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PREDICTORS OF PERITONITIS AMONG CANADIAN PERITONEAL
DIALYSIS PATIENTS
By
Sharon J. Nessim, MD
A thesis submitted in conformity with the requirements for the degree of Master of
Science
Graduate Department of the Institute of Medical Science
University of Toronto
© Copyright by Sharon J. Nessim (2009)
ii
Predictors of Peritonitis among Canadian Peritoneal Dialysis Patients
Sharon J. Nessim
Master of Science, Institute of Medical Science, University of Toronto, 2009
Abstract
Despite the decreasing incidence of peritoneal dialysis (PD) peritonitis over time, its
occurrence is still associated with adverse outcomes. This thesis focuses on
determining factors associated with PD peritonitis in order to facilitate identification of
patients at risk.
Using data collected in a multicentre Canadian database between 1996 and 2005,
the study population comprised 4,247 incident PD patients, of whom 1,605 had at
least one peritonitis episode. Variables independently associated with peritonitis
included age [rate ratio (RR) 1.04 per decade increase, 95% CI 1.011.08], Black
race (RR 1.37, 95% CI 1.001.88) and having transferred from hemodialysis (RR
1.24, 95% CI 1.111.38). There was an interaction between gender and diabetes
(p=0.011), with an increased peritonitis risk only among female diabetics (RR 1.27,
95% CI 1.101.47). Choice of continuous ambulatory PD vs. automated PD did not
influence peritonitis risk. These results contribute to our understanding of peritonitis
risk among PD patients.
iii
Acknowledgements
I would like to thank several people, without whom this thesis would not have been
possible. First and foremost, I am extremely grateful to my supervisor, Dr. Vanita
Jassal, for her invaluable guidance, advice and support throughout my Master’s
degree. I am also indebted to my thesis committee members, Dr. Joanne Bargman,
Dr. Peter Austin and Dr. Jan Hux, who provided insight into the clinical importance of
the project, the study design and the intricacies of the data analysis and
interpretation. I thank you all for your interest, your time and your patience. I would
also like to thank Dr. Ken Story, Dr. Alex Kriukov and Dr. Rosane Nisenbaum for
assistance and advice regarding the statistical methods used in this thesis.
This work was generously supported by a Kidney Foundation of Canada Fellowship
Award, as well as by an Educational Fellowship from Baxter Healthcare. The
University of Toronto Clinician Scientist Program provided additional financial
support.
iv
Table of Contents
Abstract………………………………………………………………………………………ii
Acknowledgements…………………………………………………………………………iii
List of tables……………………………………………………………………………vii, viii
List of figures………………………………………………………………………………..ix
List of Abbreviations…………………………………………………………………………x
1. INTRODUCTION……………………………………………………………………….....1
1.1 Rationale………………………………………………………………………………1
1.2 Research objectives….…………………...…………………………………………2
1.3 Hypotheses…………………………………………………………………………...2
2. BACKGROUND…………………………………………………………………………..4
2.1 Endstage renal disease and dialysis options….…………………………………4
2.1.1 Renal replacement therapy: Peritoneal dialysis vs. hemodialysis…..….4
2.1.2 Peritoneal dialysis submodalities: CAPD and APD…..………………….7
2.2 Peritonitis in peritoneal dialysis patients………………………………………….9
2.2.1 Definition……………………………………………………………………..9
2.2.2 Incidence and outcomes…..………………………………………………10
2.2.3 Risk factors common to all PD patients……..…………………………..10
2.2.4 Peritonitis prevention strategies……………………………..….………..11
2.3 Predictors of peritonitis…………………………………………………….………16
2.3.1 Current knowledge regarding peritonitis risk……………………………16
2.3.2 Age…………………………………………………………………………..19
v
2.3.3 Peritoneal dialysis submodality: CAPD vs. APD..………………………21
2.4 Statistical methodology used to study occurrence of peritonitis………………23
3. METHODOLOGY………………………………………………………………….........28
3.1 Data sources………………………………………………………………………..28
3.2 Patient population…………………………………………………………………..29
3.3 Model covariates……………………………………………………………………29
3.4 Outcomes……………………………………………………………………………30
3.5 Statistical Analyses…………………………………………………………………31
4. RESULTS………………………………………………………………………………...33
4.1 Patient cohort……...………………………………………………………………..33
4.2 Independent predictors of PD peritonitis…......................................................34
4.3 Interactions……………………………………………...…………………………..35
4.4 Era effect…………………………………………………………..………………..36
4.5 Comparison of peritonitis modeling strategies………………………………….36
4.6 Sensitivity analysis for peritonitis relapse/recurrence exclusion criteria……..37
5. DISCUSSION……………………………………………………………………………38
5.1 General discussion………………………………………………………………...38
5.2 Impact on nephrology practice……………………………………………………44
5.3 Limitations……………………………………………………………………..……45
5.4 Conclusions………………………………………………………………..……….48
5.5 Future directions……………………………………………………………………48
6. ILLUSTRATIONS…...............................................................................................50
6.1 Tables…………………………………………………………………………….....51
vi
6.2 Figures………………………………………………………………………………68
7. REFERENCES………………….............................................................................71
8. APPENDIX…………………………………………………………………………........85
vii
List of Tables
Table 1. Summary of studies testing the association between age and peritonitis……………………………………………………………………51
Table 2. Summary of studies on the association between CAPD vs. APD and peritonitis……………………………………………………………………52
Table 3. Distribution of peritonitis episodes within the patient cohort…………..53
Table 4. Baseline demographic characteristics for the entire patient cohort…..54
Table 5. Comparison of POET cohort vs. CORR data…………………………...55
Table 6. Multivariable negative binomial model for the outcome of peritonitis...56
Table 7. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch…………………..57
Table 8. Multivariable AndersenGill model for the outcome of peritonitis……..58
Table 9. Multivariable AndersenGill model for the outcome of peritonitis in the subgroup of patients with no submodality switch……………………….59
Table 10. Interaction between diabetes and peritonitis by gender………………..60
Table 11. Testing for interactions between each variable and era………………..61
Table 12. Association between age and peritonitis by era…………………………62
Table 13. Comparison of results of multivariable negative binomial model and AndersenGill model for peritonitis………………………………………..63
Table 14. Multivariable negative binomial model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)……………………………………………………………………….64
Table 15. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria) ……………………65
viii
Table 16. Multivariable AndersenGill model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)………………………………………………………………………66
Table 17. Multivariable AndersenGill model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria) …………………...67
ix
List of Figures
Figure 1. Illustration of intraluminal and periluminal entry of organisms into the peritoneal cavity…………………………………………………………….68
Figure 2. Flow diagram of patient cohort from POET database…………………..69
Figure 3. Distribution of patients in POET database by province………………...70
x
List of Abbreviations
ANZDATA Australia and New Zealand Dialysis and Transplant Registry
APD Automated peritoneal dialysis
CAPD Continuous ambulatory peritoneal dialysis
CI Confidence interval
CKD Chronic kidney disease
CNS Coagulasenegative staphylococcus
CORR Canadian Organ Replacement Register
ESRD Endstage renal disease
GN Glomerulonephritis
HD Hemodialysis
HR Hazard ratio
PD Peritoneal dialysis
POET Peritonitis Organism Exit sites Tunnel infections
RCT Randomized controlled trial
RR Rate ratio
RRF Residual renal function
SD Standard deviation
USRDS United States Renal Data System
1
INTRODUCTION
1.1 Rationale
Despite improvements in the management of patients with chronic kidney disease
(CKD), there was a 34% increase in the number of Canadian patients reaching end
stage renal disease (ESRD) between 1997 and 2006 (1). When renal transplantation
is not an immediate option, the only remaining renal replacement therapy option for
patients who reach ESRD is dialysis. There are currently two forms of dialysis
available: hemodialysis (HD) and peritoneal dialysis (PD). While HD is the more
commonly utilized modality, PD has the advantage of being a homebased therapy
(relative to HD, in which the majority of patients are required to come to hospital for
treatments thrice weekly).
One of the most concerning complications associated with PD is infection of the
peritoneal space – known as peritonitis. To date, little is known about how best to
study the occurrence of peritonitis and what factors predict its occurrence.
2
1.2 Research Objectives
The thesis will address the following research questions:
(1) Among incident Canadian peritoneal dialysis patients, what patient and dialysis
related factors are associated with peritonitis? Specifically:
(i) Is increasing age associated with an increased risk of peritonitis?
(ii) Does choice of PD submodality (continuous ambulatory PD (CAPD) vs.
automated PD (APD)) affect peritonitis risk?
(2) Are the results of rate and timetoevent analyses comparable in the study of the
occurrence of peritonitis?
1.3 Hypotheses
This thesis seeks to identify whether there are baseline demographic characteristics
among incident PD patients that predict the occurrence of peritonitis. Two variables
for which the current literature is inconsistent include age and PD submodality. It is
hypothesized that increasing age is associated with a higher risk of peritonitis, and
that the two submodalities of PD, CAPD and APD, are similar with regard to
peritonitis risk.
3
It is generally believed that it is vital to incorporate the amount of time spent on
dialysis into the way in which peritonitis as an outcome variable is defined, such that
the most appropriate analyses for the occurrence of peritonitis would involve defining
peritonitis as a either a rate or timetoevent. We hypothesized that both rate and
timetoevent analyses are effective tools to study peritonitis, and would yield similar
predictors of peritonitis with similar risk estimates.
4
2. BACKGROUND
2.1 Endstage renal disease and dialysis options
2.1.1 Renal replacement therapy: Peritoneal dialysis vs. hemodialysis
Chronic kidney disease is a growing medical problem in the general population. The
increased prevalence of renal disease has largely resulted from the rising life
expectancy (2), coupled with an increasing prevalence of diabetes mellitus (3). While
the greater awareness of the presence of renal disease among general practitioners
has led to earlier referral of patients to nephrologists and better preventive care (4,
5), progression to ESRD remains a major problem. In Canada, the number of
individuals starting renal replacement therapy has increased from 3,958 in 1997 to
5,321 in 2006 (1).The optimal mode of renal replacement therapy in these patients is
renal transplantation (6). However, given the relatively low living kidney donation rate,
the long waiting time for a deceased donor kidney and the ineligibility for transplant in
some patients, dialysis is frequently the only available treatment option.
There are two dialysis modalities, hemodialysis (HD) and peritoneal dialysis (PD).
Hemodialysis is a form of renal replacement therapy in which the patient’s blood is
passed across a filter that allows for removal of accumulated toxins and electrolytes
via diffusion, and removal of excess fluid via ultrafiltration. Using this filter (known as
the dialyzer), the dialysis machine attempts to reproduce normal kidney function. In
5
order for HD to be effective, patients generally require a minimum of 12 hours per
week connected to the machine. This is usually achieved by having patients come to
hospital for 4hour treatment sessions three times per week. Some HD patients can
be trained to dialyze themselves at home, but home HD remains an option for only a
minority of patients. Access to the patient’s bloodstream for these treatments requires
some form of vascular access, with options including a tunneled intravenous dialysis
catheter or an arteriovenous fistula or graft.
Peritoneal dialysis is the other form of dialysis. Rather than passing a patient’s blood
through an artificial dialyzer as occurs on HD, PD utilizes the patient’s own peritoneal
membrane as the ‘filter’ over which diffusion and ultrafiltration occur. A permanent PD
catheter is inserted through which an electrolytebalanced, glucoserich dialysis fluid
(known as dialysate) is infused into and drained from the peritoneal cavity. Once the
dialysate is instilled, there is diffusion of uremic toxins and electrolytes down their
concentration gradient from the bloodstream, across the peritoneal membrane and
into the fluid. Simultaneously, the high glucose concentration in the dialysate creates
an osmotic gradient for the movement of fluid from the bloodstream into the
peritoneal cavity. Once sufficient time has passed for diffusion and ultrafiltration to
occur, the dialysate is then drained from the peritoneal cavity and fresh dialysate is
instilled. The procedure of filling and draining the peritoneal cavity with dialysate is
repeated several times per day.
6
Survival outcomes for patients on HD vs. PD have been compared in several studies.
Some have reported better outcomes with PD (79), while others have shown better
outcomes with HD (1012) or no difference between the two modalities (1316). The
inconsistency in the literature may relate to several factors. Firstly, the study
populations varied widely, as demographic characteristics of patients differed among
Canada, the United States and the European countries in which studies were
conducted. Secondly, the time period over which the studies were carried out ranged
from as early as 1987 to as recently as 1999, such that advances in dialytic therapies
over that time period could have differentially affected outcome. Thirdly, some studies
compared incident PD and HD patients, while others focused on prevalent patients.
Finally, the follow up time was variable across studies. The latter two considerations
are particularly important as it appears that the relationship between dialysis modality
and outcome changes over time, such that the survival advantage for PD reported in
some studies was only seen in incident cohorts during the first two years after
initiation of dialysis (79).
The biggest limitation in determining the effect of dialysis modality on outcome is the
observational nature of the studies used to try to answer this question. As such, they
cannot fully account for the fact that patients who go on PD, a homebased modality,
frequently differ systematically from those who go on incentre HD despite attempts
to adjust for potential confounders. In order to answer this question while avoiding
systematic bias, an attempt was made to do a largescale randomized controlled trial
(RCT) of HD vs. PD (17). Unfortunately, this RCT was unsuccessful because of
7
difficulty in recruitment, with only 5% of eligible patients having no preference for PD
or HD and agreeing to be randomized to either modality. In the absence of an RCT,
physician opinion varies widely. While some physicians have a bias favoring either
HD or PD, most physicians believe that HD and PD are equally effective renal
replacement therapy options and that the decision should be left to the patient when
possible. Hence, the majority of patients who receive predialysis care are given
information on both modalities, and are offered a choice.
In most countries, HD is the most frequently used therapy, although this varies widely
by region (3, 1821). In Canada, 82% of prevalent dialysis patients in 2006 were on
HD with the remaining 18% on PD (1). Since PD is a homebased therapy, it provides
greater independence to patients and allows for less utilization of hospitalbased
resources relative to incentre HD. As a result, in 2005, the Ministry of Health in
Ontario made several recommendations in an attempt to increase the prevalence of
PD utilization to 30% (22). It is clear that a better understanding of the complications
associated with PD would aid in achieving this target.
2.1.2 Peritoneal dialysis submodalities: CAPD and APD
Once a decision is made to initiate PD, a choice must be made between the two
forms of PD: continuous ambulatory peritoneal dialysis (CAPD) and automated
peritoneal dialysis (APD).
8
In CAPD, patients are taught how to manually fill and drain their peritoneal cavity via
their PD catheter using aseptic technique. A typical patient performs 4 exchanges
daily, usually in the early morning, midday, evening, and before bedtime. For
example, 2 L of dialysate is instilled at 8 am and left to dwell until 12 pm. This fluid is
then drained, and another 2 L would be instilled to dwell until 4 pm, and so on. The
net result is typically 4 exchanges of fluid, with dialysate in the peritoneal cavity
throughout the 24hour period.
In APD, patients use an automated cycler to perform their exchanges during the
night. Patients on APD connect their PD catheter to the cycler before going to bed.
The cycler, which is programmable to the patient’s specifications, then instills
dialysate into the peritoneal cavity, and will exchange the specified volume of fluid at
preset intervals during the night while the patient sleeps. For example, the cycler may
be programmed to provide four 2 L exchanges over a 9hour period during the night.
In the morning, the patient will disconnect from the cycler, and carry on with his or her
daily activities. The choice of CAPD vs. APD in some instances may be guided by the
transport characteristics of the patient’s peritoneal membrane, but is often left to the
discretion of the patient based on lifestyle and usual daily activities. In the event that
a patient is unable to perform the manual exchanges required for CAPD due to visual
or cognitive impairment or impaired manual dexterity, APD is usually the submodality
of choice as connections to the cycler can be performed by a family member or
visiting nurse.
9
2.2 Peritonitis in peritoneal dialysis patients
2.2.1 Definition
The peritoneal cavity into which dialysate is infused in PD patients is a sterile
environment. Infection of the peritoneal space is known as peritonitis. In the general
population, peritonitis is an extremely rare occurrence, and usually results from a
perforated abdominal viscus with movement of organisms from the bowel lumen into
the peritoneal space. In contrast, peritonitis is a well described complication among
patients on PD. The ISPD has defined PD Peritonitis as at least 2 of 3 of the
following: (i) clinical symptoms or signs suggestive of peritoneal inflammation, (ii)
effluent cell count with greater than 100 white blood cells per µL, of which at least
50% are neutrophils, and (iii) a positive effluent Gram stain or culture (23).
Entry of organisms into the peritoneal cavity in PD patients can occur via several
mechanisms. The two most common mechanisms include introduction of organisms
into the lumen of the catheter by touch contamination at the time of catheter
connections, and periluminal entry from the exit site along the outside wall of the
catheter through the subcutaneous tunnel and into the peritoneal cavity (Figure 1).
Peritonitis episodes can also result from transmigration of organisms across the
intestinal wall, and rarely from bacteremia with seeding of the peritoneal cavity or
transvaginal migration of organisms.
10
2.2.2 Incidence and outcomes
In the early years after introduction of PD, peritonitis occurred once in every 910
patientmonths (24, 25). Since that time, however, the frequency of peritonitis as a
complication has continued to decline (2630), with current peritonitis rates as low as
1 in every 41 patientmonths (29).
Despite the decreasing peritonitis rates over time, the occurrence of peritonitis
remains a concern given its association with adverse outcomes. Specifically, PD
peritonitis is associated with increased mortality (31, 32) and hospitalization (33). In
addition, PDrelated infections are the most frequent reason for discontinuation of
PD, accounting for 28% of transfers to HD in one study (34). While HD patients do
not get peritonitis, those with tunneled dialysis catheters are instead prone to
catheterrelated bacteremia, such that overall infection rates between PD and HD are
similar (35). Despite the comparable infection risk, the occurrence of peritonitis and
the associated potential for adverse outcomes has led some nephrologists to avoid
recommending PD.
2.2.3 Risk factors common to all PD patients
There are several factors common to all PD patients that favor the occurrence of
peritonitis. Firstly, all patients have PD catheters that allow for communication
11
between the nonsterile external environment and the sterile peritoneal cavity. This
allows for both intraluminal and periluminal entry of organisms. A second factor
common to all PD patients is the use of dialysate containing a high concentration of
glucose. Since glucose is an excellent growth medium for bacteria, the introduction of
even a small inoculum of organisms may be enough to cause peritonitis.
Furthermore, it has been shown that ESRD patients have impaired host immunity
(3639), as well as abnormal peritoneal immune function (4042). The reason for the
impaired peritoneal immunity is thought to be the unphysiologic nature of
conventional PD solutions, including their high glucose concentration,
hyperosmolarity, acidic pH and the formation of glucose degradation products during
the heat sterilization of the dialysate bags. Under normal circumstances, local
peritoneal immunity plays an important role in the prevention and clearance of PD
peritonitis. When exposed to conventional dialysate, however, there is abnormal
leukocyte recruitment in response to inflammatory stimuli (42) and impaired
phagocytic function (40).
2.2.4 Peritonitis Prevention Strategies
With these common risk factors in mind, several modifications to PD practice have
been made over time in order to reduce peritonitis risk. The first major advance was
the introduction of improved PD connectology. The initial catheter connection method
involved conventional ‘spike’ connection systems. In the 1980s, it was hypothesized
that disconnect systems using a Y–set would be superior to spike connection
12
systems for the prevention of peritonitis. The “flush before fill” technique using a Y
connection system allowed for drainage of the spent dialysate into an empty drainage
bag, followed by flushing of the tubing with some fresh dialysate before infusion of
the remainder of the fresh dialysate into the patient. It was hypothesized that flushing
the tubing before filling the peritoneal cavity would reduce the risk of infusing
organisms introduced during the connection procedure into the peritoneal cavity. As
predicted, the use of this Yset resulted in important reductions in the rate of
peritonitis in several studies (24, 4345). Subsequently, a more advanced form of
disconnect system known as the doublebag system was also shown to be superior
to standard spike connection systems (45). While the double bag (or twin bag)
system was hypothesized to further reduce peritonitis risk relative to standard Ysets
by having one fewer connection, studies comparing these two disconnect systems
have not consistently shown a benefit of one over the other (4548). Based on the
available data, the 2005 International Society for Peritoneal Dialysis (ISPD)
guidelines suggest to avoid spiking of dialysis bags in CAPD patients, and to instead
use a doublebag system with the “flush before fill” technique to reduce the risk of
contamination (49).
The second major advance was the introduction of antibacterial ointments applied to
the PD catheter exit site or nares to reduce bacterial colonization. The risk associated
with bacterial colonization was first recognized by Luzar et al who studied S. aureus
nasal carriage in patients on CAPD. In this study, it was found that S. aureus nasal
carriers had exit site infection rates that were four times higher than noncarriers (50).
13
The most likely explanation for this is that patients who have S. aureus colonization in
their nares are more likely to have S. aureus colonization at their PD catheter exit
site. The corollary of this is that eradication of colonization would reduce peritonitis
occurring via periluminal migration of bacteria along the catheter tunnel into the
peritoneal cavity. Supporting this hypothesis, it has since been shown that application
of antibacterial ointments to the nares or catheter exit site not only reduces the risk of
exit site infection (30, 5153) but also of peritonitis (30, 53). The most studied
ointment is mupirocin, which is a topical antibacterial agent with excellent activity
against Gram positive organisms (54). The use of an agent active against Gram
positive bacteria is appropriate since S. aureus is the most common organism
causing exit site infection, accounting for 42% of episodes in one study of Canadian
patients (26). While the majority of the studies on ointments for peritonitis prophylaxis
have involved use of mupirocin, there are more recent data to suggest that
gentamicin cream applied to the catheter exit site is an excellent alternative (30). One
advantage of the latter agent over mupirocin is the provision of Gram negative
coverage, particularly against Pseudomonas species. Based on these data, the 2005
ISPD guidelines for PDrelated infections suggested using one of the following
regimens: (1) exit site mupirocin daily in all patients or only in S. aureus nasal
carriers, (2) intranasal mupirocin for 57 days each month in nasal carriers, or (3) exit
site gentamicin cream daily in all patients (49).
Other strategies that have been adopted over time to reduce risk of peritonitis and
exit site infection include use of downwarddirected catheter tunnels (55), and
14
administration of prophylactic antibiotics at the time of catheter insertion (56, 57).
Prophylactic antibiotics have also been recommended prior to colonoscopies with
polypectomy based on several case reports of peritonitis with enteric organisms
occurring shortly after such procedures (49, 5861).
In addition to the above strategies, several studies have looked at different PD
catheter designs. While modifications to the extraperitoneal segment of the catheter
have not led to reductions in peritonitis (6268), the data on use of single vs. double
cuff catheters are conflicting (6971). As a result, no definitive recommendations have
been made as to the optimal PD catheter type for the prevention of peritonitis (49).
Given that impaired peritoneal immunity is thought to be due in part to the
bioincompatibility of the standard dialysate solutions used, several studies have
sought to determine whether use of newer, more biocompatible PD solutions might
be associated with a lower peritonitis risk. The improved biocompatibility of these
solutions relates to their more neutral pH and their lower concentration of glucose
degradation products. In a small randomized crossover study comparing
biocompatible solutions with conventional solutions, the use of biocompatible
dialysate for 6 months was associated with enhanced phagocytic activity of peritoneal
macrophages, reduced constitutive inflammatory stimulation and better preservation
of the mesothelial cell integrity (72). Three observational studies to date have
reported a lower peritonitis rate with biocompatible solutions as compared with
standard solutions (7375), with another showing no effect (76). The only RCT of
15
conventional vs. biocompatible PD fluids that included data on infectious outcomes
did not show a difference in peritonitis rates between the groups (77), although
peritonitis was a secondary endpoint and the study was therefore not powered for
this outcome. Given the cost associated with these biocompatible solutions, further
randomized controlled studies are required to clarify whether the improved peritoneal
immunity translates into reduced peritonitis risk.
The importance of proper patient training in the prevention of PDrelated infections
has also been studied. In one RCT, 620 PD patients were randomly assigned to
receive either enhanced training using an adult learning theorybased curriculum or a
nonstandardized conventional training program. Those who received the enhanced
training had significantly fewer exit site infections and peritonitis episodes (78).
While the frequency of peritonitis has been dramatically reduced with the
incorporation of these preventive strategies, peritonitis still occurs. And despite the
presence of the aforementioned risk factors in all PD patients, it remains unclear why
some patients never develop peritonitis while others go on to have multiple episodes.
16
2.3 Predictors of peritonitis
2.3.1 Current knowledge regarding peritonitis risk
Several observational studies have attempted to identify factors that might predict the
occurrence of peritonitis in PD patients. Among demographic characteristics, two
American studies have identified an association between Black race and peritonitis,
with a 2632% increased risk (55, 79). In addition, a large observational study from
Australia and New Zealand reported a 76% increased risk of peritonitis among
Aboriginal patients (80). While each of these observational studies adjusted for a
wide range of patient and dialysisrelated factors, neither accounted for
socioeconomic characteristics which may have varied widely by racial category.
Another risk factor that has been associated with peritonitis is diabetes mellitus, with
a 13 to 64% increased peritonitis risk among diabetics (79, 81, 82). This is not
surprising as diabetic patients with renal disease have been shown to have an
increased risk of infections in general (35, 83). Obesity has also been linked with a
higher risk of peritonitis, with one study reporting a hazard ratio (HR) of 1.08 per 5
kg/m 2 increase in body mass index (80), and another reporting a HR of 1.29 in
patients with a body mass index ≥ 30 (84). This higher risk may relate to the
increased risk of dialysate leak among obese PD patients (85), in that leaks may
predispose to peritonitis. In addition, an abdominal pannus may overlie the exit site
and impair proper exit site care.
17
In addition to the above predictors, current or recent use of immunosuppressive
agents for previous transplantation or glomerulonephritis (GN) has been associated
with an increased risk of PD peritonitis (86). The increased risk among patients with
failed transplants has been confirmed in some studies (87, 88) but not in others (55,
89, 90). While immunosuppression would be expected to be associated with an
increased infection risk, the observational nature of these studies raises the
possibility of residual confounding, as patients who have received a renal transplant
are frequently the healthiest patients within a dialysis population. The improved
general health of these transplant patients may offset any potential adverse infection
risk associated with immunosuppression. Alternatively, one might hypothesize that
the absence of an association between immunosuppression and peritonitis in some
studies could reflect the relatively low doses of immunomodulatory agents used in
patients with failed transplants who have returned to dialysis.
Among biochemical parameters that are routinely measured in PD patients, a low
serum albumin at the time of initiation of PD has been shown to be associated with a
shorter time to first peritonitis, with HRs ranging from 1.351.67 per 10 g/L decrease
in baseline albumin (81, 91). The relationship between hypoalbuminemia and
peritonitis may be either causative or associative. Specifically, one could hypothesize
that malnutrition may directly increase risk of infection as a result of weaker immunity.
In contrast, it is possible that a low albumin at the time of PD initiation is simply a
18
marker of a more inflamed patient with greater comorbidity who is more predisposed
to infectious complications.
Another biochemical variable of relevance is residual renal function (RRF) (82). In a
recent study, each additional 1 ml/min/1.73 m 2 of residual glomerular filtration rate
was independently associated with a 21% lower risk of peritonitis. The reduced risk
associated with RRF may relate to the better solute clearance achieved (particularly
of middle molecules), as it is known that uremia impairs host immunity (3639).
Alternatively, RRF may simply be a marker of a healthier patient with either less
comorbidity or a shorter dialysis vintage, and therefore a patient less likely to develop
peritonitis.
In addition to known demographic and biochemical associations with peritonitis,
having a first peritonitis episode is associated with an increased risk of developing a
subsequent episode. In one study, a peritonitis episode occurring within the first 6
months after initiation of PD was associated with a shorter time to subsequent
peritonitis, with a HR of 2.15 (79). A second study showed a RR of 2.08 for peritonitis
in the patients who had a prior episode (55). This increased risk of subsequent
peritonitis likely relates in part to patient factors that predispose to the development of
peritonitis. However, an additional consideration may be the formation of a biofilm on
the PD catheter over time (9294). This biofilm is thought to consist of bacteria that
attach to the PD catheter and become surrounded by an impenetrable glycocalyx
matrix coat. It is hypothesized that the presence of a biofilm may predispose to the
19
development of peritonitis, and put patients at increased risk of subsequent infection
with the same organism due to difficulty in eradicating the organism. This hypothesis
was tested in a cohort of 198 patients with multiple peritonitis episodes (95). In this
study, 80% of patients had at least one repeat infection with the same organism, and
79% of patients had more than half of their peritonitis episodes caused by the same
organism, suggesting that bacterial biofilm formation on the peritoneal catheters may
be an additional factor playing a role in the frequency of peritonitis in some patients.
Two important variables for which data have been inconsistent in regard to peritonitis
risk include age and choice of CAPD vs. APD. These will be discussed below.
2.3.2 Age
The question of whether increasing age is associated with a higher risk of peritonitis
is an important one given the increasing number of older patients reaching ESRD (3).
Answering this question is particularly relevant as some nephrologists are reluctant to
offer PD as a dialysis option to elderly patients. This was evidenced by a recent study
in which North American nephrologists from several centers were asked to assess
medical eligibility for HD, PD and renal transplantation among patients with advanced
CKD (96). In this study, the most frequently cited reason for not offering PD to a
patient was older age.
20
The relationship between age and peritonitis among PD patients has been
investigated in several observational studies. The largest study to have looked at this
studied 11,975 American patients on PD between 1994 and 1997. With age treated
as a categorical variable, age ≤ 44 years was associated with an increased risk of
peritonitis relative to those aged 65 to 74, with a HR of 1.09 at 9 months (79). In
contrast, in a large, multicenter cohort of 3,162 patients from Australia and New
Zealand followed from 19992003, age ≥ 65 was associated with an increased
hazard of peritonitis relative to those under 25, 2544, and 4564 years of age (80).
Two other studies of Spanish PD patients reported a higher peritonitis rate in older
patients (97, 98), while another study demonstrated an increased peritonitis risk
among nondiabetic patients greater than 70 years of age (99). Several other studies
have reported no association between age and peritonitis (82, 100103). These
studies are summarized in Table 1.
There are several possible explanations for the inconsistent relationship between age
and peritonitis across studies. The first is that age has been variably defined in these
studies. For example, some studies used age as a categorical variable (with various
arbitrary cutoffs used to define ‘elderly’), while others used age as a continuous
variable. Another contributing factor is the varying size of the studies to date, such
that some of the smaller studies may have been underpowered to detect an
association, should one have been present. Furthermore, the studies that have
assessed the association between age and peritonitis span nearly two decades.
Since PD technique has changed significantly over time, it is difficult to know whether
21
improvements in technique would have had an impact on the relationship between
age and peritonitis. For example, elderly patients who have less manual dexterity
may be more likely to have breaks in aseptic technique, such that introduction of the
‘flush before fill’ technique and prophylactic ointments might offer greatest benefit in
this population and offset the increased risk. Thus, the best assessment of the
relationship between peritonitis and age would involve studying age as a continuous
variable in a large, contemporary cohort of PD patients.
2.3.3 Peritoneal dialysis submodality: CAPD vs. APD
With regard to choice of PD submodality, understanding the risk of peritonitis with
CAPD vs. APD is important as it has implications for our recommendations to
patients when they are choosing between these two options. CAPD was originally
proposed to be associated with an increased risk of peritonitis in the early years of
PD before advances in connectology. In contrast, the potential association between
APD and a higher peritonitis risk has been attributed to the presence of a high
comorbidity burden among a subset of APD patients, who may have been pre
selected for this modality on the basis of an inability to perform CAPD. If CAPD and
APD are equivalent in terms of infectious risk, it would reinforce our practice of
offering both options to patients initiating PD when feasible. In the absence of large,
contemporary RCTs comparing the outcomes of the two submodalities, we are left to
rely mostly on observational data to try to answer this question.
22
Several studies have addressed the relationship between use of CAPD vs. APD and
peritonitis, including one RCT and several observational studies. The clinical trial
conducted by de Fijter et al included 97 patients enrolled from 1988 to 1991, and
randomized them to CAPD using a Yconnector or APD. In this study, there were 54
peritonitis episodes among 25 CAPD patients, as compared with 31 episodes among
19 APD patients. This corresponded to a peritonitis rate that was significantly higher
among CAPD patients relative to APD patients (0.94 vs. 0.51 episodes per patient
year) (104). Two other observational studies that have looked at the peritonitis risk
associated with CAPD vs. APD have also suggested that CAPD is associated with a
higher peritonitis risk, with HRs ranging from 1.72 to 2.08 (97, 100). However, in the
largest observational study to have addressed this question, using data from 11,975
American PD patients, CAPD was associated with a 6% lower risk of peritonitis
relative to APD (79). Furthermore, another study of 1,205 Scottish PD patients found
no difference between CAPD and APD in terms of peritonitis risk (105). These
studies are summarized in Table 2.
While the only RCT to have addressed this question suggested that CAPD was
associated with a higher peritonitis risk than APD, this study was conducted
approximately 20 years ago at a time when peritonitis rates and PD practice were
significantly different from what they are at present. As a result, the external validity
of the study (or generalizeability) may be limited. In addition, some of the apparent
inconsistency among these studies may relate in part to the fact that some of the
studies in which CAPD was associated with a higher peritonitis rate included patients
23
who were on PD before the adoption of the improved connectology systems, which
greatly reduced the risk of contamination at the time of an exchange (24, 4345). The
implementation of such systems would have preferentially benefited CAPD patients.
Thus, studying a contemporary cohort of patients who initiated dialysis after adoption
of these more advanced connectology systems is vital to understanding the current
peritonitis risk with CAPD vs. APD.
2.4 Statistical methodology used to study occurrence of peritonitis
Some of the variability in the predictors of peritonitis that have been identified may
relate to the patient populations studied, the varying sizes of the cohorts studied, the
different variables included in the multivariable models in each study and the different
eras over which data were collected. However, an additional complicating factor is
the type of analysis chosen to assess for variables associated with peritonitis.
The choice of the type of multivariable regression model depends on the way in
which the outcome variable, peritonitis, is defined. The simplest way to define
peritonitis would be as a dichotomous variable, such that a patient either had or did
not have a peritonitis episode. It is apparent that this definition of peritonitis is limited
by the fact that it does not take into account the amount of time the patient is on PD
(which represents the time at risk). As a result, if all patients are followed for a very
short time, one would not be able to distinguish between those patients who would
24
never have developed peritonitis and those who were destined to get peritonitis after
a certain amount of ‘exposure time’ on PD. It is clear, then, that the definition of
peritonitis as an outcome variable should incorporate the amount of time a patient is
on PD.
The first method of defining peritonitis that incorporates time involves calculating a
peritonitis rate for each patient – that is, the number of episodes of peritonitis
experienced by a patient divided by the followup time. Models in which one can
determine the relationship between a variable and the peritonitis rate include the
Poisson model and the negative binomial model. The Poisson model is best used for
a stochastic process when events occur independently of one another. One
assumption inherent in Poisson modeling is that the mean is equal to the variance.
The negative binomial model is a variant of the Poisson model that does not make
the assumption that the mean is equal to the variance, and as a result, it provides a
better fit if there is overdispersion of the data (106, 107).
The second option for defining peritonitis with incorporation of time at risk is to study
the timetoperitonitis. The most frequently used model is the Cox proportional
hazards model (as long as the hazard associated with each variable is proportional
over time). This allows for determination of the predictors of a shorter time to
peritonitis. One limitation of this type of analysis is that it allows one to look only at
predictors of timetofirst peritonitis, such that one would not be able to utilize all data
on subsequent peritonitis episodes in the same patient. Performing a timetoevent
25
analysis incorporating multiple events occurring over time is possible, but to date, this
analytic tool has not been used for the study of peritonitis.
There are three timetoevent modeling approaches that have been used to study
multiple events within an individual: the AndersenGill model, the marginal (WLW)
model and the conditional (PWP) probability model (108). The AndersenGill model is
a variant of the Cox model that allows for incorporation of data on recurrent events.
One condition of this model is the assumption of independence of events within a
subject. Based on simulated data, this model gives nearly unbiased estimates of the
treatment effect even when an important covariate has been omitted. The marginal
model is a model that was first used by Wei, Lin and Weissfeld to analyze bladder
cancer data in the context of multiple recurrences per subject. Limitations of this
model include potential violation of the proportional hazards assumption, and biased
estimates of effect when covariates are omitted. The conditional model, proposed by
Prentice, Williams and Peterson, allows for variation in the underlying intensity
function from event to event. However, the conditional model is even more
susceptible to biased estimates when an important covariate is omitted. When
choosing between these three models for the study of PD peritonitis, the absence of
important covariates in many large dialysis datasets would favor the use of the
AndersenGill model.
The utility of both rate and timetoevent analyses in the study of peritonitis was
identified in the early years of PD therapy. The first insight into the optimal statistical
26
modeling of peritonitis came in 1981 when Corey studied a cohort of 129 Toronto PD
patients and determined that the distribution of peritonitis was random based on the
goodness of fit provided by the Poisson regression model (109). A later study
proposed a variant of this strategy in the form of a ‘mixed effects’ Poisson model,
which could incorporate not only the ‘fixed effects’ corresponding to information
collected across individuals, but also a random effect due to individuals (110).
Another study looked at using a lifetable analysis to model peritonitis, and showed
that the peritonitis probability curve constructed with only the first episode of
peritonitis was almost identical to that constructed from all episodes of peritonitis
(111). The authors concluded that this finding further supported the random
distribution of peritonitis among patients, and suggested that analyzing the timeto
first peritonitis was an accurate means of expressing the probability of developing
peritonitis.
The majority of studies to date that have looked at predictors of the occurrence of
peritonitis have either performed a peritonitis rate analysis or a timetofirst peritonitis
analysis. While readers of the literature tend to interpret these studies
interchangeably, there are little comparative data to date to suggest whether
peritonitis rate analyses and timetoevent analyses are equivalent as analytic tools in
the study of peritonitis. Two studies have compared modeling strategies. In the first
study of a small number of pediatric patients, a tight correlation was demonstrated
between a peritonitis rate analysis using a negative binomial model and a time to first
peritonitis analysis (112). A second study also showed similar predictors of peritonitis
27
and parameter estimates using a negative binomial model and a timetofirst
peritonitis model (80). It is not known whether modeling peritonitis using a rate
analysis and an AndersenGill model for multiple events would yield similar
predictors, and whether the estimates of risk would be congruent. Demonstration of
such congruency would be relevant to the interpretation of published studies and to
the design of future studies.
28
3. METHODOLOGY
3.1 Data sources
The study included Canadian PD patients for whom data were available through the
Peritonitis Organism Exit sites Tunnel infections (POET) database (Baxter
Healthcare). The POET Clinical Monitoring System is a software program designed
to organize and analyze PD patient data to identify and monitor the causes of
infection, catheter complications, and therapy transfers.
The POET software was offered to all PD centers in Canada that were using Baxter
PD products (representing an estimated 85% of Canadian PD centers). No financial
or other incentive was provided to the individual centers for use of the software.
Installation of the software was documented for 56 Canadian centers. To create the
database, Canadian centers that used POET in a consistent manner to track
infectious and noninfectious complications were asked to contribute their data.
Specifically, centers that reported data for at least 1 year and had cumulative data for
at least 20 adult patients in their program were invited to contribute their deidentified
data. Twenty five centers met these criteria, and were included in the database.
These Canadian centers ranged in size from 48–803 patients per center, and
reported on patients between 1990 and 2005. Data collection was performed by the
PD nurses in the majority of centers.
29
Prior to study initiation, research ethics board approval was obtained from the
University Health Network.
3.2 Patient population
The database included both prospectivelycollected data on incident PD patients as
well as data on prevalent patients that were retrospectively entered into the database
when a given center started using the POET software. In order to distinguish
prospectivelycollected data from retrospectivelycollected data, the 25 Canadian
centers contributing to the database were contacted to determine the exact time
when their center started using the POET software for data collection. In order to
avoid a survivorship bias related to inclusion of prevalent patients and a recall bias
related to retrospective data collection, we included only incident patients in whom
data were collected prospectively. The time period for prospective data collection was
from January 1, 1996 until September 12, 2005.
3.3 Model covariates
Demographic data used in the current study included age, gender, race, diabetic
status, GN as a cause of ESRD, modality before PD start (new to dialysis, transfer
from HD, failed transplant) and PD submodality (CAPD vs. APD). Data available for
the latter variable included the submodality at the time of initiation of PD, and the
30
submodality as of the most recent data entry. Detailed information on all submodality
switches during a patient’s time on PD was not available. As a result, in order to
reduce confounding by modality switching, a secondary analysis was performed after
exclusion of any patient who switched from one PD submodality to another (CAPD to
APD or vice versa).
While the database included extensive comorbidity data, the majority of these
comorbidities (with the exception of diabetes mellitus) appeared to be underreported
when compared with CORR data and were therefore excluded from the analysis (5).
This is not surprising as comorbidities are known to be underreported in
administrative data (113).
Given that the prospective cohort included patients who initiated PD over a 10year
period, we defined two eras of patients in order to assess for an era effect: an earlier
cohort consisting of those who initiated PD between January 1, 1996 and December
31, 2000, and a more contemporary cohort consisting of those who initiated PD
between January 1, 2001 and September 12, 2005.
3.4 Outcomes
The primary outcome was the occurrence of peritonitis, which was defined in two
ways: (1) as a peritonitis rate, and (2) as a timetoperitonitis incorporating all
peritonitis episodes.
31
In order to focus on independent peritonitis events, relapsing or recurrent episodes
were excluded. Standard ISPD definitions were used, with a relapse defined as an
episode occurring within 4 weeks of completion of therapy of a prior infection with
negative culture or the same organism, and a recurrence defined as an episode
occurring within 4 weeks of completion of therapy of a prior infection but with a
different organism (49). While we did not have data on the duration of antibiotic
therapy, it was standard practice over the time period of the study to treat peritonitis
with a minimum of 2 weeks of antibiotics, with some patients being treated with up to
4 weeks of antibiotics. The primary analyses were performed based on the
conservative assumption of 4 weeks of antibiotic therapy, such that all peritonitis
episodes occurring within 60 days of a previous episode were excluded. In order to
ensure that the assumption about the antibiotic duration did not affect the results, we
performed a sensitivity analysis, with repetition of the analyses after exclusion of
peritonitis episodes occurring within 45 days of a prior episode (assuming 2 weeks of
antibiotic therapy).
3.5 Statistical Analyses
Continuous variables were reported as mean ± SD. Two models were used to assess
the predictors of peritonitis. In the first, potential predictors were tested using a
multivariable negative binomial model that modeled the number of peritonitis
episodes per patient, using the duration of follow up as an offset variable. In the
32
second model, peritonitis outcome was reported as the time to each peritonitis event,
and analyzed using an AndersenGill model for ordered multiple events. This model
allowed information on all events to be included with the assumption that each event
was independent. A priori selected variables for inclusion as covariates included age,
gender, race, diabetic status, GN as a cause of ESRD, modality before PD start (new
to dialysis, transfer from HD, failed transplant) and PD modality (CAPD vs. APD).
To assess whether the era of PD initiation influenced the relationship between each
variable and peritonitis, we tested an interaction term between era and each variable
as a method of initial screening. If the interaction was found to be statistically
significant, then subsequent analyses were performed to determine the relationship
between that variable and peritonitis in each of the two eras. Statistical significance
was defined as a p value of <0.05. All statistical analyses were performed using SAS
(version 9.1).
33
4. RESULTS
4.1 Patient cohort
The entire cohort consisted of 6,544 patients, including 4,247 incident patients in
whom data were collected prospectively and 2,297 prevalent patients in whom data
were retrospectively entered. After exclusion of prevalent patients, the study sample
consisted of 4,247 incident PD patients, of whom 1,605 had 3,058 episodes of
peritonitis. The remaining 2,642 patients had no peritonitis episodes. Of the 3,058
peritonitis episodes, 503 were excluded as they occurred within 60 days of a prior
episode and were assumed to be recurrent or relapsing events. Consequently, the
analyses included data for 2,555 peritonitis episodes seen amongst 4,247 patients
with a total of 7,319 years of followup. A flow diagram of the patient cohort included
in the analyses is illustrated in Figure 2. The distribution of peritonitis episodes within
the cohort is shown in Table 3. Eight provinces contributed data to the POET
database, with the largest number of patients coming from Ontario. The distribution of
patients by province is shown in Figure 3.
When all 3,058 peritonitis episodes were counted, the overall peritonitis rate was 1
episode in 26 patientmonths on PD. This decreased to 1 episode in 33 patient
months after exclusion of recurrent or relapsing events. The median time on PD was
1.37 years with an interquartile range of 0.62 to 2.43 years. Of the 4,247 patients
included in the study, 1,445 (34.0%) were still being followed at the end of the data
34
collection period (median followup time 2 years), 18.4% of patients in the cohort died
after a median time on PD of 1.31 years, 27.2% transferred to HD after a median
time on PD of 0.93 years, and 12.2% received a renal transplant after a median time
on PD of 1.21 years.
Demographic characteristics of the patients are presented in Table 4. A comparison
between the Canadian POET cohort and prevalent Canadian PD patients from the
2006 CORR data is shown in Table 5 (1).
4.2 Independent predictors of PD peritonitis
For the analysis in which peritonitis was modeled as a count, the negative binomial
model was used. In the multivariable negative binomial model, variables
independently associated with a higher peritonitis rate included age (rate ratio (RR)
1.04 per decade increase, 95% confidence interval (CI) 1.011.08, p=0.010), Black
race (RR 1.37, 95% CI 1.001.88, p=0.05) and transfer from HD to PD (RR 1.24, 95%
CI 1.11 1.38, p<0.001). Predictors of a lower peritonitis rate included having GN as
the cause of ESRD (RR 0.87, 95% CI 0.751.00, p=0.05) (Table 6). An interaction
between gender and diabetes was identified and is reported in detail below (section
4.3).
The relationship between use of CAPD vs. APD and peritonitis was assessed in the
subset of 3,180 patients who did not switch submodalities during their time on PD
35
(Table 7). In this subgroup, CAPD was not associated with a higher peritonitis rate
than APD (RR 1.03, 95% CI 0.911.16, p=0.66).
Using the multivariable AndersenGill Cox model, variables associated with a shorter
time to peritonitis included age (HR 1.03 per decade increase, 95% CI 1.011.06,
p=0.025), Black race (HR 1.47, 95% CI 1.151.88, p=0.002) and transfer from HD to
PD (HR 1.24, 95% CI 1.131.35, p<0.001). Having GN as the cause of ESRD was
associated with a longer time to peritonitis (HR 0.86, 95% CI 0.760.97, p=0.015)
(Table 8). Among the 3,180 patients with no submodality switch, use of CAPD was
not associated with a shorter time to peritonitis than use of APD (HR 1.02, 95% CI
0.921.13, p=0.69) (Table 9).
4.3 Interactions
In both analyses, a significant interaction between gender and diabetes was seen
(p=0.011 in the negative binomial analysis and p = 0.002 in the AndersenGill model).
When the association between diabetes and peritonitis by gender was assessed, it
was found that female diabetics were at increased risk of peritonitis (RR 1.27, 95% CI
1.101.47, p=0.001), while male diabetics were not (RR 0.99, 95% CI 0.871.13,
p=0.88) (Table 10).
36
4.4 Era effect
Initial screening for an era effect for each of the variables revealed that the only
significant interaction was for the relationship between age and era (p=0.001) (Table
11). Because of the presence of an interaction, the relationship between age and
peritonitis in each era was assessed (Table 12). In this analysis, it was found that the
higher peritonitis risk associated with increasing age in the overall analysis was
entirely accounted for by those initiating dialysis prior to the year 2001 (RR 1.11
between 1996 and 2000, 95% CI 1.061.17, p<0.001), with no relationship between
age and peritonitis thereafter (RR 1.00 between 2001 and 2005, 95% CI 0.951.04,
p=0.83) (114).
4.5 Comparison of peritonitis modeling strategies
The relationship between peritonitis and each of the variables in the multivariable
model was tested in both a negative binomial “rate” model and an AndersenGill time
toevent model. These results are compared in Table 13. The two models yielded
similar predictors of peritonitis, and with comparable estimates of risk.
37
4.6 Sensitivity analysis for peritonitis relapse/recurrence exclusion criteria
For the main analyses, any peritonitis episode occurring within 60 days of a prior
episode was excluded as a recurrent or relapsing episode based on the assumption
of a maximum of 4 weeks of antibiotic therapy. Since some patients were likely to
have received shorter courses of antibiotics, we repeated the analyses after
exclusion of any peritonitis episode occurring within 45 days of a prior episode in
order to exclude any bias introduced by this assumption. In these analyses, the
results did not change appreciably, with identification of similar predictors of
peritonitis and similar risk estimates in both the negative binomial model and the
AndersenGill model (Tables 1417).
38
5. DISCUSSION
5.1 General discussion
Using a large multicentre database of Canadian patients initiating PD between 1996
and 2005, the work contained within this thesis highlights several novel and
confirmatory findings. Predictors of PD peritonitis identified in this study included
Black race, transfer from HD to PD, and being a female diabetic. Increasing age was
only associated with an increased risk of peritonitis among those initiating PD before
the year 2001. In contrast to several prior studies, we found that choice of CAPD vs.
APD did not influence the peritonitis risk. Furthermore, these results were similar
regardless of modeling strategy, suggesting that both rate analyses and timetoevent
analyses are comparable analytic tools for studying the occurrence of PD peritonitis.
Prior to this study using the POET database, the two largest observational studies to
have looked at variables associated with peritonitis utilized the USRDS database (79)
and the ANZDATA registry (80). The former analysis included 11,975 American
patients on PD between 1994 and 1997. Covariates included age, gender, race,
cause of ESRD, comorbidities, PD submodality, number of entryperiod
hospitalization days, entryperiod hematocrit and peritonitis during the entry period.
Unfortunately, as a result of the method of data collection, patients who did not
survive their first 9 months on PD were excluded, as were those with secondarypay
Medicare insurance patients or those insured by health maintenance organizations.
39
Furthermore, peritonitis episodes occurring in the first 3 months on PD were not
captured, nor could the database capture whether a patient had one or more
peritonitis episodes during the 6month entry period. The ANZDATA analysis, which
included data on 3,162 patients from Australia and New Zealand commencing PD
between 1999 and 2003, was more comprehensive in that it was able to capture data
on all new PD starts from the time of PD initiation. Covariates included age, gender,
race, comorbidities, body mass index, timing of nephrology referral and peritoneal
transport status. Similar to the ANZDATA registry, advantages of the POET database
include the multicenter nature of the database, the inclusion of a relatively
contemporary PD cohort and the availability of data from the first day of initiation of
PD.
Among the variables that have been linked to peritonitis, the data on age have been
conflicting (79, 80, 82, 97, 99103). As discussed, several factors may be responsible
for this variability. Firstly, different results may reflect the varying age cutoffs used to
define ‘elderly’ in the studies. Secondly, many of the studies that have looked at the
effect of age on peritonitis have been small, singlecenter studies with limited
statistical power. Thirdly, the era in which the patients received dialysis is quite
variable, with some studies reporting on patients who were on PD in the late 1980s,
and others reporting on more contemporary PD cohorts. The importance of the latter
issue relates to the major advances in PD connectology (24, 4548, 115) and exit site
care (30, 5153) that occurred over this time period. While increasing age was
associated with a higher peritonitis rate in our overall analysis, we have identified an
40
era effect for age, such that increasing age is only associated with peritonitis among
those who initiated PD before the year 2001. The lack of association between
increasing age and peritonitis in recent years may reflect the fact that the “flush
before fill” technique and the use of topical antibacterial agents provide an added
‘safety net’ to contamination in elderly patients who may have impaired vision or
dexterity. Importantly, there was no era effect for any of the other predictor variables,
suggesting that their association with peritonitis is not related to the year in which the
patient initiated PD.
The finding that Black race is associated with a greater risk of peritonitis is consistent
with previous American studies (55, 79). The basis for the increased risk is unclear,
but could relate to genetic differences or to socioeconomic factors that are not
captured in most large databases. While the higher peritonitis rate among African
American patients has contributed to increased technique failure rates (116), the
survival of Black patients on PD remains at least as good as that of Caucasian
patients (116, 117).
The increased peritonitis rate associated with transfer from HD to PD has not been
previously reported. We hypothesize that this increased risk may be attributable to
two high risk groups. The first group includes those who were ‘crash starts’ on HD
with little predialysis care and subsequently chose to transfer to PD. These patients
would likely be sicker, with poorer nutritional status, a greater degree of inflammation,
more rapid loss of RRF and an increased susceptibility to infection and adverse
41
outcomes (118121). The second group includes those who had been on HD for
years and exhausted all vascular access options. For the latter group, the lack of
RRF at the time of transfer to PD may contribute to their peritonitis risk given that loss
of RRF is an independent predictor of peritonitis (82). Since we do not have
information on dialysis vintage prior to transfer, we cannot determine with certainty
which group of patients accounted for the increased peritonitis risk. Nevertheless,
physicians caring for PD patients should be aware of the higher peritonitis rate
among those transferring from HD.
It is not surprising that diabetes has been previously reported to be associated with a
higher peritonitis rate (79, 81, 82) as it is known that diabetic patients with renal
disease are at higher risk of infection in general (35, 83). However, in this study, we
found for the first time a significant interaction between gender and diabetes, such
that the higher peritonitis rate was present only among female diabetics. This is of
particular interest as several large US studies have demonstrated a higher incidence
of death on PD among women, in particular among female diabetic patients (7, 11,
15). In one study using USRDS data, Bloembergen et al noted a differential effect of
gender on PD outcomes, with women at significantly higher risk of death due to
infection than men (11, 122). In a subsequent comparison of PD and HD outcomes
by Vonesh et al, female diabetic patients were one of the few subgroups in which PD
was associated with a higher risk of death than HD (15). Furthermore, Collins et al
reported a higher risk of allcause death for female diabetics ≥ 55 years of age on PD
as compared with HD (7). In causespecific analyses in the latter study, it was found
42
that these patients had a significantly higher risk of infectious death on PD. A smaller
single center study subsequently reported that infection was the second leading
cause of death among older diabetic women on PD (123). Our finding that female
diabetics have the highest peritonitis rates therefore suggests that the higher risk of
infectionrelated death in this group may be mediated in part through a higher risk of
PD peritonitis. While the basis for the increased peritonitis risk among female
diabetics requires further study, loss of RRF may play a role, as it has been shown
that diabetes is associated with a greater decline in RRF (124127). Furthermore,
there is one study demonstrating a more rapid loss of residual kidney function among
female dialysis patients (124), although the data on gender and RRF are conflicting
(124127).
With regard to the lower peritonitis rate among patients with GN as the cause of
ESRD, the results are not surprising. While use of immunosuppressive agents in this
subgroup of patients may increase infection risk (86), these patients tend to be
younger and have fewer comorbidities. Since we were not able to adjust for
comorbidities other than diabetes, the reduced peritonitis risk among patients with
glomerulonephritis is likely due to residual confounding.
While the majority of patients initiate PD either as their initial modality or after transfer
from HD, some patients start PD after failure of their renal transplant. There are data
to suggest that those who return to dialysis after graft loss are at increased risk of
adverse outcomes, including death (87, 128) and peritonitis (8688). Other studies
43
have refuted these findings (89, 90). The theoretical basis for an increased peritonitis
risk includes the use of immunosuppressive therapy, and the long renal disease
vintage in the majority of these patients. While having a failed transplant was not
associated with an increased peritonitis risk in our analysis, this group accounted
only for 3% of PD starts so that this dataset was likely underpowered to detect a
difference, should it have been present.
Several previous studies have addressed the issue of whether the use of CAPD vs.
APD has an effect on peritonitis risk. The majority of studies have found that CAPD is
associated with a higher risk of peritonitis (97, 100, 104), including the only RCT to
have studied the relationship between modality and peritonitis risk (104). However,
there are also observational data to suggest a lower risk of peritonitis on CAPD
relative to APD (79), or no difference between the two submodalities in terms of
peritonitis risk (105). The apparent inconsistency among these studies may relate in
part to the fact that some of the studies in which CAPD was associated with a higher
peritonitis rate included patients who were on PD before the adoption of the improved
PD connectology systems, which greatly reduced the risk of contamination at the
time of an exchange. In our study, which included a larger and more contemporary
cohort of patients than in most of the previous studies, there was no association
between peritonitis and the use of CAPD vs. APD. These data support the generally
accepted practice of having the choice between CAPD and APD guided by patient
preference if the patient is capable of performing both modalities.
44
With regard to the optimal modeling approach to study the occurrence of peritonitis,
there are little comparative data. Most studies have reported either peritonitis rates or
time to first peritonitis. Two studies have compared modeling using a negative
binomial ‘rate’ model and a timetofirst peritonitis analysis, with both demonstrating
similar predictors (80, 112). However, one of the limitations of the time to peritonitis
analyses reported to date is that all studies using this type of modeling have only
incorporated time from initiation of dialysis until the first peritonitis episode. In our
analyses, we have used an AndersenGill model which allows for modeling of time to
peritonitis with the incorporation of all events occurring in each patient. Using this
type of modeling, information on all peritonitis episodes can be included. Based on
the similar results between the rate and timetoevent analyses in our study, we
conclude that both are appropriate analytic methods in the assessment of factors
related to peritonitis.
5.2 Impact on nephrology practice
While peritonitis rates among PD patients have declined over time, the occurrence of
peritonitis remains a major concern for both patients considering PD as well as the
nephrologists looking after them. The inconsistent data on the relationship between
increasing age and peritonitis may be a contributing factor in regard to the concern
over offering PD to older patients (96). The data presented in this thesis suggest that
among a contemporary cohort of PD patients, peritonitis risk does not increase with
45
increasing age and should therefore not be a limiting factor in the selection of PD as
a dialysis modality.
Furthermore, we have shown in this observational study that peritonitis risk is similar
between CAPD and APD. In the absence of strong evidence suggesting a difference
in occurrence of peritonitis between CAPD and APD, peritonitis risk should not
contribute to the decisionmaking when selecting between these submodalities.
Finally, we have for the first time identified diabetes among women and transfer from
HD to PD as predictors of peritonitis. While these do not represent modifiable risk
factors, an awareness of the increased risk in these patients should heighten the
vigilance among the members of treating team.
5.3 Limitations
Our study has several limitations. As with most large datasets, the data have not
been validated against patient charts. Since this was a clinical database, the data
entry was performed by the PD nurses and not trained data collectors. As such, the
accuracy and reproducibility of the data entry cannot be verified. The variables most
likely to be entered accurately include basic demographics such as age, race and
gender, as well as easy to identify comorbidities such as diabetes, and the well
defined outcome of peritonitis. Some comorbidity data such as the presence of
cardiovascular disease or lung disease are more difficult to define, and hence may be
46
less accurately reported or underreported. As a result, for the purpose of our
analyses, we elected to study only those variables that were most likely to have
complete and accurate data entry.
In order to reduce some of the bias inherent in observational data, we studied only a
subpopulation of the entire cohort in the database. Specifically, we chose to include
only those patients in whom data were prospectively entered in order to avoid bias
related to retrospective data entry. We also chose to exclude prevalent patients as
inclusion of these patients in the analysis might have introduced a survivor bias.
With regard to the data source, we cannot exclude the possibility of a selection bias
in that the 25 centers included in the database had to have met the following criteria:
(i) were among the 85% of Canadian centers using Baxter PD products, (ii) agreed to
use the POET software for data collection, and (iii) had consistent data entry for a
minimum of 20 patients. Despite this limitation, the database included centers of
varying sizes in both academic and community hospitals with good representation
from almost all Canadian provinces.
While the multivariable regression models incorporated several potentially important
variables, as with many database studies, the covariates were limited to those for
which data were available. We acknowledge that there are important variables for
which we did not have information. Firstly, we did not have data on biochemical
parameters such as serum albumin and RRF both of which have been linked with
47
peritonitis risk. In addition, we did not have data on dialysis vintage. This would have
been particularly relevant among patients who transferred from HD in order to
distinguish whether the increased peritonitis risk was largely attributed to ‘crash
starts’ on dialysis or long term HD patients. We also did not have information on S.
aureus nasal carriage, or on use of prophylactic ointments that are known to reduce
peritonitis risk. Other important variables that could not be adjusted for included those
that pertain to socioeconomic status, as these might have influenced peritonitis risk.
Finally, while the POET database provided information on race, there was no
separate category for Aboriginal race. This would have been of interest, as this is a
highly prevalent population in several Canadian provinces, with significant differences
in demographics and comorbidities, and increased frequency of technique failure
(129). While large multicenter datasets have the advantage of greater power to
detect clinically meaningful associations, their limited ability to adjust for all potentially
important variables leads to the inevitable possibility of residual confounding.
Since we did not have detailed information on all switches between CAPD and APD
during a patient’s time on PD, we tested the association between the PD modality
and peritonitis by performing the analysis in a subgroup of patients who did not
switch between CAPD and APD during their time on PD. Despite this, the number of
patients in this subgroup was still larger that the majority of studies that have tested
this association. While these data are reassuring, it is important to note that there are
many factors that influence the choice of CAPD vs. APD. While we adjusted for basic
patient demographics and diabetic status, we did not adjust for other comorbidities
48
that may have differed between the patient groups, nor could we adjust for non
medical factors contributing to modality selection. As a result, we cannot exclude the
possibility of residual confounding due to variables that were not included in our
model.
5.4 Conclusions
In conclusion, our study has, for the first time, identified transfer from HD to PD as an
independent risk factor for PD peritonitis. In addition, there was an interaction
between gender and diabetes such that a higher peritonitis risk was only seen among
female diabetics. In contrast to previous studies, the choice of CAPD vs. APD did not
affect the risk of peritonitis. We have also found that age is not a risk factor for
peritonitis in a contemporary cohort of PD patients. Finally, we have demonstrated
that rate analyses and timetoevent analyses are both appropriate analytic tools to
study the occurrence of PD peritonitis.
5.5 Future directions
In light of the newly identified predictors of peritonitis, future studies can be directed
at trying to understand the basis for the increased risk and potential strategies to
reduce risk in these patients. For example, the finding that patients transferring from
HD to PD are at increased risk of peritonitis should be explored further in order to
49
determine whether those at increased risk are ‘crash starts’ on HD or those with a
long dialysis vintage, and whether more extensive training among these patients
might be warranted. With respect to the consistent association between female
diabetics and adverse outcomes, including increased peritonitis risk, future studies
are needed to explore the basis for this increased risk, including whether hormonal
mediators, residual renal function or other factors are responsible for this important
finding. Given the increasing incidence of ESRD among Canadian Aboriginal patients
and their higher risk of PD technique failure, further study is warranted to examine
the risk of PDrelated infectious complications in this population. Finally, the POET
database can also be used to further explore other aspects of PD peritonitis risk,
including the relationship between PD catheter type and peritonitis, and the
microbiology and outcomes of infectious complications.
50
6. ILLUSTRATIONS
6.1 Tables
6.2 Figures
51
Table 1. Summary of studies testing the association between age and peritonitis
AUTHOR
(reference)
DATA
SOURCE
# OF
PTS
ERA Definition
of age
Statistical
analysis
RESULTS
Oo (79) USA
(USRDS)
11,975 19941997 044
4564
6574
≥75
Cox PHM 044: HR 1.09 (1.011.19)
4564: HR 1.01 (0.941.09)
6574: reference group
≥75: 1.07 (0.991.16)
Lim (80) Australia
and New
Zealand
(ANZDATA)
3,162 19992003 024
2544
4564
≥65
NBM
Cox PHM
024: RR 0.9 (0.661.22)
2544: RR 0.83 (0.701.00)
4564: RR 0.88 (0.771.01)
≥65: reference group
Rodriguez Carmona (97)
Spain 328 19891998 Continuous
variable
Rate model (not specified)
Increased risk of 0.005 episodes
per patientyear per year older
Han (82) Korea 204 20002005 Continuous
variable
Cox PHM No effect of age (HR 0.99, 95% CI
0.981.01)
Huang (100) Taiwan 177 19932000 Continuous
variable
Cox PHM No effect of age (HR 1.00, p=0.94)
De Vecchi (99) Italy 156 19851995 ≥70 vs 4060ttest 0.52 vs. 0.37 episodes/patient
year (p<0.002)
Kadambi (101) USA 493 19942000 <50
5064
≥65
ANOVA No difference (p=NS)
Li (103) Hong Kong 328 20002004 ≥65 vs <65 Lifetable
analysis
No difference (p=0.75)
Holley (102) USA 206 19791992 ≥60 vs 1849Poisson
model
No difference (p=NS)
Perez
Contreras (98)
Spain 381 19931996 ≥65 vs <65 ttest 0.72 vs. 0.55 episodes/patient
year (p=0.01)
PHM = proportional hazards model, NBM = negative binomial model, ANOVA =
analysis of variance
52
Table 2. Summary of studies testing the association between CAPD vs. APD and peritonitis
AUTHOR Country # OF PTS
ERA STATISTICAL ANALYSIS
RESULTS
De Fijter (104) Holland 97 19881991 ttest Lifetable analysis
APD better: RR 0.54 (p=0.03) 11 vs. 18 months to first peritonitis (p=0.06)
Oo (79) USA (USRDS)11,975 19941997 Cox PHM CAPD better: HR 0.94 (p=0.008)
Kavanagh (105) Scotland (Scottish Renal Registry)
1,205 19992002 Poisson model No difference (p=0.21)
Rodriguez Carmona (97)
Spain 328 19891998 Rate model (not
specified)
APD better: CAPD associated with excess of 0.2 episodes/patientyear (p=0.008)
Huang (100) Taiwan 177 19932000 Cox PHM APD better: HR 0.58, p=0.05
53
Table 3. Distribution of peritonitis episodes within the patient cohort
Number of peritonitis episodes
Number of patients (total n=4,247)
Percentage of patients
0 2642 62.21% 1 1047 24.65% 2 327 7.70% 3 141 3.32% 4 52 1.22% 5 23 0.54% 6 8 0.19% 7 2 0.05% 8 0 0 9 4 0.09% 10 1 0.02%
54
Table 4. Baseline demographic characteristics for the entire patient cohort
n=4,247
Age (mean, years) 59 ± 16 Gender (% male) 55% Race (%)
Caucasian Black Asian Other
82% 2% 6% 10%
Modality (% on CAPD) Initial Most recent
74% 52%
Modality before PD start (%): New to dialysis Transfer from HD Failed transplant Other/unknown
58% 24% 3% 15%
Cause of ESRD: Diabetes Mellitus Hypertension Glomerulonephritis Cystic kidney disease Other
35% 17% 15% 5% 27%
Diabetic 40%
CAPD = continuous ambulatory peritoneal dialysis, PD = peritoneal dialysis, HD = hemodialysis, ESRD = endstage renal disease
55
Table 5. Comparison of patient demographics between POET and CORR
POET database
Study patients 19962005
CORR data
Prevalent patients 2006
Age (mean, years) 019 2044 4564 6574 75+
1% 19% 38% 24% 17%
1% 15% 39% 25% 20%
Gender (% male) 55% 56% Cause of ESRD:
Diabetes Mellitus Hypertension Glomerulonephritis Cystic kidney disease Other
35% 17% 15% 5% 27%
31% 18% 19% 6% 26%
CORR = Canadian organ replacement register, ESRD = endstage renal disease
56
Table 6. Multivariable negative binomial model for the outcome of peritonitis
n = 4,247 patients NEGATIVE BINOMIAL MODEL
Rate ratio 95% CI p value Age (per decade increase) 1.04 1.011.08 0.010
Black 1.37 1.001.88 0.05
Asian 0.89 0.741.08 0.24
Diabetes female male
1.27 0.99
1.101.47 0.871.13
0.001 0.88
Glomerulonephritis 0.87 0.751.00 0.05
Transfer from HD 1.24 1.111.38 <0.001
Failed transplant 1.27 0.951.69 0.12
HD = hemodialysis, CI = confidence interval
57
Table 7. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch
n = 3,180 patients NEGATIVE BINOMIAL MODEL
Rate ratio 95% CI p value Age (per decade increase) 1.04 1.001.08 0.034
Black 1.26 0.871.82 0.24
Asian 0.88 0.701.10 0.26
Diabetes female male
1.31 0.95
1.111.54 0.811.11
0.001 0.53
Glomerulonephritis 0.90 0.761.07 0.24
Transfer from HD 1.31 1.151.49 <0.001
Failed transplant 1.12 0.771.63 0.57
CAPD vs. APD 1.03 0.911.16 0.66
CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, HD = hemodialysis, CI = confidence interval
58
Table 8. Multivariable AndersenGill model for the outcome of peritonitis
n = 4,247 patients ANDERSENGILL MODEL
Hazard ratio
95% CI p value
Age (per decade) 1.03 1.011.06 0.025
Black 1.47 1.151.88 0.002
Asian 0.91 0.781.06 0.23
Diabetes female male
1.31 1.02
1.171.48 0.911.14
<0.001 0.75
Glomerulonephritis 0.86 0.760.97 0.015
Transfer from HD 1.24 1.131.35 <0.001
Failed transplant 1.18 0.931.49 0.17
HD = hemodialysis, CI = confidence interval
59
Table 9. Multivariable AndersenGill model for the outcome of peritonitis in the subgroup of patients with no submodality switch
n = 3,180 patients ANDERSENGILL MODEL
Hazard ratio
95% CI p value
Age (per decade) 1.03 1.001.06 0.06
Black 1.34 1.001.81 0.05
Asian 0.91 0.751.11 0.36
Diabetes female male
1.37 0.98
1.191.57 0.851.12
<0.001 0.73
Glomerulonephritis 0.88 0.761.02 0.09
Transfer from HD 1.29 1.161.44 <0.001
Failed transplant 0.96 0.701.32 0.81
CAPD vs. APD 1.02 0.921.13 0.69
CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, HD = hemodialysis, CI = confidence interval
60
Table 10. Association between diabetes and peritonitis by gender
n = 4,247 patients NEGATIVE BINOMIAL MODEL ANDERSENGILL MODEL
Rate ratio 95% CI p value Hazard ratio
95% CI p value
Diabetes female male
1.27 0.99
1.101.47 0.871.13
0.001 0.88
1.31 1.02
1.171.48 0.911.14
<0.001 0.75
CI = confidence interval
61
Table 11. Interaction between each variable and era in the multivariable negative binomial model
INTERACTION TERM WITH ERA p value
Age 0.001
Black 0.28
Asian 0.85
Diabetes x Gender 0.58
Glomerulonephritis 0.87
Transfer from HD 0.41
Failed transplant 0.33
*CAPD vs. APD 0.24
*subgroup of 3,180 patients who did not switch between CAPD and APD during their time on PD
HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis
62
Table 12. Association between age and peritonitis by era.
N = 4,247 patients NEGATIVE BINOMIAL MODEL
ANDERSENGILL MODEL
Rate ratio
95% CI p value Hazard ratio
95% CI p value
OVERALL (n=4,247 patients) 1.04 1.011.08 0.010 1.03 1.011.06 0.025
19962000 (n=1,494 patients) 1.11 1.061.17 <0.001 1.08 1.041.23 <0.001 20012005 (n=2,753 patients) 1.00 0.951.04 0.83 0.99 0.951.03 0.61
CI = confidence interval
63
Table 13. Comparison of results of multivariable negative binomial model and AndersenGill model for peritonitis
N = 4,247 patients NEGATIVE BINOMIAL MODEL
ANDERSENGILL MODEL
Rate ratio
95% CI p value Hazard ratio
95% CI p value
Age (per decade) 1.04 1.011.08 0.010 1.03 1.011.06 0.025
Black 1.37 1.001.88 0.05 1.47 1.151.88 0.002
Asian 0.89 0.741.08 0.24 0.91 0.781.06 0.23
Diabetes female male
1.27 0.99
1.101.47 0.871.13
0.001 0.88
1.31 1.02
1.171.48 0.911.14
<0.001 0.75
Glomerulonephritis 0.87 0.751.00 0.05 0.86 0.760.97 0.015
Transfer from HD 1.24 1.111.38 <0.001 1.24 1.131.35 <0.001
Failed transplant 1.27 0.951.69 0.12 1.18 0.931.49 0.17
*CAPD vs. APD 1.03 0.911.16 0.65 1.02 0.921.13 0.69
*subgroup of 3,180 patients who did not switch between CAPD and APD during their time on PD
HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval
64
Table 14. Multivariable negative binomial model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)
n = 4,247 patients NEGATIVE BINOMIAL MODEL
Rate ratio 95% CI p value Age (per decade increase) 1.04 1.011.08 0.009
Black 1.36 0.981.89 0.06
Asian 0.89 0.741.08 0.26
Diabetes female male
1.28 0.98
1.101.48 0.861.12
<0.001 0.77
Glomerulonephritis 0.87 0.751.00 0.05
Transfer from HD 1.25 1.121.40 <0.001
Failed transplant 1.32 0.981.77 0.06
HD = hemodialysis, CI = confidence interval
65
Table 15. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)
n = 3,180 patients NEGATIVE BINOMIAL MODEL
Rate ratio 95% CI p value Age (per decade increase) 1.04 1.001.08 0.040
Black 1.23 0.841.81 0.29
Asian 0.87 0.691.10 0.24
Diabetes female male
1.32 0.94
1.111.56 0.801.10
0.001 0.43
Glomerulonephritis 0.90 0.761.07 0.24
Transfer from HD 1.33 1.171.52 <0.001
Failed transplant 1.17 0.801.70 0.43
CAPD vs. APD 1.02 0.911.15 0.71
HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval
66
Table 16. Multivariable AndersenGill model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)
n = 4,247 patients ANDERSENGILL MODEL
Hazard ratio
95% CI p value
Age (per decade) 1.03 1.011.06 0.020
Black 1.48 1.161.88 0.001
Asian 0.90 0.771.06 0.20
Diabetes female male
1.33 1.01
1.191.49 0.911.13
<0.001 0.80
Glomerulonephritis 0.86 0.770.97 0.015
Transfer from HD 1.24 1.141.36 <0.001
Failed transplant 1.21 0.971.52 0.10
HD = hemodialysis, CI = confidence interval
67
Table 17. Multivariable AndersenGill model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)
n = 3,180 patients ANDERSENGILL MODEL
Hazard ratio
95% CI p value
Age (per decade) 1.03 1.001.06 0.09
Black 1.33 1.001.79 0.05
Asian 0.91 0.761.10 0.33
Diabetes female male
1.37 0.98
1.191.57 0.851.12
<0.001 0.63
Glomerulonephritis 0.88 0.761.01 0.07
Transfer from HD 1.31 1.181.46 <0.001
Failed transplant 0.98 0.721.33 0.91
CAPD vs. APD 1.02 0.931.13 0.69
HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval
68
6.2 Figures
Figure 1. Illustration of intraluminal (dashed arrow) and periluminal (solid arrow) entry of organisms into the peritoneal cavity
Peritoneum
Peritoneal cavity
PD catheter
69
Figure 2. Flow diagram of patient cohort from POET database
2,297 prevalent patients excluded
4,247 incident patients
2,642 patients without peritonitis
6,544 total patients
1,605 patients with peritonitis (3,058 episodes)
2,642 patients without peritonitis
1,605 patients with peritonitis (2,555 episodes)
503 recurrent or relapsing episodes excluded
4,247 patients with 2,555 episodes of peritonitis
70
Figure 3. Distribution of Patients in POET Database by Province
Distribution of Patients in POET Database by Province
Ontario BC Manitoba Nova Scotia Quebec Alberta Saskatchewan Newfoundland
71
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8. APPENDICES
8.1 Appendix A: Peer reviewed publications arising from this work (with permission from Clin J Am Soc Nephrol)
(1) Nessim SJ, Bargman JM, Austin PC, Story K, Jassal SV. Impact of age on peritonitis in peritoneal dialysis patients: an era effect. Clin J Am Soc Nephrol 4(1): 13541, 2009
(2) Nessim SJ, Bargman JM, Austin PC, Nisenbaum R, Jassal SV. Predictors of peritonitis among patients on peritoneal dialysis: results of a large, prospective Canadian database. Clin J Am Soc Nephrol epub ahead of print 2009 Apr 30.