Allan Garland, MD, MA

Associate Professor of Medicine & Community Health Sciences

University of Manitoba

Long-term versus Short-term

Prognostication in Critical Illness

Conflicts of Interest

Nothing to declare

Introduction & Rationale - 1

Prognosticating, to inform discussing achievable treatment plans and goals with patients/surrogates is one of the most important things we do in the ICU

The usual ICU risk stratification systems only address short-term outcomes, while patients care more about long-term survival (JGIM 5:402,1990)

In the absence of established quantitative approaches to prognosticating, physicians commonly use gestalt

Trainees consistently report that they receive inadequate training and feel ill-prepared for prognosticating (J Palliative Med 1:347,1998; CCM 30:290,2002)

Introduction & Rationale - 2

SO -- it’s important to better understand the main determinants of long-term mortality in critical illness

Hypothesis: The determinants of long-term mortality differ from the main determinants of short-term mortality

A necessity in addressing this question is to separately analyze the short-term and long-term phases

Identifying the determinants of long-term mortality requires analysis that starts after the short-term phase has ended

– prior studies assessing long-term survival failed to do this

Methods

Data: linked provincial administrative data + clinical database covering all Winnipeg ICUs

All adults admitted to all ICUs in the Winnipeg Regional Health Authority of Manitoba over 11 years, 1999-2010

– only included each person’s first ICU episode

Preliminary step: Kaplan-Meier survival curve

3 multivariable mortality regression models

– short-term -- logistic model; death to 30 days after ICU admit

– long-term -- Cox model; time to death >90 days after ICU admission among those who survived to day#90

– both -- Cox model; time to death starting from ICU admission

Covariates for Mortality Models

Age, sex, residency location (in vs. outside Winnipeg), SES (average household income by postal code); comorbidities

ICU admission details: year; timing (weekday vs. weekend, days vs. nights); pre-ICU location (ED, ward, OR/PACU); hospital type (community vs. tertiary)

ICU admission diagnosis category

Severity of illness: GCS; APACHE II APS excluding neurologic subscore; use of life support during first 2 ICU days (invasive MV, vasoactive agents, dialysis)

Cubic splines to allow for nonlinear relationships between of mortality with age, APS

Evaluation of Impact of Groups of Variables

Quantified the predictive power of five clusters of variables

– age

– sex and SES

– comorbid conditions

– acute illness characteristics (diagnosis, GCS, APS, pre-ICU location, need for MV ventilation, vasoactive drugs, dialysis)

– remaining model variables (admission timing, admission year, hospital type, residency location)

For this used Akaike’s Information Criterion (AIC)

– a goodness-of-fit measure for these models

– within each model -- evaluated DAIC when one of the variable clusters was omitted from the model

Patients & Illnesses

Variable

N 33,324

Female sex (%) 40.5%

Age (yrs), mean ± SD 63.5 ± 16.2

ICU admit diagnosis category (%)

Circulatory System 60.4

Symptoms, Signs (includes sepsis) 11.8

Respiratory System 10.7

Injury and Poisoning 7.6

Digestive System 3.4

APACHE II score, mean ± SD 15.3 ± 7.9

Mechanical ventilation ICU days 1-2 (%) 57.3

Vasoactive drugs ICU days 1-2 (%) 46.2

Renal dialysis ICU days 1-2 (%) 5.4

Results

Median lengths-of-stay:

– ICU: 2.4 days (IQR 1.1-4.6)

– hospital: 11 days (IQR 6-24)

Mortality (after ICU admission)

– 15.9% died within 30 days

– 19.5% died by 90 days

Kaplan-Meier Survival Curve (unadjusted)

0 2 4 6 8 10 120.00

0.25

0.50

0.75

1.00

30 days

90 days

S

urv

iva

l F

ract

ion

Years after ICU Admission

2 phases of

mortality

associated with

critical illness

Early high rate

of death

Later lower rate

of dying that is

clearly

established a few

months after

ICU admission

Independent variable

Death at 30 days

after ICU admission

Odds Ratio

Death >90 days

after ICU admission,

among 90 day survivors

Hazard Ratio

Age asymptotic rise * asymptotic rise *

Female sex 1.00 0.85 *

Socioeconomic status

Lowest urban income (reference) -- --

Highest urban income 0.83 * 0.72 *

Institutionalized 0.62 * 1.40 *

Regression Results - Demographics

Regression Results - Selected items

Death at 30 days

Odds Ratio

Death >90 days

Hazard Ratio

Comorbid conditions

Metastatic cancer 3.44 * 4.68 *

Liver disease 2.87 * 1.72 *

Congestive heart failure 1.17 * 1.61 *

Chronic pulmonary disease 1.12 * 1.51 *

Diabetes 0.93 1.39 *

Obesity 0.79 * 0.97

Drug abuse 0.71 * 1.42 *

Admission diagnoses

Circulatory System (reference) -- --

Respiratory System 1.23 * 1.55 *

Genitourinary System 0.46 * 1.38 *

Regression Results - Severity of Illness

Variable Death at 30 days

Odds Ratio

Death >90 days

Hazard Ratio

APS-Neurologic subscore asymptotic rise * rise then plateau*

Glasgow Coma Scale score

3-5 21.02 * 0.98

15 (reference) -- --

Mechanical ventilation ICU days 1/2 1.51 * 0.99

Vasoactive drugs ICU days 1/2 1.42 * 0.95

Dialysis ICU days 1/2 1.13 1.36 *

BUT

The magnitude and p-values of coefficients

in regression equations do not directly

indicate their predictive power

Death to

30 days after

ICU admission

Acute illness characteristics 1.00

Age 0.14

Comorbid conditions 0.09

Admission year, admission timing,

hospital type, residency location 0.002

Sex, socioeconomic status 0.001

Details of the acute illness are the dominant factor relating to short-term mortality

Relative Predictive Value of Variables

Post-90 day survival,

among 90 day

survivors

Acute illness characteristics 0.26

Age 1.00

Comorbid conditions 0.93

Admission year, admission timing,

hospital type, residency location 0.04

Sex, socioeconomic status 0.07

Relative Predictive Value of Variables

Age and comorbid conditions are the dominant determinants of long-term mortality, among short-term survivors

– same as for the general population

Death to

30 days after

ICU admission

Post-90 day survival,

among 90 day

survivors

Survival from

ICU admission

Acute illness characteristics 1.00

Age 0.69

Comorbid conditions 0.55

Admission year, admission timing,

hospital type, residency location 0.04

Sex, socioeconomic status 0.03

Relative Predictive Value of Variables

Main determinants of long-term mortality starting from onset of critical illness, are acute illness, age and comorbid conditions

Relative Predictive Value of Variables

Death to

30 days after

ICU admission

Post-90 day survival,

among 90 day

survivors

Survival from

ICU admission

Acute illness characteristics 1.00 (ref) 0.26 1.00 (ref)

Age 0.14 1.000 (ref) 0.69

Comorbid conditions 0.09 0.93 0.55

Admission year, admission timing,

hospital type, residency location 0.002 0.04 0.04

Sex, socioeconomic status 0.001 0.07 0.03

--------------- Regression models ---------------

Long-term mortality starting from onset of critical illness, is determined by a mixture of the main determinants of short-term and long-term mortality -- as expected

Summary: Main Findings

There are 2 distinct phases of survival related to critical illness

Death in the month after onset mainly determined by the type and severity of acute illness, but this influence decayed relatively rapidly -- age and comorbidity were only minor contributors to short-term survival

Among those who survived to 90 days after illness onset, subsequent survival was mainly determined by comorbid conditions and age, as in the general population

Discussion - 1

Appreciation of these phases is relevant, and can be helpful in discussing achievable goals and care plans

People generally place more value on long-term than short-term survival conversations focused only on the chance of surviving the acute illness are inadequate

Clarity may be improved by explicitly framing these discussions around short-term followed by long-term considerations:

– first discuss the chances of short-term survival from the critical illness, as determined by its type and severity

– then, if short-term survival seems sufficiently possible discuss long-term survival, as mainly related to age and the burden of comorbid conditions

Discussion - 2

Strengths

– large, population-based study assessing consecutive, unselected patients admitted over a substantial timespan to all types of ICUs

– assessed a wide and robust range of potential determinants of mortality

– first study whose methodology allowed for clear delineation of short-term from long-term influences on outcome, and did so on the same patient cohort

Limitation

– some known determinants of mortality were not contained in our data -- prehospital living site, prehospital functional status, post-hospital discharge location

Collaborators

Randy Fransoo, PhD

Kendiss Olafson, MD, MPH

Clare Ramsey, MD, MSC

Marina Yogendran, MSc