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Methodological issues in assessing the incidence of peritoneal dialysis-associated peritonitis in children
Franz Schaefer1, Marianne Kandert, Reinhard Feneberg
1Division of Pediatric Nephrology, Children's Hospital, Ruperto-Carolus University, Heidelberg, Germany. Franz_Schaefer@med.uni-heidelberg.de
Insights
Peritonitis incidence in children on automated peritoneal dialysis (PD) shows a bimodal distribution, with some experiencing few episodes and others around one per 6-9 months. Median patient-specific rates or negative binomial modeling better express peritonitis risk in pediatric PD patients.
Area of Science:
- Pediatric Nephrology
- Peritoneal Dialysis
- Infectious Disease Epidemiology
Background:
- Peritonitis is a significant complication in children undergoing automated peritoneal dialysis (PD).
- Understanding the incidence and distribution of peritonitis is crucial for optimizing treatment and patient outcomes.
- Previous studies have not fully characterized patient-specific peritonitis rates in pediatric populations.
Purpose of the Study:
- To evaluate the incidence distribution of peritonitis in pediatric automated PD patients.
- To assess the utility of patient-specific peritonitis rates for risk assessment and analysis.
- To determine the most accurate methods for expressing average peritonitis risk in this cohort.
Main Methods:
- A single-center, 2-year observational study of 49 children (aged 2 months to 18 years) on automated PD.
- Analysis included cohort-specific incidence, median patient-specific incidence, and mean incidence using negative binomial modeling.
- Peritonitis-free survival was assessed using Kaplan-Meier analysis.
Main Results:
- A total of 68 new peritonitis episodes and 21 relapses occurred in 27 patients.
- Patient-specific peritonitis incidence exhibited a bimodal distribution: many patients had few episodes, while a cluster experienced approximately 1 episode per 6-9 months.
- Median peritonitis-free survival was 6.9 months; incidence correlated inversely with patient age and PD duration.
Conclusions:
- The non-Gaussian distribution of peritonitis in children necessitates using median patient-specific rates or negative binomial modeling for accurate risk assessment.
- Assigning personal peritonitis risk facilitates risk factor analysis in pediatric PD cohorts.
- These findings aid in tailoring management strategies for pediatric patients on automated PD.
Objectives:
To evaluate the distribution of peritonitis incidence and assess the usefulness of patient-specific peritonitis rates in children.
Design:
49 children on automated peritoneal dialysis (PD) followed during a 2-year observation period.
Setting:
Single-center, academic children's hospital.
Patients:
49 children aged 2 months to 18 years; 24 prevalent, 25 incident during the observation period. Cumulative observation time was 639 patient-months.
Main Outcome Measures:
Cohort-specific peritonitis incidence, median patient-specific peritonitis incidence, mean peritonitis incidence by gamma-Poisson (negative binomial) modeling, peritonitis-free survival by Kaplan-Meier life-table analysis.
Results:
68 new peritonitis episodes and 21 relapses occurred in 27 patients. The distribution of patient-specific peritonitis incidence was bimodal, with a large group experiencing no or very few episodes, and another cluster around 1 episode per 6-9 months. Overall cohort-specific peritonitis incidence was 1.28, median subject-specific incidence 0.99, and mean incidence according to negative binomial modeling 1.04 (95% confidence interval 1.02-1.06) episodes per patient-year. Median peritonitis-free survival time was 6.9 months. In those patients who developed peritonitis, subject-specific peritonitis incidence was inversely correlated with patient age (r = -0.42, p < 0.05) and duration of chronic PD at last observation (r = -0.42, p < 0.05).
Conclusions:
Since the distribution of peritonitis in children is non-Gaussian, the average risk of peritonitis is more accurately expressed by the median of the individual subject-specific peritonitis rates or by the mean incidence estimate obtained by the negative binomial distribution model. The assignment of a personal peritonitis risk to each patient permits risk factor analysis by routine statistical methods, even in smaller populations.