyses should not be compared
and we have to be mindful of possible bias through confounding by other study-level
characteristics when we consider sub-group analyses. For patient and intervention characteristics,
differences in subgroups that are observed within studies are more reliable than analyses of
subsets of studies 69
. Meta-regression is an extension to subgroup analyses that allows the effect
of continuous, as well as categorical, characteristics to be investigated, and in principle allows
the effects of multiple factors to be investigated simultaneously. Meta-regression should
generally not be considered when there are fewer than ten studies in a meta-analysis 69
.
In this thesis we explored clinical heterogeneity by establishing subgroups of studies according
to distinct patient populations and conducted subgroup analyses based on different variables
extracted from the studies, including specific pandemic time periods (first wave, second wave,
27
prolonged enrollment), geographical region (country, region, continent, World Bank economic
development status), study population characteristics (unselected patients, mechanically
ventilated), co-morbidities (pregnancy or post-partum), specific illnesses (ARDS, acute kidney
injury) and ICU specific interventions such as receipt of rescue oxygenation therapy (ECMO,
HFOV). Different subgroups are analyzed as follows.
6.3.1 Time as a factor in the reporting of mortality: We divided the pandemic into distinct
time-points (based on the enrollment of the patients to the individual studies) and described the
mortality associated with Wave I (April 1, 2009 to August 31 2009), Wave II (September 1 2009
to January 31 2010), and for patients enrolled from February 1, 2010. We anticipated a
significant overlap of enrollment between these distinct waves of the pandemic. Due to this we
also reported on the mortality associated with studies enrolling for between 5 to 9 months of the
pandemic and for studies enrolling for more than 9 months of the pandemic (these studies were
assessed together regardless of the time period of enrollment). As one of the main hypothesis of
our study was to investigate whether early reporting of pandemics was associated with a
difference in reported mortality we further performed a paired analysis for all the counties that
reported during Wave I of the pandemic with studies from the same countries that enrolled for
longer than 9 months. These results were presented as a risk difference, which is defined as the
difference between the observed risks in two groups under study. The risk difference describes
the estimated difference in the probability of experiencing an event.
6.3.2 Geography and economic development as factors in the reporting of the mortality:
We reported on mortality at three geographical levels: 1. World Bank region; 2. Continent; and,
3. Hemisphere. We also report the mortality using the same cohort of studies as described above
28
after using the World Bank categorization for high income, upper and lower middle income and
low income economy countries.
6.3.3 Influence of specific ICU population on the reporting of mortality: We divided the
studies into three distinct categories which we believe signified differing severity of disease in
the cohorts that were being evaluated, on the basis of mortality estimates from non-H1N1
populations: unselected critically ill patients; mechanically ventilated patients; and patients
undergoing non-conventional mechanical ventilation. We compared the mortality for the three
groups, to determine the influence of severity of illness. We also summarized the differences in
the duration of mechanical ventilation and length of stay in the ICU for each sub-group.
6.3.4 Age as a factor in the reporting of mortality: Because of the heterogeneity among the
pediatric group, we did not include pediatric studies for our comparative analyses and we report
a comparison of studies with only adults with studies that describe patients of all ages.
6.3.5 Influence of single center or multicenter studies on the reporting of mortality: We
compared reported mortality from multicenter studies compared to single center studies.
6.3.6 Influence of the number of patients in a study on the reporting of mortality: Based on
a priori discussion and review of various cohort studies reporting on the Influenza A (H1N1)
pandemic we divided the studies into 6 sub-groups. These were based on the number of patients
described in each manuscript: 10 or less; 11 to 25; 26-100; 101-250; and >250. We then
compared the difference in cumulative mortality in all these sub-groups.
6.3.7 Mortality in specific sub-groups of critically ill patients: We reported the mortality in
sub-groups of specific patients. We report mortality associated with co-morbidities or co-
presenting conditions (e.g. pregnancy). We also report on studies of that included patients based
29
upon their receipt of specific therapies such as mechanical ventilation, ECMO, HFOV; and,
common organ system failures (ARDS, acute kidney injury)
6.4 Hierarchical meta-regression
Our meta-regression compared mortality rates associated with the H1N1 pandemic in
populations with major differences in their access to health care based on the geographical region
and economic development of a country. The global prevalence of co-morbid conditions, and
underlying health characteristics have stark differences. Moreover the perceptions around
critical illness are very different among different cultures and countries and decisions regarding
admission to an ICU are dependent on a number of factors, possibly independent from associated
patient characteristics. Therefore, it is insufficient to adjust only for the background
characteristics of the patients when we compare these studies with respect to their mortality and
other outcomes. For the final regression model, we grouped similar predictor variables into
hierarchical clusters to investigate their respective and potentially clustered relationships with the
primary outcome of mortality 70
. We used a three-level hierarchical meta-regression to assess the
association between study level data pertaining to patients (age, need for mechanical ventilation,
severity of illness) and mortality by considering the variability between the system specific
characteristics (either geographical , or socioeconomic status) as well as the variability between
studies within the system 69,70
. We developed two separate clusters for the system-based
variables (socioeconomic status, and geographical region) and the heterogeneity at these two
levels was explored with random effects models. We then developed two separate hierarchical
models to study the impact of study level data pertaining to patients.
30
We developed an unconditional three level random effects model with random effects at the
level of the system based variables (socioeconomic status of a country, or geographical region of
a country) and at the level of studies within those socioeconomic status or geographical region.
We then added a fixed effect at the study level for specific patient characteristics (age, sex and
percentage of mechanical ventilation). The patient characteristics that were not significant in the
three-level hierarchical meta-regression were removed from the model and we assessed the
variance components in the presence of the significant fixed-effects. Similarly if the variance
components were not significant were removed from the model. This provided the most precise
assessment for the association of study level data pertaining to patients and mortality. We also
studied the association between study level data pertaining to patient characteristics and
mortality. We took into account the variability at the level of the cluster for system-based
variables (we studied variability based on both the socioeconomic status of a country and the
geographical region of the country). Finally, we assessed the variability in the reported mortality
between studies within a given system both with and without the addition of the cluster of patient
related variables.
31
Chapter 7: Results
7.1 Description of Included studies
Study Flow
Our search strategy yielded 5443 citations after de-duplication. We retrieved 429 articles for a
detailed evaluation and included 213 articles for our qualitative assessment. We included 87
articles for our meta-regression (Figure1).
Study Characteristics
We identified 219 studies from 213 articles (6 of the articles compared 2 different time periods
of the pandemic and were thus reported separately) from 50 countries that met our inclusion
criteria (A detailed description of the study characteristics are described in Table 1). The study
characteristics were similar when we evaluated the studies included in the meta-regression, and
the hierarchical meta-regression (Table 1). Unselected critically ill patients were described in
69% of the studies, while mechanically ventilated patients were detailed in 47 (21%) of the
studies. The included studies were distributed among different geographical regions. Forty
(18%) were from North America, 25 (11%) from Latin America and the Caribbean, 77 (35%) of
the studies originated in Europe, and 25 % were from Asia. (Table 1) Only 6 (2%) of the studies
were published from African countries. Fifty-six (26%) studies described patients with ARDS, 9
(4%) described patients with acute kidney injury, 20 (9%) of the studies described patients who
were evaluated or received ECMO, and only 8 (4%) studies described critically ill pregnant
patients. (Table 1) There was no substantial difference in the reporting of demographic, and
intervention variables among populations when we evaluated all included studies compared to
studies only included in the meta-analyses or hierarchical meta-regression model (Table 2).
32
Figure 1: Flowchart of studies included in the systematic review using PRISMA guidelines
Additional records identified
through references of included
articles and review articles
(n = 27)
Records after duplicates removed
(n = 5443)
Records screened
(n =5443)
Records excluded (n =5015)
Critically ill patients not described: 2621
No clinical outcomes of interest described: 1546
Conference Abstracts: 461
Reviews: 387
Case reports: 574
Full-text articles assessed
for eligibility
(n = 428) Full-text articles
excluded, with reasons (n =215)
Outcome of interest not reported: 58
Critically Ill patients not described: 48
Only fatal Cases reported: 21
Review Article: 19
Case Report: 14
Other Reasons: 55
Articles included in qualitative synthesis
219 studies from 213 articles
Records identified through
database searching
(Medline-3824
EMBASE-3413)
LILACS-31
African Index Medicus-33)
33
Table 1: System and study based characteristics described in 219 studies from 213 articles
compared to the studies selected for the meta-regression and hierarchical model respectively.
Study Characteristics All Studies
(n-219)
Period of Enrollment
April 2009-August 2009
September 2009-January 2010
February 2010 until end of pandemic
Studies enrolling through different waves of the pandemic
50 (23%)
31 (14%)
3 (1%)
137 (62%)
Multicenter Studies 109 (49%)
Study size (number of patients)
5-10
11-25
26-100
101-250
>250
35 (16%)
74 (34%)
67 (30%)
22 (10%)
21 (10%)
Studies with only adult patients 134 (62%)
Studies describing unselected critically ill patients 151(69%)
Studies describing specific subgroups
ARDS
Acute kidney injury
Pregnant critically ill
Mechanical ventilation
ECMO
56 (26%)
9 (4%)
8 (4%)
46 (21%)
20 (9%)
Study geographical region Americas
North America*
Latin America and Caribbean
Europe
Western Europe
Eastern Europe
Asia
Middle East
South Asia
East Asia and Pacific
Africa
North Africa
Sub-Saharan Africa
Australia/New Zealand
40 (18%)
25 (11%)
67 (31%)
10 (4%)
12 (5%)
12 (5%)
32 (15%)
3 (1%)
3 (1%)
16 (7%)
Study country economic status of the country High income economy
Upper middle income economy
Lower middle income economy
155 (71%)
50 (22%)
13 (7%)
Values are numbers (percentages) unless stated otherwise. We describe the system based, temporal
and geographical characteristics of countries included in our systematic review. We also describe
similar variables for studies included in our meta-regression and our hierarchical model. This
table shows that at each level the relative distribution of the variables remained constant
34
throughout the reported studies.*Mexico is excluded from North America and is considered to be a
part of Latin America and Caribbean in the World Bank geographical regions
Table 2: Description of patient characteristics, intensive care specific interventions and outcomes
from included studies compared to the studies selected for the meta-regression and hierarchical
model respectively.
Characteristics All studies
(n=219)
Studies for meta-
regression
(n=113)
Studies used in
hierarchical meta-
regression (n=86)
N Median
(IQR),
Proportion
N Median (IQR),
Proportion
N Median (IQR),
Proportion
Age 17
3
40 (33-44) 86 42 (37-46) 69 42 (35-45)
Females 17
0
49% 87 49% 72 49%
APACHE II 86 18 (14-20) 43 17 (14-19) 33 17 (15-19)
Lung Disease 14
0
26% 72 25% 57 23%
Obesity 98 28% 60 27% 47 24%
Pregnancy 10
1
9% 57 9% 44 8%
ICU Course
ARDS 12
8
93% 73 96% 55 96%
Acute renal failure 48 35% 25 39% 23 42%
Renal replacement
therapy
63 17% 35 16% 31 20%
Need for Inotropes 97 50% 47 51% 37 59%
Antivirals 91 100% 53 100% 43 100%
Antibiotics 48 100% 26 100% 24 100%
Corticosteroids 69 49% 33 52% 28 56%
Outcomes
Duration of
mechanical ventilation
69 10 (7-13) 36 10 (7-14) 27 10 (8-13)
ICU length of stay 95 11 (8-18) 47 11 (8-20) 40 11 (9-18)
Mortality* 219 28% 113 32% 87 33%
Categorical variables are described as numbers (percentages) and continuous variables are
described as median (interquartile range) unless stated otherwise. N Denotes the number of studies
that reported on each variable. The reporting of patient level variables remained similar at all
levels of our analysis of the reported studies. APACHE II: Acute Physiology and Chronic Health
Evaluation II; ARDS: Acute Respiratory Distress Syndrome; ICU: Intensive Care Unit.
35
7.2 Quality of Included studies
Risk of Bias and quality of evidence assessment
We did not identify any randomized controlled trials; therefore, only observational studies
(cohort, case-series) were included in our analysis. The Newcastle-Ottawa Scale scores for the
risk of bias ranged from 4 to 9 out of a maximum of 9 with a median of 7 across studies. Most of
the studies were considered to be of high quality. (Table 3) As we were not comparing two
distinct groups of patients we evaluated the risk for under- or over-reporting of mortality based
on the three domains of the scale. We defined the risk as being high for studies with a score of 6
or lower.
Table 3: The Median (range) of the Newcastle-Ottawa scale for different groups of studies
Overall
Score
Selection
of study
groups
Comparability
of groups
Ascertainmen
t of exposure/
disease
All studies 7 3 2 3
Based on period of Enrollment
Wave 1
Wave 2
Prolonged
7
8
7
3
3
3
2
2
2
3
3
3
Geographical Region
North America
Eastern Europe
Western Europe
Latin America and Caribbean
Australia/ New Zealand
East Asia
South Asia
Mid East and North Africa
Sub-Saharan Africa
8
7.5
7
7
7
7
8
8
6
3
3
2
3
3
3
3
3
2
2
2
2
2
1.5
1.5
2
2
1
3
3
3
3
3
3
3
3
3
Non-Selected Critically ill
patients
8 3 2 3
Newcastle-Ottawa Scale describing the quality of the studies based on different subgroups. We
describe the quality of the studies based on the time of enrollment, the geographical regions, and
studies just describing non-selected critically ill patients. Most studies were considered to be of high quality based on our scoring criterion (decided a priori)
36
7.3 Meta-analysis
We used a random-effects model for the meta-regression to compare the sub-groups, because of
statistically significant heterogeneity among included studies, in addition to substantial clinical
heterogeneity among the included studies. (Figure 3)There were differences in the patient
characteristics, the interventions provided and the overall severity of illness among many of the
studies. The statistical heterogeneity among the included studies was further tested using the I2
statistic. The I2 statistic revealed significant heterogeneity among the studies in all the subgroups.
We also examined the risk of publication bias on non-duplicate studies in adults with a funnel
plot and detected a relative paucity of small studies with a large difference in mortality. (Figure
2)
Figure 2: Funnel Plot
Funnel plot examining the risk of publication bias based on the logit proportion of mortality. There
is a relative paucity of small studies with a large difference in mortality. Also there are only a few
small studies with a small difference in mortality. These represent a specific group (pregnant females) with a very low mortality associated with Influenza A (H1N1) pandemic.
37
There were only a few small studies with a small difference in mortality. These represent a
specific group (pregnant females) with a very low mortality associated with Influenza A (H1N1)
pandemic. There are no studies examining the utilization of statistical tests to report publication
bias in studies describing outcomes reported in the logit form. Due to this we tested the
asymmetry of the funnel plot using 4 different tests: Egger classical; random-effects Egger’s test;
Begg’s Correlation test (Table 4). No asymmetry was detected using the random effects Eggers
and Begg’s correlation test. The Egger classic did show a statistical difference, but the regression
for this test is unweighted and is thus more unreliable.
Table 4: Tests for evaluation of asymmetry of funnel plot to study publication bias
Method Dependent
Variable
Independent
Variable
Weights p-value
Egger classical: Egger
Weighted regression with
multiplicative dispersion
Treatment/SE 1/SE No weights 0.0263
Egger: random-effects Treatment SE inverse of (variance+
between-study
variance)
0.2827
Begg’s correlation 1
(Kendall’s tau)
Standardized
treatment
Variance 0.1579
Begg’s correlation 2
(Kendall’s tau)
Standardized
treatment
Sample size 0.3266
Trim and fill method on
the random effects model
See Figure 3
SE: Standard Error
We then used a trim and fill effect on the random effects model and estimated that data from 15
studies was missing. All these studies had a large difference in mortality but they were a mix of
both small and large studies (Figure3)
38
Figure 3: Funnel Plot with Trim and fill effect revealing missing studies
The black dots represent the individual studies with a distribution of the logit of reported mortality. The empty dots represent the potentially missing studies
7.4 Meta-regression
We identified multiple subgroups as detailed above, and performed meta-regression on the
reported mortality during the 2009 Influenza A (H1N1) pandemic on all these subgroups. We
excluded duplicate studies and studies reporting exclusively on pediatric patients for these
subgroups (unless otherwise specified), and used data from 114 studies to report on the mortality
associated with specific subgroups of patients.
39
7.4.1 Reported mortality over time
14 studies reported during the first wave of the pandemic, and 20 studies reported on the second
wave. There was a significant overlap between the duration for different studies. Nineteen
manuscripts described patients over a prolonged period of time (>9 months) (Figure 3). Overall
mortality was 31% among adult patients. Mortality for the first wave of the H1N1 pandemic was
38.7% (95% CI 32.6-45.2) in wave 1, 30.1% (95% CI 22.8-38.6) in wave 2, and 30.5% (95% CI
25.2-36.3) during prolonged enrollment (p=0.66). (Figure 4) There was no difference in
mortality when early reports from specific countries were compared with studies reporting on
prolonged periods of time. (Table 5)
7.4.2 Age and mortality
There was a significant difference in the mortality based on the age of the population being
described. Mortality was significantly lower in the pediatric studies (13.6% (95% CI 9-20.2)
compared to the adult studies (29.5% (95 % CI 26.1-33.2) (P<0.0001). 31 studies described both
adults and pediatric patients with a reported mortality of 32.7% (95% CI 28.1-36.9), but in all
these studies, more than two-thirds of the patients were adults. (Figure 4)
7.4.3 Geographical Area of the study and reported mortality
We evaluated the impact of the geographical area on the reporting of mortality in different ways.
As we had a large number of countries we could not report on the individual differences amongst
the countries, so we reported the mortality based on the hemisphere, continent and the specific
region to which the country belonged. There was no difference in the mortality based on studies
from northern hemisphere (30.6% (95% CI 27.9-33.5)) compared to the southern hemisphere
(33% (95% CI 23.6-43.9)) (Figure 5). But when we studied this based on the continents and
40
Figure 4: Reported mortality associated with 2009 Influenza A (H1N1) associated critical illness.
We describe the mortality based on temporal (early, late and prolonged enrollment), study (study
size, single center compared to multicenter and adults compared to pediatrics), and the geographic
location and socioeconomic development from the included studies. The black squares represent
the point estimate and 95% confidence intervals (CIs) around the mortality for each subgroup.
The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic.
41
Table 5: Meta-analysis comparing the reported mortality from “early enrollment” (the Wave 1 for
each individual country) during the H1N1 pandemic with studies describing prolonged enrollment
from the same countries. We evaluated the differences in the reporting of mortality among the
individual countries by using both a fixed-effect and a random-effect model
Country Relative Risk (95% CI) Risk Difference (95% CI)
Australia/ New
Zealand
1.09 (0.87-1.36) 0.01 (-0.01-0.04)
Canada 1.27 (0.89-1.82) 0.04 (-0.01- 0.11)
China 1.3 (1.01-1.68) 0.06 (-0.009-0.11)
France 2.66 (0.89-7.9) 0.12 (0.03-0.21)
Italy 1.003 (0.28-3.53) 0.0006 (-0.25-0.25)
Spain 0.84 (0.45-1.55) -0.04 (-0.19-0.11)
USA 0.77 (0.51-1.16) -0.06 (-0.17-0.04)
Fixed Effect
Model
1.13 (0.98-1.29) p-value 0.07 0.03 (-0.007-0.05) p-value 0.009
Random Effect
Model
1.12 (0.93-1.34) p-value 0.21 0.03 (-0.004-0.07) p-value 0.08
Quantification of
Heterogeneity
Test of
Heterogeneity
I2=28.1% (0%-69%)
Q=8.34 d f=6 p-value= 0.21
I2=45.6% (0%-77%)
Q=11 d f=6 p-value= 0.08
The table shows that at an individual country level, the relative risk of death was not statistically
significantly different during the duration of the pandemic. The reporting from early case-series
gave an approximate estimate of the overall mortality in any given country though the entirety of a
pandemic. However, we also found that there were significant intra-country differences in the
reported mortality among different countries, and these differences also tended to remain constant
when they are studied through the entirety of the pandemic.
geographical regions we found significant differences in the reported mortality among different
continents and geographical regions respectively (Figure 4). Of interest the mortality reported
from Australia (15.1% (95% CI 12.6-17.9) was significantly lower than all other continents.
Studies from Africa reported the highest mortality (41.8% (95% CI 22.9-63.5)), but it was
comparable to studies from Asia (36.9% (95% CI 30.6-43.6)) and South America (36.4% (95%
CI 28.9-44.7)). North America (27.4% (95% CI 23.6-31.6)), and Europe (27.2% (95% CI 23.4-
31.4)) had comparable reported mortality. When we compared the reported mortality based on
42
the geographical region the reported mortality was the highest in the Sub-Saharan African
(52.7% (95% CI 29.2-75.2)) countries and South Asian (60.9% (95% CI 49.6-71.2)) countries.
Mortality was comparable in North America (24.5% (95% CI 21.9-27.2)); West Europe (25.4%
(95% CI 21.5-29.8)) and East Asia and Pacific 27.6% (95% CI 22.9-32.9)). Reported mortality
in Middle Eastern and North African countries (33.8% (95% CI 27.7-40.4)), Eastern European
(35.3% (95% CI 25.5-46.6), and Latin American Countries (38.6% (95% CI 32.2-45.4)) all
showed a more pronounced effect when the geographical region rather than the hemisphere or
the continent was considered.
7.4.4 Economic status of the country and reported mortality
High income economies had significantly lower reported mortality (26% (95% CI 23.5-28.6)
compared to upper middle income Economies (36.7 (95 % CI 31.3-42.4)) and lower middle
income economies (57.6% (95% CI 45.8-68.5)) respectively (P<0.0001). (Figure 3) There were
clinically relevant differences in the duration of mechanical ventilation among studies from high
income economies (11[8-16] days) compared to upper (9 [8-10] days) and lower (8 [6-10] days)
middle income economies (Table 6).
43
Figure 5: Differences in reported mortality based on different geographic variables for the included countries (hemisphere, continent and World Bank designated geographical region).
The Black squares represent the point estimate and 95% confidence intervals (CIs) around the
mortality for each subgroup. The black diamond is the summary or overall combined estimate of
mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical regions
is associated with the best discriminative power to report the differences in mortality in a global context.
44
Table 6: Differences in Mortality, Length of Stay in the ICU and duration of Mechanical
ventilation based on the World Bank economic development classification.
World Bank
Economic
development status
High income
economy
Upper middle
income
economy
Lower middle
income
economy
Short-term mortality N N N
155 24% 49 35% 13 52%
Duration of
Mechanical
Ventilation, days
49
11 (8-16)
13 9 (8-10) 6 8 (6-10)
Length of Stay in ICU,
days
76
11 (8-20)
14
10 (7-12) 4 10 (7-11)
Variables are described as median (interquartile range) unless stated otherwise. N Denotes the
number of studies that reported the specific outcomes. ICU: Intensive Care Unit
7.4.5 Reported mortality in specific ICU populations
Unselected Critically ill patients were described in 71 (63%) of the studies included in our meta-
regression, while 36(32%) studies described cohorts with ARDS. We divided the studies based
on the severity of illness of patients into multiple sub-groups. Mortality was substantially higher
among patients undergoing mechanical ventilation (42.1% [95 % CI 35.8-48.7]) in comparison
to unselected critically ill patients, (27.1% [95 % CI 24.4-29.9]) (Figure 5). Mortality in patients
with ARDS was 37.4% (95% CI 31.6-43.7) and 43.9% (95% CI 26.1-63.5) among critically ill
patients with acute kidney injury, and 9.6% (95% CI 4.5-19.2) among critically ill in pregnant
patients.
45
Figure 6: Differences in reported mortality based on subgroups of patients with different severity
of illness (need for mechanical ventilation), critical illness associated organ failure (ARDS; AKI) or co-presenting conditions (pregnancy).
The black squares represent the point estimate and 95% confidence intervals (CIs) around the
mortality for each subgroup. The black diamond is the summary or overall combined estimate of mortality associated with the 2009 Influenza A (H1N1) pandemic
During the H1N1 pandemic the use of non-conventional therapies for ARDS were extensively
reported, so we described the studies in detail at three different levels. Studies with unselected
critically ill patients were compared to studies reporting on only mechanically ventilated patients
and patients undergoing extracorporeal membrane oxygenation. (Table 7)
46
Table 7: Differences in baseline characteristics based on the studies only describing unselected
critically ill patients, studies describing patients undergoing mechanical ventilation, and studies
describing patients under consideration or actually getting ECMO
The patient characteristics, co-morbidities and ICU specific interventions were similar in these
sub-groups. Patients who underwent ECMO had longer duration of mechanical ventilation and
length of stay in the ICU when compared to mechanically ventilated patients. Patients
undergoing ECMO had a lower mortality that patients undergoing mechanical ventilation.
7.5 Hierarchical Meta-Regression
The reported mortality for most subgroups described above was similar when we restricted our
analysis to only the studies included in our hierarchical model (Appendix). However, reported
Characteristics Unselected
Critically ill
(n=151)
Mechanical
Ventilation
(n=46)
Extracorporeal
membrane
Oxygenation
(n=20)
n n n
Age 119 41 (30-45) 39 41 (35-46) 14 36 (32-40)
Females 115 47% 38 50% 16 51%
APACHE II 59 18 (14-21) 20 18 (16-21) 6 18 (17-19)
Lung Disease 104 28% 25 23% 10 14%
Obesity 67 26% 23 27% 7 40%
Pregnancy 70 9% 19 9% 12 23%
ARDS 75 73% 37 100% 14 100%
Acute Renal Failure 37 32% 9 50% 2 49%
Renal Replacement Therapy 44 15% 13 17% 6 25%
Need for Inotropes 64 44% 24 55% 8 65%
Antivirals 59 99% 26 100% 5 100%
Antibiotics 35 98% 12 100% 1 100%
Corticosteroids 44 48% 17 50% 8 42%
Duration of Mechanical ventilation
44 9 (7-11) 18 12(9-19) 8 22 (11-27)
ICU length of stay 68 9 (7-12) 20 12(10-20) 7 22 (18-33)
Short term Mortality* 151 25% 47 36% 20 31%
47
mortality showed a difference based on the number of patients included in the study (24.5 %
[95% CI (20.4%-29%)] in studies with more than 250 patients, and 42% [95% CI (32%-52.7%)]
in studies with ≤ 10 patients); (Appendix).
The three-level unconditional meta-analysis model was first fit by taking into account the
variability between the economic status and between studies within the economic status. The
model had three levels (study, economic development and patient specific variables), but only 2
variance components to estimate though: one at the level of the study and one at the level of
economic development. We then introduced the study level data pertaining to patient variables as
a fixed effect. Only mechanical ventilation retained any statistical significance when we
evaluated the study level data pertaining to patients. So we only used the need for mechanical
ventilation as a fixed effect.
When we studied the three level unconditional (no predictors at any level, which helps partition
the outcome variation) random effects model with economic stauts of a country and studies
within the economic status, the variance at the level of economic status was 0.37 and at the level
of the study was 0.22. When we added mechanical ventilation as the fixed effect to the model the
variance at the level of the economic status dropped to 0.28 and at the level of the study became
0.17. Therefore need for mechanical ventilation explains 24% (1-0.28/0.37*100) of the
variability in reported mortality among the included studies. The variance at the level of the
economic status explains 29% (1-0.22/0.17*100) of the variability in reported mortality at the
study level within the economic status of countries. (Table 8)
48
Table 8: Hierarchical model with 3 levels (specific patient variables [need for mechanical
ventilation are treated as fixed effects] and study and economic development of the country are
treated as random effects with studies clustered within the economic development.
Model 1
Unconditional random effects model with clustering at two levels (study and economic
development) Variance Components Estimate Standard Error p-value
Second Level:
Variance among studies within economic
development
0.22 0.06 <0.0001
Third level:
Variance between economic development
0.36 0.39 0.17
Model 2
Addition of patient specific variable (mechanical ventilation) as a fixed effect Variance Components Estimate Standard Error p-value
Second level:
Variance among studies within economic
development
0.27 0.30 0.18
Third level:
Variance between economic development
0.17 0.05 0.0009
Effect of patient specific variables on reported mortality in a hierarchical model with
clustering at three levels
Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value
<70%
70-89%
90-99%
100%
0.55
0.79
0.76
1
(0.35 to 0.86)
(0.49 to 1.28)
(0.42 to 1.38)
0.01
0.27
0.30
*Odds ratio in comparison to 100% of mechanically ventilated patients. A three level multi-
regression model was developed accounting for the variability of study, and the geographic region
of the country on the reported mortality during the H1N1 pandemic. When we added the need for
mechanical ventilation in critically ill patients to this model it was significantly associated with
mortality.
But a part of this variability at the second and third level of our model is explained by the
significant heterogeneity existing in the fixed effects variable (mechanical ventilation) in this
three level model. Due to this significant variability, the level of the economic status of a country
is non- significant. Due to the fact that the variability at the third level is not significant we
decided to reduce our economic status model to a 2-level hierarchical model for our final
analysis. This two-level hierarchical model showed a significant difference in the mortality based
on the addition of the fixed effect variable (mechanical ventilation) (Table 9)
49
Table 9: Hierarchical model with two levels. The specific patient variables [need for mechanical
ventilation] is treated as a fixed effect against studies clustered within the economic development of
a country
Unconditional model with clustering at two levels (study and patient)
Variance Components Estimate Standard Error p-value
Variance among studies within economic
development
0.29 0.07 <0.0001
Effect of patient specific variables as affixed effect
Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value
<70%
70-89%
90-99%
100%
0.46
0.66
0.97
1*
0.30 to 0.69
0.66 to 1
0.56 to 1.69
0.0003
0.05
0.93
*Odds ratio in comparison to 100% of mechanically ventilated patients. A two level multi-
regression model was developed accounting for the variability at the level of study on the reported
mortality during the H1N1 pandemic. The need for mechanical ventilation in critically ill patients
was significantly associated with mortality.
We also developed a three-level unconditional meta-analysis model by taking into account the
variability between the geographic region and between studies within the geographic region. The
model also had three levels (study, geographic region and patient specific variables), but only 2
variance components to estimate though: one at the level of the study and one at the level of
geographic region. We then introduced the study level data pertaining to patient variables as a
fixed effect. Only mechanical ventilation retained any statistical significance when we evaluated
the study level data pertaining to patients. So we only used the need for mechanical ventilation as
a fixed effect.
When we studied the three level unconditional (no predictors at any level, which helps partition
the outcome variation) random effects model using geographic region of a country and studies
within a geographic region the variance at the level of the geographic region was 0.23 and at the
level of the study was 0.17. By adding mechanical ventilation as a fixed effect to the model the
variance at the level of the geographical region changed to 0.14 and at the level of the study
changed to 0.16 signifying that need for mechanical ventilation explains 64% (1-0.23/0.14*100)
50
of the variability in the reported mortality at the level of the geographic region of a country and
only 6% (1-0.17/0.16*100) of the variability at the study level within the geographic regions
(Table 10)
When we evaluated the reported mortality in each three-level hierarchical model, both the study
and system based variables were associated with some degree of variability in reported mortality,
but the study level data pertaining to patients was strongly associated with the reported mortality.
Table 10: Hierarchical model with 3 levels specific patient variables [need for mechanical
ventilation] is treated as fixed effects and study and economic development of the country are treated as random effects with studies clustered within the economic development.
Model 1
Unconditional random effects model with clustering at two levels (study and
geographic region)
Variance Components Estimate Standard Error p-value
Second Level:
Variance among studies within Geographic
region
0.23 0.15 0.07
Third level:
Variance between geographic region
0.17 0.06 0.001
Model 2
Addition of patient specific variable (mechanical ventilation) as a fixed effect
Variance Components Estimate Standard Error p-value
Second level:
Variance among studies within economic
development
0.14 0.10 0.09
Third level:
Variance between economic development
0.16 0.06 0.002
Effect of patient specific variables on reported mortality in a hierarchical model
with clustering at three levels
Mechanical Ventilation as fixed effect Odds ratio 95% CI p-value
<70%
70-89%
90-99%
100%
0.58
0.73
0.79
1*
(0.38 to 0.88)
(0.49 to 1.08)
(0.48 to 1.30)
0.01
0.11
0.33
*Odds ratio in comparison to 100% of mechanically ventilated patients. A three level multi-
regression model was developed accounting for the variability at study and geographic region of the
country on the reported mortality during the H1N1 pandemic. The need for mechanical ventilation
in critically ill patients was significantly associated with mortality
51
Chapter 8: Discussion
In this systematic review and meta-regression of 219 studies investigating pandemic influenza A
(H1N1) related critical illness from 50 countries, we found that overall mortality for critically ill
adults was 31%. Our study highlights significant heterogeneity in the reported mortality among
published literature during the Influenza A (H1N1) pandemic. Our systematic review revealed
that early in the course of the pandemic there was a tendency to report on selected populations
(i.e. patients requiring mechanical ventilation, severe ARDS etc.), which in turn inflated the early
mortality estimates associated with the Influenza A (H1N1) pandemic. Differences in reported
mortality were only partly explained by the greater severity of illness of the population under
study, and our meta-regression further revealed a significant heterogeneity in the reported
mortality according to the global region and the country’s economic development status. When
these variables were considered in a hierarchical model, study-based variables (size of the
population, single center studies), and system-based variables (geographical region, economic
development) were not significantly associated with mortality. In our hierarchical model the
reported mortality, instead, was heavily significantly influenced by study based variables
pertaining to patient characteristics, most specifically the initial need for mechanical ventilation
in the patient population described.
These findings are important because they emphasize that while patient-based factors are most
influential in determining outcome, the region and system of care delivery represents a
potentially modifiable factor that can lead to improved survival for recoverable infections. These
findings also emphasize the limitations of generalizing early reported outcomes from a limited
region and among a relatively small number of patients and have relevance for contemporary
52
outbreaks of seasonal and avian influenza, Middle East Respiratory Syndrome Coronavirus and
Ebola.
We had hypothesized that reporting outcomes from very early phases in a pandemic, when either
case definitions are imprecise or treatment protocols or capacity are sub-optimal, may similarly
influence reported morbidity and mortality and such estimates might not be generalizable to later
stages in a pandemic.14,33,36,71
Our meta-regression showed that the reported mortality was non-
significantly higher early in the outbreak (38.8% during the earliest pandemic wave and 29.8% in
subsequent waves). However, reported mortality early in the pandemic was heavily influenced
by a tendency to report on selected populations (e.g. patients requiring mechanical ventilation,
those with severe ARDS). Early reports focusing on these highly selected populations either
under-reported mortality (mortality of 13.2% among pediatric studies) or over-report mortality
(e.g. those with severe ARDS, requiring mechanical ventilation, with acute kidney injury, etc.),
when compared to the mortality associated with patients afflicted across the entire pandemic.
Early reports during outbreaks and pandemics should ideally describe consecutively enrolled,
objectively defined but minimally selected patients to best inform appropriate clinical and policy
decisions. Reporting on selected populations is important to identify risk factors for differential
outcomes; however, such selected populations should also be clearly defined. This ensures
accurate assessment of disease severity at a global scale and allows for early recognition in
differences in outcomes over different time periods and geographical regions. Ideally this would
be accomplished using prospectively developed, flexible and tiered case report forms that are
appropriate for a variety of resource settings 72
.
Reporting on differences in regional outcomes associated with critical illness in a global context
is challenging. The lack of standardized definitions, and differences in severity of disease that
53
are vaguely classified as critical illness have been cited as potential barriers.37,41,73
When we
compared differences in reported mortality based on early reporting compared to prolonged
periods of enrollment for individual countries with available data, there was no intra-country
difference in the reported mortality over time, but there were inter-country differences in
reported mortality persisted over time in most of the countries (Table 5). 57,74-76
Our also study
highlights that the use of geographic variables such as hemispheres or continents is likely less
sensitive to differences in outcomes. This may be because differential resources and patient
characteristics can exist within broadly defined geographical units. The use of either economic
development or geographic regions as defined by World Bank was more sensitive in
demonstrating the impact on reported mortality. Recent studies have attempted to describe the
burden of critical care and associated utilization of critical care at a global level.44
The economic
development of the country might be a surrogate marker for the availability of ICU beds or
specific therapies, and the region might give us more information about the similarities or
dissimilarities at a system-based and patient-based level in different areas of the world. This was
further reaffirmed by our hierarchical meta-regression models, which showed that a patient based
variables such as the use of mechanical ventilation was significantly associated with mortality
even when we account for the study, geographical or economic variables.
Our study points out that the present mortality reporting for new outbreaks and pandemics are
likely heavily influenced by regional and economic variables. The period of enrollment of
studies, and the severity of illness are other important factors. These findings highlight the need
for standardized reporting of critical illness during outbreaks at a global level. As a number of
viral outbreaks are associated with significant respiratory or circulatory failure, initial reports
need to make a distinction between reporting of mortality in cohorts of unselected critically ill
54
patients, and patients with respiratory failure; for instance, those requiring mechanical ventilation
and patients requiring rescue therapies. A difference in mortality persisting among countries with
similar resources might be a manifestation of the reporting practices in those specific countries.
A number of studies reporting during the H1N1 pandemic used ICU admission and death as a
combined outcome.77-79
Critical illness is associated with a 30-40% mortality in many case series
and cohort studies. The use of a composite endpoint of mortality and intensive care unit
admission is both uneven (mortality and critical illness do not carry the same clinical weighting)
and misleading as the main reason for an ICU admission is to minimize the likelihood of death
associated with a disease. Future reporting of outcomes associated with critical illness during
outbreaks need to consider critical illness as a separate variable from death.
Strengths of this systematic review include a comprehensive search strategy, with duplicate
screening and data abstraction that provides the most complete review of pandemic H1N1
outcomes. We used validated strategies to minimize bias in the selection of studies and reporting
of outcomes with clinical judgment to decide a priori to combine studies reporting on different
time periods of the pandemic, specific sub-groups and clinically important interventions. We
further strengthened our results by utilizing multiple meta-regressions to get the most accurate
estimate of mortality associated with the H1N1 pandemic. We used random effects models to
aggregate data and generate conservative confidence limits for the point estimate of the pooled
treatment effect.
However, the quality of our meta-analysis is limited by the quality of included studies, most of
which were observational cohorts without a comparison group. In these observational studies, the
effect of unidentified confounding factors or residual confounding for known factors cannot be
55
ruled out. We used the Newcastle-Ottawa scale to ensure we quantified the risk for bias. The
majority of our outcomes still focused on the developed regions with capacity to carry out
observational and intervention research, and also, the capacity to provide tertiary and quaternary
care for these patients. We were able to include some developing countries, but the data from
least developed countries is unavailable and therefore missing. The differences in reported
mortality based on the economic development of countries highlights that mortality at a global
scale may have been higher than had been previously reported. This observation is similar to
other recently published studies22,80
. Despite an exhaustive review of the literature, we did not
collect patient level data, and in the end the estimates on reporting of mortality were based on
only study level variables.
56
Chapter 9: Conclusions and suggestions for future research
In this systematic review of the published literature examining global patient characteristics and
outcomes for H1N1-related critical illness during the 2009-2011 pandemic, we provide the most
accurate and valid estimates of outcomes, and explore how these outcomes differ according to
population, patient and study characteristics. Outcomes associated with new outbreaks may
appear very different (usually worse) through reporting on a small, selected group of very ill
patients early in the course of an outbreak. Therefore, such reports should consider limiting their
reporting to the features associated with the new disease and highlight the serious limitations in
predicting true outcome rates. Our analysis also reveals that at a system-based level, the
economic development of a country, and the use of geographical regions gives more valid
estimation of effect as compared to the traditional use of continents or hemispheres on the
reported mortality during disease outbreaks. Outcomes from a relatively small number of
patients, early in an outbreak and from specific regions may lead to biased estimates of outcomes
on a global scale. Differences in mortality in a geographic context are not temporal but reported
mortality can be different through the phases of a pandemic in a given country. Our results
highlight that a standardized global approach to reporting on outbreaks and pandemics may give
us more accurate estimates of morbidity and mortality associated with new diseases. Reported
mortality for new outbreaks may be higher or lower depending upon selected patient
characteristics, the number of patients described, and the region and economic status of the
outbreak location. These findings have relevance for new and ongoing outbreaks. Outbreaks
should use case report forms that are prospectively developed, flexible in components, scalable
to a variety of resource settings, encompass some measure of severity of illness to allow for risk
adjustment across regions, and globally available.72
A standardized global approach to reporting
57
on outbreaks and pandemics will provide us more accurate estimates of morbidity and mortality
associated with new diseases and provide the most valid information upon which to base current
and future research, clinical care, and health systems responses.
58
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Appendix 1: Search strategy and MeSH terms used
MEDLINE search:
Influenza A(H1N1) Virus Search terms:
1. exp Pandemics
2. exp Influenza, Human
3. exp Disease Outbreaks
4. exp Influenza A Virus, H1N1 Subtype
5. exp Influenza A Virus
Critical Illness search terms:
1. exp Critical Care
2. exp Intensive Care Units
3. exp Critical Illness
4. exp Intensive Care
5. exp Mechanical Ventilation
6. exp Artificial ventilation
7. exp Vasopressors
8. exp Inotropes
EMBASE search:
Influenza A(H1N1) Virus Search terms:
1. exp Influenza virus A H1N1/
2. exp Pandemic influenza/
Critical Illness Search terms:
1. exp Intensive care/ or exp intensive care unit/
2. exp Critical illness/
3. exp Critically ill patient/
4. exp Mechanical Ventilation
5. exp Artificial ventilation
6. exp Vasopressors
7. exp Inotropes
LILACS and African Index Medicus search
1. exp Influenza virus A H1N1/
2. exp Pandemic influenza/
79
Appendix 2:
Studies $: 19,21,38-40,75,81-288
289
Studies included in
qualitative synthesis
(n = 213)$
Studies included in hierarchical meta-regression model
(n =60) #
Comparison of:
-Number of patients enrolled
(n=114)*
-Adults vs Pediatrics vs both
(n=131)@
-Single vs Multicenter
(n=114)*
Studies evaluating specific ICU population
(n=114)*
Studies evaluating geography/ economic development
(n=114)*
Studies evaluating time of enrollment
(n=107)+
80
Studies *: 21,38,40,75,81-85,88,90,92,93,97,99,100,102,105-109,115,120,122,124,127,129,131,133-135,137,144,147-151,153,156-
158,160,161,165-167,169,170,172-174,177,183,185-187,189,190,192-194,196,199,203-205,207,208,215-217,219,221-223,227-
230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,271,272,274,276,277,280,282,288
Studies @
:21,38,40,75,81-85,87-90,93,97,99-102,105-109,115,120,122,124,127,129-131,133-135,137,144,147-151,153,156-
158,160,161,165-167,169,170,172-174,177,183,185-187,189,190,192-194,196,198,203-205,207,208,215-217,220-223,227-
230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,270,272,274,276,277,280,282,288
110,123,140,143,171,175,178,180,191,200,213,232,263,266,273,286,287
Studies+: 21,38,40,75,81-85,88,90,93,100,102,105-109,115,122,124,127,129-131,133-135,137,144,148-151,153,156-
158,160,161,165,167,169,170,172-174,183,185-187,189,190,192-194,196,199,201,203-205,207,208,215-217,219,221-223,227-
230,234,235,239,241,244,246,248,249,251-253,255-258,260,261,264,267,268,270,274,276,277,280,282,285,288
Studies#: 21,38,40,81,84,88,93,99,106,111,114,121,135,142,147-149,153,156,158,165-167,174,185,186,190,194,203-
205,208,217,219,221,227-229,233,234,238,239,244,251-253,256,258,260,261,264,267,268,274,277,280,281,283
81
Appendix 3: Reported mortality associated with 2009 Influenza A (H1N1) associated critical
illness for the studies used in the hierarchical meta-regression models
We describe the mortality based on temporal (early, late and prolonged enrollment), study (study
size, single center compared to multicenter and adults compared to pediatrics), and the
geographic location and economic development from the included studies. The black squares
represent the point estimate and 95% confidence intervals (CIs) around the mortality for each
subgroup. The black diamond is the summary or overall combined estimate of mortality
associated with the 2009 Influenza A (H1N1) pandemic
82
Appendix 4: Differences in reported mortality based on different geographic variables for the
included countries (hemisphere, continent and World Bank designated geographical region) for
the studies used in the hierarchical meta-regression models
The black squares represent the point estimate and 95% confidence intervals (CIs) around the
mortality for each subgroup. The black diamond is the summary or overall combined estimate of
mortality associated with the 2009 Influenza A (H1N1) pandemic. The use of geographical
regions is associated with the best discriminative power to report the differences in mortality in a
global context.
83
Appendix 5: Differences in reported mortality based on subgroups of patients with different
severity of illness (need for mechanical ventilation), critical illness associated organ failure
(ARDS; AKI) or co-presenting conditions (pregnancy) for the studies used in the hierarchical
meta-regression models
The black squares represent the point estimate and 95% confidence intervals (CIs) around the
mortality for each subgroup. The black diamond is the summary or overall combined estimate of
mortality associated with the 2009 Influenza A (H1N1) pandemic
84
Appendix 6: Table A1: System and study based characteristics described in 221 studies from 218
articles compared to the studies selected for the meta-regression and hierarchical model
respectively.
Study Characteristics All Studies
(n-219)
Studies for
Meta-
regression
(n-113)
Studies for
hierarchical model
(n-86)
Period of Enrollment
April 2009- August 2009
September 2009-January 2010
February 2010 till end of pandemic
Studies enrolling through different waves of
the Pandemic
50 (23%)
31 (14%)
3 (1%)
137 (62%)
21 (18%)
26 (23%)
1 (1%)
66 (58%)
12 (14%)
13(14%)
1 (1%)
62 (71%)
Multicenter Studies 109 (49%) 46 (40%) 42 (48%)
Study size (number of patients)
5-10
11-25
26-100
101-250
>250
35 (16%)
74 (34%)
67 (30%)
22 (10%)
21 (10%)
23 (20%)
44 (40%)
30 (26%)
6 (5%)
10 (9%)
13 (15%)
36 (42%)
20 (23%)
6 (7%)
11 (13%)
Studies with only adult patients 134 (62%) 79 (72%) 56 (66%)
Studies describing unselected critically ill
patients
151(69%) 71 (62%) 55 (63%)
Studies describing specific subgroups
ARDS
Acute Kidney Injury
Pregnant critically ill
Mechanical Ventilation
ECMO
56 (26%)
9 (4%)
8 (4%)
46 (21%)
20 (9%)
36 (32%)
4 (4%)
3 (3%)
39 (35%)
8 (7%)
27 (32%)
5 (6%)
1 (1%)
30 (36%)
5 (6%)
Study geographical region
Americas
North America*
Latin America and Caribbean#
Europe
Western Europe
Eastern Europe
Asia
Middle East
South Asia
East Asia and Pacific
Africa
North Africa
Sub-Saharan Africa
Australia/New Zealand
40 (18%)
25 (11%)
67 (31%)
10 (4%)
12 (5%)
12 (5%)
32 (15%)
3 (1%)
3 (1%)
16 (7%)
12 (11%)
14 (13%)
39 (34%)
9 (8%)
6 (5%)
8 (7%)
17 (15%)
3 (3%)
3 (3%)
2 (2%)
4 (5%)
15 (18%)
24 (28%)
9 (10%)
7 (8%)
7 (8%)
12 (14%)
3 (4%)
3 (4%)
2(2%)
Study country economic status of the country
High Income Economy
Upper Middle Income Economy
Lower Middle Income Economy
155 (71%)
49 (22%)
13 (7%)
73 (64%)
32 (28%)
9 (8%)
50 (57%)
28 (32%)
8 (9%)
85
Values are numbers (percentages) unless stated otherwise. We describe the system based, temporal
and geographical characteristics of countries included in our systematic review. We also describe
similar variables for studies included in our meta-regression and our hierarchical model. This
table shows that at each level the relative distribution of the variables remained constant
throughout the reported studies.
Appendix 7:
Definitions for the Thesis
1. Severity of Illness scores: These are scoring systems used in critically ill patients to
assess the severity of disease and provide an estimate of in-hospital mortality. The
estimate is based on collection of specific clinical and/or physiologic variables with
different weighting. These severity scores are then used to calculate the probability
of mortality among patients. Ideal scoring systems should be easy to collect, be well-
calibrated, have a high level of discrimination and should be generalizable across
various patient populations 290
For the purpose of our study we have collected data on
the following severity of illness scores.
a. APACHEII/III/IV: The Acute Physiologic and Chronic Health Evaluation
(APACHE) scoring system is a severity score used to predict hospital mortality.
Age, diagnosis at the time of admission, and numerous acute physiologic and
chronic health variables are a part of the APACHE Score 290,291
.
b. SAPSII/III: Simplified Acute Physiology score (SAPS) is a severity of disease
classification system that describes the morbidity in patients based on 12 routine
physiologic measurements 292
.
c. SOFA: The Sequential Organ Failure Assessment (SOFA) uses simple
measurements of six major organ functions to calculate a severity score. Serial
measurements of this score are predictive of mortality in critically ill patients 293
.
d. PRISM III: The PRISM III is a scoring system used to predict critical care
outcomes for pediatric patients. It describes severity of illness or injury in this
population 294
.
2. Co-Morbidities
Presence of one or more medical conditions that existed in addition to the most significant
condition (usually recorded as the "most responsible diagnosis" on hospital discharge abstracts)
that caused a patient's stay in the hospital. The number of comorbid conditions is used to provide
an indication of the health status (and is also used to help estimate the risk of death) of patients.
86
a. Heart Disease: Described as the presence of documented any coronary artery
disease, congestive heart failure, congenital heart disease, valvular abnormalities
and chronic arrhythmias
b. Lung Disease: Described as the presence of any asthma, interstitial lung disease,
chronic obstructive pulmonary disease (COPD) including chronic bronchitis and
emphysema; bronchiectasis, cystic fibrosis, pneumoconiosis and
bronchopulmonary dysplasia (BPD)
c. Immunosuppression: Immunodeficiency related to use of immunosuppressive
drugs (e.g. chemotherapy) or systemic steroids, Human Immunodeficiency Virus
infection and Acquired Immune Deficiency Syndrome and autoimmune diseases
resulting in systemic immunodeficiency.
d. Malignancy: Defined as the presence of any metastatic solid or hematological
malignancy.
e. Obesity: We used the WHO definition of obesity for our study, defined as a Body
Mass Index (BMI) of > 30 kg/m2. BMI is calculated as body weight in kilograms
divided by the square of the height in meters (kg/m2).
f. Pregnancy: We defined pregnancy as a state for any female who was either
pregnant or post-partum (within 6 weeks of delivery) at the time of H1N1
infection.
3. World Bank Classification for geographical regions of the world:
North America (Canada and United States of America); Europe and Central Asia (Albania,
Hungary, Romania, Armenia, Kazakhstan, Serbia, Azerbaijan, Kosovo, Tajikistan, Belarus,
Kyrgyz Republic, Turkey, Bosnia and Herzegovina, Macedonia, FYR, Turkmenistan, Bulgaria,
Moldova, Ukraine, Georgia, Montenegro, Uzbekistan); East Asia and Pacific (American Samoa,
Malaysia, Samoa, Cambodia, Marshall Islands, Solomon Islands, China, Micronesia, Fed. Sts,
Thailand, Fiji, Mongolia, Timor-Leste, Indonesia, Myanmar, Tuvalu, Kiribati, Palau, Tonga,
Dem. Rep. Korea, Papua New Guinea, Vanuatu, Lao PDR, Philippines, Vietnam); South Asia
(Afghanistan, India, Pakistan, Bangladesh, Maldives, Sri Lanka, Bhutan, Nepal); Middle East
and North Africa(Algeria, Jordan, Tunisia, Djibouti, Lebanon, West Bank and Gaza, Egypt,
Libya, Yemen, Iran, Morocco, Iraq, Syrian Arab Republic); Sub-Saharan Africa (Angola,
Gambia, Rwanda, Benin, Ghana, São Tomé and Principe, Botswana, Guinea, Senegal, Burkina
Faso, Guinea-Bissau, Seychelles, Burundi, Kenya, Sierra Leone, Cameroon, Lesotho, Somalia,
Cabo Verde, Liberia, South Africa, Central African Republic, Madagascar, South Sudan, Chad,
Malawi, Sudan, Comoros, Mali, Swaziland, Dem. Rep Congo, Mauritania, Tanzania, Congo,
Mauritius, Togo, Côte d'Ivoire, Mozambique, Uganda, Eritrea, Namibia, Zambia, Ethiopia,
Niger, Zimbabwe, Gabon, Nigeria); Latin America and the Caribbean (Argentina, Ecuador,
Nicaragua, Belize, El Salvador, Panama, Bolivia, Grenada, Paraguay, Brazil, Guatemala, Peru,
Colombia, Guyana, St. Lucia, Costa Rica, Haiti, St. Vincent and the Grenadines, Cuba,
Honduras, Suriname, Dominica, Jamaica, Venezuela, RB, Dominican Republic, Mexico) and
Australia and New Zealand
87
Appendix 8: List of Excluded studies
No. Study Identifier Country of
Study
Reason for Exclusion
1. Azziz- Baumgartner,
PLoS One, 2012
Argentina Discusses the burden of disease
and resource utilization associated
with H1N1 and does not focus
upon patient level variables
2. Palacios, PlosOne,
2009
Argentina Severity of illness did not meet
our inclusion criteria
3. Trimarchi, NDT plus
2009
Argentina Detailed findings of the same
population described in another
manuscript
4. Kusznierz, Influenz
and other respir
viruses, 2013
Argentina Mortality in critically ill patients
not described
5. Forrest, Intensive
Care Medicine, 2011
Aus/NZ Discusses only transportation of
patients requiring ECMO
6. Fitzgerald, Crit Care
and Resuscitation,
2012
Aus/NZ Letter to the editor; discusses the
difficulties with continuous veno-
venous hemodialysis in patients
undergoing HFOV
7. Hayashi, Internal
Medicine Journal,
2011
Aus/NZ No clear distinction of critically ill
patients from other patients
8. Ng, American Journal
of Transplantation,
2011
Aus/NZ Fewer than 5 critically ill patients
9. Bellomo,
Contributions to
Nephrology, 2010
Australia Outcome variables of interest not
described
10. Mulrennan, PLoS
One, 2010
Aus/NZ Outcome variables of interest not
described
11. Hodgson, Crit Care,
2012
Australia Described only long term quality
of life in ECMO patients, not
outcomes of interest
12. Higgins, Anaesth
Intensive Care, 2011
Aus/ NZ Discusses the economic impact of
H1N1 Pandemic
13. Hewagama, Clin
Infect Disease, 2010
Aus/ NZ No data describing critically ill
patients provided
14. Burns, Prehospital
Emergency Care,
2011
Australia Discusses logistics of ECMO
retrieval
15. Pirakalathanan,
Journal of Medical
Imaging and
Australia Only discusses the radiographic
findings in H1N1 patients
88
Radiation Oncology,
2013
16. Lum, Medical Journal
of Australia, 2009
Australia Modeling study to examine the
demands associated with critical
care services during the H1N1
pandemic
17. Khandaker,
Neurology 2012
Australia Neurologic findings associated
with H1N1 in pediatric patients;
no clearly defined parameters for
critically ill children
18. Li, Chinese Medical
Journal, 2012
China Describes only histopathological
findings
19. Capelozzi, Clinics,
2010
Brazil Describes only morphological
features associated with ARDS in
H1N1
20. Seixas,
Histopathology 2010
Brazil Describes histopathology in fatal
cases
21. Lorenzoni, Arquivos
de Neuro-Psiquiatria,
2012
Brazil Describes muscle biopsy results in
only fatal cases
22. Lenzi, Revista Da
Sociedade Brasileira
de Medicina Tropical
Brazil No outcomes associated with
critical illness reported separately
23. Morris, BMJ Open,
2012
Canada No mortality in critically ill
patients provided
24. Muller, PLoS One,
2010
Canada Has non-H1N1 data
25. Campbell, CMAJ,
2010
Canada Death and ICU admission not
described separately
26. Helferty, CMAJ,
2010
Canada No ICU outcomes described
27. Zahariadis, Infect Dis
Med Microbiol, 2010
Canada Only two patients described,
otherwise a review of
microbiology and genetics of
H1N1
28. Zhang, Chinese
Medical Journal,
2012
China No clinical outcomes described
29. Fang, PLos One,
2012
China No clinical outcomes described
30. Xu, PLos One, 2013 China Post-pandemic cohort described
31. Yang, Journal of
Infection, 2010
China Separate outcomes of critically ill
patients not described
32. Yan, Chinese Journal
of Internal Medicine,
2009
China Critically ill patients not described
89
33. Chen, Chinese
Journal of Radiology
China No clinically relevant outcomes
discussed
34. Wu, national Medical
Journal of China
China Only discusses the features of fatal
cases
35. Leick-Courtois,
Archives de Pediatrie,
2011
France Fewer than 5 critically ill patients
36. Luyt, Chest, 2012 France Discusses Long-term outcomes in
ARDS patients
37. Annane, Intensive
Care Medicine, 2012
France No outcomes of interest are
described
38. Fuhrman,
Eurosurveillance,
2010
France Outcomes in critically ill patients
not described separately
39. Wiramus, Annales
Francaises
d’Anesthesie et de
Reanimation, 2010
France Reviews epidemiological data
from different studies throughout
the world, no new data presented
40. Gonzalo-Morales,
Rev Chil Pediatr
2011
Chile Characteristics and outcomes
associated with critical illness not
clearly mentioned
41. Ugarte, Crit Care
Med, 2010
Chile No patient specific data of interest
provided
42. Gudmundsson,
Laeknabladid, 2010
Iceland Editorial
43. Prasad, The Journal
of the association of
Physicians of India
India Only describes autopsy findings
44. Bal, Histopathology,
2012
India Only describes autopsy findings
45. Sharma, Journal of
Infect Dev Ctries
2010
India No information on critically ill
patients
46. Shelke, Pathology
International, 2012
India Only pathological findings
described
47. Mishra, PLoSOne,
2010
India Does not describe any critically ill
patients separately
48. Kute, Indian Journal
of Critical Care, 2011
India Letter to the editor
49. Chudasama, Lung
India, 2011
India No outcomes in critically ill
patients reported
50. Chudasama, J Infect
Dev Countries, 2010
India No outcomes associated with
critical illness reported
51. Samra, Anaesth, Pain
and Intensive Care,
India Fewer than 5 patients
90
2010
52. Samra, Indian J
Community Med,
2011
India Letter to the editor, not describing
variables associated with critical
illness
53. Kinikar, Indian J
Pediatr, 2011
India No variables associated with
critical illness described
54. Kinikar, Indian J
Pediatr, 2012
India No variables associated with
critical illness described
55. Jahromi, International
Journal of Obstetric
Anesthesia, 2010
Iran Fewer than 5 patients
56. Gouya, Iranian Red
Crescent Medical
Journal
Iran No variables associated with
critical illness were discussed
57. Saleh, Iranian Journal
of Clinical Infectious
Diseases
Iran No outcomes associated with
critical illness reported
58. Baldanti, Clin
Microbiol Infect 2011
Italy Doesn’t describe specific
information in critically ill patients
59. Bellissima, Le
Infezioni in
Medicina, 2011
Italy Fewer than 5 patients
60. NIcolini, Rev Port
Pneumol, 2012
Italy No clinical outcomes of interest
described in the text
61. Valente, Radiol Med,
2012
Italy No clinical outcomes of Interest
described in the text
62. Okumura, Brain and
Development, 2012
Japan Critically ill population not
defined
63. Nukiwa, Clinical
Infect Dis, 2010
Japan Only fatal cases described
64. Lopez, Med
Intensiva, 2009
Spain Case Report
65. Chippiraz, Rev Esp
Quimioter, 2011
Spain Patients described in the study
have very low APACHE score, so
they were excluded
66. Pinilla, Emerg Radiol
2011
Spain No clinical outcomes associated
with critical illness reported
67. Martin-Loeches,
Respirology, 2011
Spain Describes only fatal cases in Spain
68. Peralta,
Eurosurveillance
2010
Spain Describes death and ICU
admission as a combined outcome
without a mechanism to
disaggregate
69. Gutierrez-Cuadra,
Revista Espanola de
Quimioterapia
Spain No data associated with critical
illness provided
91
70. Rodriguez, Medicina
Intensiva, 2011
Spain Describes the outcomes associated
with ICU admissions in the post
pandemic period
71. Cardenosa, Human
Vaccines, 2011
Spain Variables associated with critical
illness not described separately
from hospitalized patients
72. Gonzalez,
Enfermedades
Infecciosas y
Microbiologia
Clinica, 2011
Spain No specific variables associated
with critical illness described
separately
73. Viasus, Clinical
Microbiology and
Infection, 2011
Spain ICU admission and mortality were
used as a composite measure for
severe disease
74. Rodriguez, Archivos
de
Bronchoneumologia,
2010
Spain Review article
75. Bibro, Critical Care
Nurse, 2011
USA Case report
76. Nickel, Public Health
Reports 2011
USA Describes death and ICU
admission together with no
mechanism to disaggregate
77. Fowlkes, Clinical
Infectious Disease,
2011
USA Only describes the epidemiology
of fatal cases in USA
78. Strouse, Blood, 2010 USA No information on critically ill
patients
79. Farooq, J Child
Neurol, 2012
USA Outcomes in critically ill patients
are not separately reported
80. McKenna, BMC
Infectious Diseases,
2013
USA Describes death and ICU
admission together
81. Mendez-Figueroa,
Am J Obstet
Gynecol, 2011
USA Only 3 patients admitted to the
neonatal ICU
82. Jain, Clinical
Infectious Diseases
2012
USA Same population was reported in
article by Bramley et al
83. Skarbinski, Clinical
Infectious Diseases,
2011
USA Same population was reported in
article by Bramley et al
84. Regan, Influenza
2011
USA Only describes the epidemiology
of fatal cases in USA
85. Cox, Clinical
Infectious Diseases,
USA Only has information on pediatric
fatalities during the H1N1
92
2011 pandemic
86. Lee, Clinical
Infectious Diseases,
2011
USA Only has information on fatal
cases in New York
87. Louie, PLoS ONE,
2011
USA Only describes fatal cases in
California
88. Nguyen, Crit Care
Medicine, 2012
USA No information on mortality in the
entire cohort of patients
89. Michaels, American
Journal of Surgery,
2013
USA Only characteristics of ECMO
discussed in this article
90. Miller, Journal of
Intensive Care
Medicine, 2011
USA No outcomes of interest reported
91. Sundar, Journal of
Intensive care
Medicine, 2011
USA Variables all divided into short
term and long term mechanical
ventilation
92. Newsome, Birth
Defects Research
USA Only outcomes of Infants of
critically ill pregnant females
93.
94. Li, Journal of Clinical
Virology, 2009
USA Uses all patients infected with
different strains of influenza
95. Katouzian, Journal of
Investigative
Medicine, 2010
USA No outcomes of interest discussed
96. Pannaraj, Journal of
Perinatology, 2011
USA No outcomes of interest were
described
97. Jamieson, Lancet,
2009
USA Critically ill patients not described
separately
98. Valdes, Rev Cubana
Med Trop, 2011
Cuba Critically ill patients not described
separately
99. Molbak, Vaccine,
2011
Denmark ICU specific outcomes not
described
100. Ahmed, Influenza
and other respiratory
viruses, 2011
Egypt Outcomes associated with critical
illness not described
101. Bauernfiend,
Infection, 2013
Germany Influenza A H1N1patients not
clearly defined as compared to
infection due to other viruses
102. Lehners, Emerging
Infectious Diseases,
2013
Germany ICU admission and mortality were
reported together as a marker for
severe disease
103. Stein, Klin Pediatr
2011
Germany Reports only on premature
neonates
104. Burkle, Anaesthesist Germany Outcomes associated with critical
93
2010 illness not described clearly
105. Alb, Dtsch Med
Wochenschr, 2010
Germany Outcomes associated with critical
illness not described clearly
106. Zarogoulidis,
International Journal
of Internal Medicine,
2013
Greece Outcomes associated with critical
illness not reported separately
107. Lee, The Journal of
Infectious Diseases,
2011
Hong Kong Outcomes associated with critical
illness not reported separately
108. Lee, Thorax, 2013 Hong Kong Specific characteristics and
outcomes associated with critical
illness not reported separately
109. Sigurdsson, Laekna,
2010
Iceland Outcomes associated with critical
illness not reported clearly
110. Bayya- Ael, Crit Care
and Resuscitation,
2010
Israel No outcomes reported
111 Shaham, IMAJ, 2011 Israel No outcomes of interest reported
112. Saidel-Odes,
International Journal
of Infectious
Diseases, 2011
Israel Critically ill population not clearly
delineated
113. Takeda, Journal of
Anesthesia, 2012
Japan Majority of the patients included
in the study were in the post
pandemic phase
114. Fuchigami, Pediat
Emergency Med
2012
Japan Critically ill patients not reported
separately
115. Okada, J Infect
Chemother, 2011
Japan Patients did not meet our
definition for critical illness
116. Fujita, Influenza and
other respiratory
viruses, 2011
Japan Letter to the editor
117. Wada, Influenza and
other respiratory
viruses, 2010
Japan ICU admission and mortality were
described as a composite variable
with no mechanism to
disaggregate
118. Choi, Tuberc Respir
Dis 2010
South Korea Critically ill specific outcomes not
described
119. Na, Scandinavian
Journal of Infectious
Diseases, 2011
South Korea Critically ill specific population
not defined
120. Goong, Infection and
Chemotherapy
South Korea Critically ill specific population
not described
121. Balraj, Malaysian Malaysia Patient characteristics and
94
Journal of pathology,
2011
outcomes not described
122. Chowell, NEJM,
2009
Mexico No characteristics or outcomes
associated with critical illness
described
123. Echevarria-Zuno,
Lancet, 2009
Mexico Critically ill patients not described
separately
124. Vazquez- Perez,
Virology Journal,
2011
Mexico Critically ill patients not described
separately
125. Chowell, PLos One,
2012
Mexico Critically ill patients not described
separately
126. Silva-Pereya, NEJM,
2009
Mexico Only pathological findings
described
127. Rahamat-
Langendoen, Journal
of Clinical Virology
2012
Netherlands Critically ill patients not described
separately
128. Pajankar, Oman
Medical Journal,
2012
Oman Patients were not sick enough to
be considered critically ill
129. Rorat, Postepy HIg
Med Dosw, 2013
Poland Only describes fatal cases
130. Cholewinska,
Przeglad
Epidemiologiczny,
2010
Poland Critically ill patient outcomes not
described separately
131. Agha, Mediterranean
Journal of
Hematology and
Infectious Diseases,
2012
Saudi Arabia Critically ill patients not described
separately
132. Liu, Chin Crit Care
Med, 2010
China Only risk factors for critical illness
discussed, no outcomes associated
with critical illness were described
133. Siau, Singapore
Medical Journal,
2009
Singapore Critically ill patients not described
134. Wiegand, Wein Klin
Wochenschr, 2011
Switzerland Fewer than 5 patients
135. Bertisch, Swiss Med
Wkly, 2010
Switzerland Critically ill patients not described
136. Dede, BJOG, 2011 Turkey Only describes maternal deaths
associated with H1N1
137. Ozkan, Pediatric
Neurology, 2011
Turkey Critically ill specific cases are not
described
95
138. Gurgun, Tuberkuloz
ve Toraks Dergisi,
2010
Turkey Patients were not sick enough to
qualify to be considered critically
ill
139. Tutuncu, Saudi Med
J, 2010
Turkey Discusses risk factors associated
with mortality
140. Lucas, Health
technology
Assessment, 2010
UK Only discusses fatal cases
141. Mytton,
Eurosurveillance,
2012
UK No specific outcomes associated
with critical illness reported
142. Campbell, Epidemiol.
Infect 2011
UK No outcomes associated with
critical illness reported
143. Mytton, Epidemiol.
Infect, 2012
UK No specific characteristics or
outcomes associated with critical
illness described
144. Brett, PLos One 2011 UK ICU admission and death
considered as a combined outcome
145. Bewick, Thorax,
2011
UK Outcomes associated with critical
illness not reported
146. Myles, PLoS One,
2012
UK Outcomes associated with critical
illness not reported
147. Myles, Thorax, 2012 UK ICU admission and death
considered as a composite
outcome
148. Khan, Anaesthesia,
2009
UK Only assesses validity of SOFA
score as a triage tool
149. Fox, PLoS One 2012 Vietnam No separate data on critically ill
patients
150. Wang, Chin Crit Care
Med, 2010
China Only 4 patients described
151. Kato, Nippon Rinsho-
Japanese Journal of
Clinical Medicine
Japan Review Article
152. Guler Ozturk Turkey Describes only 4 patients
153. Dalziel, BMJ 2013 Critical illness and mortality were
considered as a composite
outcome
154. Jamieson, Lancet,
2009
USA Outcomes associated with critical
illness not reported separately
155. Evdokimov,
Anesteziologiia i
Reanimatologiia,
2010
Russia Full text not available
156. Dabnach, Emerging
Infectious Diseases,
Chile Specific characteristics associated
with critical illness not discussed
96
2011
157. Olga, Anesteziologie
e Intenzivni
Medicina, 2010
Czech Full text article not available
158. Oersted, Clin
Microbiology and
Infection, 2012
Denmark No outcomes associated with
critical illness discussed
159. Snacken, Influenza
and Other
Respiratory Viruses
Multiple
Countries
Outcomes associated with critical
illness not described clearly
160. Nakashidze,
Georgian Medical
News 2012
Georgia Outcomes associated with critical
illness not reported separately
161. Chowell, NEJM,
2009
Mexico Critically ill population not
described clearly
162. Firstenberg,
Emerging Infectious
Diseases, 2009
USA Case Report
163. Gomez,
Eurosurveillance,
2009
Peru Critically ill population not
described
164. Grijalva- Otero,
Archives of Medical
Research, 2009
Mexico Describes only fatal cases
165. Moreno, Intensive
Care Medicine, 2009
NA Review
166. Oliveira,
Eurosurveillance,
2009
Brazil Characteristics associated with
critical illness not described
separately
167. Fowler, Crit Care
Med, 2010
NA Review article
168. Patel, Anaesthesia,
2009
UK Fewer than 5 patients
169. Peters, Deutsches
Arzteblatt, 2009
Germany Editorial
170. Smetanin, Canadian
Journal of Infectious
Diseases and Medical
Microbiology, 2009
Canada Patient level variables and
outcomes not described
171. Presanis, PLoS
Medicine, 2009
USA Bayesian Model evaluating
severity associated with H1N1
172. Taran, Revista de la
Facultad de Ciencias
Medicas de Corboda,
2009
Argentina Critically ill patients not described
separately
97
173. Webb, Critical care
and Resuscitation
Australia Editorial
174. Akritidis, American
Journal of
Cardiology, 2010
Greece Critically ill patients not described
separately
175. Allard, Diabetes
Care, 2010
Canada Critically ill patients not described
separately
176. Bellani, Intensive
Care Unit, 2010
Italy Case Report
177. Berryman, Nursing in
Critical Care, 2010
UK Case Report
178. Chitnis, WMJ, 2010 USA Critically ill patients not described
separately
179. Castilla, Euro
Surveillance, 2010
Spain Critically ill patients not described
180. Chiumello,
IntensiveCare
Medicine, 2010
Italy Outcomes associated with critical
illness not discussed
181. He, Journal of
Central South
University, 2010
China Outcomes associated with critical
illness not described
182. Derdak, Crit Care
Medicine, 2010
USA No patient data given
183. Jaber, Annales
Francaises d’
Anesthesie et de
Reanimation, 2010
France Review Article
184. Jardim, Early Human
Development, 2010
Portugal Patients did not meet our critically
ill definition
185. Morgan, PLoS ONE,
2010
USA Outcomes associated with critical
illness not described
186. Schoub, Expert
Review of
Respiratory
Medicine, 2010
South Africa Review
187.
188. Staudinger, Wiener
Klinische
Wochenschrift, 2010
Austria Review article
189. Weiss, Pneumologie,
2010
Germany Review Article
190. Bahloul, Trends in
Anaesthesia and
Critical Care, 2010
Tunisia Review Article
191. Charu, CID, 2011 Mexico Only fatal cases discussed
192. Falagas, Argentina Review Article
98
Epidemiology and
Infection, 2011
193. Fezeu, Obesity
reviews, 2011
France Systematic Review
194. Mosby, American
Journal of Obstetrics
and Gynecology,
2011
USA Systematic Review
195. Presanis, BMJ, 2011 UK Mathematical model of severity
196. Van Kerkhove,
Influenza and other
Respiratory viruses,
2011
Multiple
Countries
No outcomes associated with
critical illness described
197. Van Kerkhove, PLoS
ONE, 2011
Multiple
Countries
No outcomes associated with
critical illness reported
198. Wong, Perfusion,
2011
NA Review
199. Barai, Australasian
Medical Journal,
2012
India Outcomes associated with critical
illness not described
200. Berdai, Pan African
Medical Journal,
2012
Morocco Outcomes associated with critical
illness not described
201. Dawood, The Lancet
Infectious Diseases,
2012
Multiple
Countries
Outcomes associated with critical
illness not described
202. Dubrov, Intensive
Care Medicine, 2011
Ukraine Abstract only
203. Fernandez, Medicina
Clinica, 2012
NA Post pandemic report
204. Homaira, Bulletin of
WHO, 2012
Bangladesh Variables associated with critical
illness not described
205. Roll, Infection, 2012 Germany Critically ill patients not described
206. Rolland- Harris,
Epidemiology and
Infection, 2012
Canada Critically ill patients not
described
207. Schuck-Paim, PLoS
ONE, 2012
Brazil Critically ill patients not described
208. Kuchar, Respiratory
Physiology and
Neurobiology
Poland Critically ill patients not described
209. Marzano, Journal of
Medical Virology,
2013
Italy Critically ill patients not described
210. Golokhvastova,
Klinicheskaia
Russia Full text not available
99
Meditsina , 2012
211. Iatyshina,
Terapevticheskii
Arkhiv, 2010
Russia Full text not available
212. Klimova,
Terapevticheskii
Arkhiv, 2010
Russia Full Text Not available
213. Kolobukhina,
Terapevticheskii
Arkhiv, 2011
Russia Full text not available
214. Luzina,Klinicheskaia
Meditsina, 2011
Russia Full text not available
100
Appendix 9: CASE REPORT FORM
Number:
Is the Study Data duplicated Yes No
Name of the Author and Study:
Study Variables
1. Year of Publication:
2. Period of Study
a. Start: b. Stop:
3. Hemisphere of Study:
4. Country of Study:
5. World Bank Region of the Country
6. Single Center Vs Multicenter :
7. Part of a Database: Yes No
a. Name of Database
8. Multiple Countries in the Study: Yes No
a. List of countries:
9. World Bank economic status country:
Low Income
Lower-Middle Income
Upper Middle Income
High Income
10. Number of Patients in the Study:
11. Patients with: N %
Confirmed H1N1
Probable H1N1
Suspected H1N1
12. Population Under Study
a. Adults
Peds
Both
101
b. Unselected Critical Care Population Specific population
Which Kind of Specific population:
13. Demographics
Age Mean SD Median IQR Other
Sex (Females) Number Percentage
14. Severity of Illness score ( Day 1)
Type Mean SD Median IQR
APACHE II/III/IV
SOFA
PRISM III
Other:
15. Major Co-Morbidities N %
Lung Disease
Heart Disease
Renal Disease
Neurologic
Liver Disease
Malignancy
Immunosuppressed
Diabetes
Obesity
Smoker
Substance Abuse
Pregnancy
16. Incidence of Specific diagnosis (At Admission) N %
a. Septic Shock
b. Acute renal Failure
c. ARDS
17. Use of Specific Therapies during ICU stay N %
a. Inotropes
b. Renal Replacement Therapy
c. Mechanical Ventilation
i. Invasive
ii. Non Invasive
iii. Failure of Non Invasive
102
18. Mechanical Ventilation Parameters (Day 1)
Mean SD Median IQR
a. FIO2
b. PaO2/FiO2
c. PEEP
d. Oxygen Index
e. Mean Airway Pressure
19. Use of Rescue therapy For Severe Hypoxemia ( At any time in ICU)
N %
a. Inhaled Nitric Oxide
b. Inhaled Prostacyclins
c. Neuromuscular Blockade
d. High Frequency Oscillation
e. Prone Positioning
f. ECMO / other ECLS
g. APRV
h. Recruitment Maneuvers
20. Outcomes Mean SD Median IQR
Duration of Mechanical ventilation
Those Dying
Those not Dying
All
Ventilation free days (of 28 or specify)
ICU Length of Stay
Those Dying
Those not Dying
All
ICU free days (of 28 or specify)
Mortality
ICU Hospital
28-Day 30-Day
60 Day 90 Day
Other (specify):___________
103
Appendix 10: Components of Newcastle Ottawa Scale
Cohort Studies
Selection Comparability Outcome
Representativeness of cohort Cohorts are comparable on the
basis of design or analysis
Assessment of outcome
Selection of non-exposed
cohort
Ascertainment of exposure Adequate follow up
Outcome absent
Case Control Studies
Selection Comparability Exposure
Adequate case definition Comparability of cases and
controls on the basis of design
or analysis
Ascertainment of exposure
Representativeness of cases Methods similar for cases and
controls Selection of controls
Definition of controls Non response rate
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