Predictors of peritonitis in patients on peritoneal dialysis: results of a large, prospective Canadian database.
OBJECTIVE
Despite the decreasing incidence of peritonitis among peritoneal dialysis (PD) patients over time, its occurrence is still associated with significant morbidity and mortality. Determining factors that are associated with PD peritonitis may facilitate the identification of patients who are at risk.


METHODS
Using data collected in the multicenter Baxter POET database between 1996 and 2005, the study population included incident Canadian PD patients. Potential predictors of peritonitis were sought using a negative binomial model and an Andersen-Gill model. Study variables included age, gender, race, cause of renal disease, diabetes status, transfer from hemodialysis (HD), previous renal transplant, and continuous ambulatory PD (CAPD) versus automated PD (APD).


RESULTS
Data were available for 4247 incident PD patients, including 1605 patients with a total of 2555 peritonitis episodes. Using the negative binomial regression model, factors that were independently associated with a higher peritonitis rate included age, Black race, and having transferred from HD. There was an interaction between gender and diabetes, with an increased risk for peritonitis among female patients with diabetes. The use of CAPD versus APD did not affect the peritonitis rate. The Andersen-Gill model for recurrent events yielded similar results.


CONCLUSIONS
Predictors of PD peritonitis included Black race, transferring from HD to PD, and diabetes among women. In contrast to previous findings, CAPD and APD were similar with regard to peritonitis risk.
Nessim
Sharon J
SJ
Department of Medicine, Division of Nephrology, St. Michael's Hospital, Toronto, Ontario, Canada. nessims@smh.toronto.on.ca
Bargman
Joanne M
JM
Austin
Peter C
PC
Nisenbaum
Rosane
R
Jassal
Sarbjit V
SV
D000328:Adult; D000368:Aged; D044383:Black People / Q000706:statistics & numerical data; D002170:Canada / Q000453:epidemiology; D016208:Databases, Factual; D048909:Diabetes Complications / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D007676:Kidney Failure, Chronic / Q000453:epidemiology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010530:Peritoneal Dialysis / Q000009:adverse effects* / Q000706:statistics & numerical data*; D010538:Peritonitis / Q000175:diagnosis / Q000453:epidemiology*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D006435:Renal Dialysis / Q000706:statistics & numerical data; D012307:Risk Factors; D017678:Sex Distribution