A predictive model of treatment effectiveness of refractory peritoneal dialysis-related peritonitis in patients with peritoneal dialysis: a single-center observational study in South China

Abstract

Abstract Background To prevent loss of peritoneal function caused by persistent abdominal inflammation, the guidelines recommend early extubation in patients with refractory peritoneal dialysis-associated peritonitis (rPDAP). In attempt to pinpoint high-risk patient cohorts that did not respond to treatment for refractory peritonitis, we created a model to predict the effectiveness of peritonitis treatment. Methods This observational cohort study included peritoneal dialysis (PD) patients from January 1, 2011 to December 31, 2020. Multivariate logistic regression analysis was used to explore the factors affecting the occurrence and prognosis of rPDAP, and to construct a predictive model for the success of rPDAP treatment. Receiver operator characteristic (ROC) curve, calibration and decision curve were drawn to evaluate the predictive performance of the model. Results A total of 1, 397 cases of peritoneal dialysis-associated peritonitis (PDAP) occurred in our center during the study period, of which 558 cases were diagnosed as rPDAP. The incidence of refractory peritonitis was 0.047 cases/patient-year. In the study, 440 cases with rPDAP were included. Among them 304 cases (69.1%) had been successfully cured, while 136 cases (30.9%) were treatment failure, of which 19 cases (4.3%) died, 85 (19.3%) cases transferred to hemodialysis and 32 cases (7.2%) were relapse/recurrent peritonitis. Dialysate culture results showed 132 (30.0%) cases were infected with gram positive bacteria, 161 (36.6%) gram negative bacteria. Multivariate Logistic regression analysis showed that episodes of peritonitis previously ≤ 3 times were correlated with the better prognosis of rPDAP, but white blood cell (WBC) counts in peritoneal dialysate on the 3rd day of peritonitis or WBC counts on the 5th day ≥ 300 × 106/L, the pathogenic microorganism with gram-negative bacteria, as well as longer duration of PD were associated with poor outcomes. The C-statistical value of the training data set was 0.870 (95%CI: 0.821–0.918). The calibration curve and clinical decision-making curve also proved that this nomogram could accurately predict the success of treatment in patients with refractory peritonitis. Conclusion The nomogram model created through internal verification indicated a strong clinical application value and a high prognostic prediction accuracy for rPDAP.