Predictive models on patients’ eligibility for peritoneal dialysis

Background Peritoneal dialysis (PD) is being promoted because it is cost-effective and has equivalent outcomes to facility-based hemodialysis (HD). Determining PD eligibility is critical but subjective, with high variability among renal programs. This study aimed to establish a predictive model for PD eligibility among individuals who started treatment with HD. A secondary objective was to identify predictors of PD eligibility and determine if eligible patients went on to receive PD. Methods This retrospective cohort study included individuals starting HD at multiple hospitals in Alberta, Canada, as part of the START program between 1 October 2016 and 31 March 2018. Twenty-seven predictors, including patient characteristics, laboratory values, and comorbidities, were considered in logistic regression modeling. The outcome variable was PD eligibility, as determined by a standardized interdisciplinary assessment. The model selection was based on the Akaike information criterion. The confusion matrix was used for each model to compare the predicted versus observed eligibility. The final model was calibrated and presented. Results Among the 598 participants, 391 (65.4%) were considered eligible for PD. The logistic regression model achieved a modest performance in discriminating patients who were eligible for PD, with a high sensitivity of 91.3%, an accuracy of 0.68 (95% CI, 0.65–0.72), and an area under the receiver operating characteristic curve ranging from 0.69 to 0.71. Age (OR = 0.98; 95% CI, 0.97–0.99), body mass index (OR = 0.95; 95% CI, 0.93–0.97), starting dialysis in intensive care unit (OR = 0.53; 95% CI, 0.31–0.92), and polycystic kidney disease (OR = 0.37; 95% CI, 0.13–0.99) were statistically significant factors associated with a lower likelihood of being considered eligible for PD. Out of the 391 eligible PD patients, 87 (22.3%) received PD treatment within 6 months of starting HD. Conclusions The majority of patients starting HD were considered eligible for PD. Our model exhibits a high level of sensitivity and could serve as a valuable tool for screening potential candidates following the commencement of HD.