L Meng1, X Zhu2, L Yang3, X Li1, S Cheng1, S Guo1, X Zhuang1, H Zou1, W Cui1. 1. Department of Nephrology, Second Hospital of Jilin University, Changchun 130041, China. 2. Department of Nephrology, Second Division of First Hospital of Jilin University, Changchun 130031, China. 3. Department of Nephrology, Jilin Central Hospital, Changchun 132011, China.
Abstract
OBJECTIVE: To develop and validate a risk prediction model of treatment failure in patients with peritoneal dialysis-associated peritonitis (PDAP). METHODS: We retrospectively analyzed the data of patients undergoing peritoneal dialysis (PD) in 3 dialysis centers in Jilin Province who developed PDAP between January 1, 2013 and December 31, 2019. The data collected from the Second Hospital of Jilin University and Second Division of First Hospital of Jilin University) were used as the training dataset and those from Jilin Central Hospital as the validation dataset. We developed a nomogram for predicting treatment failure using a logistic regression model with backward elimination. The performance of the nomogram was assessed by analyzing the C-statistic and the calibration plots. We also plotted decision curves to evaluate the clinical efficacy of the nomogram. RESULTS: A total of 977 episodes of PDAP were included in the analysis (625 episodes in the training dataset and 352 episodes in the validation dataset). During follow-up, 78 treatment failures occurred in the training dataset and 35 in the validation dataset. A multivariable logistic regression prediction model was established, and the predictors in the final nomogram model included serum albumin, peritoneal dialysate white cell count on day 5, PD duration, and type of causative organisms. The nomogram showed a good performance in predicting treatment failure, with a C-statistic of 0.827 (95% CI: 0.784-0.871) in the training dataset and of 0.825 (95% CI: 0.743-0.908) in the validation dataset. The nomogram also performed well in calibration in both the training and validation datasets. CONCLUSION: The established nomogram has a good accuracy in estimating the risk of treatment failure in PDAP patients.
OBJECTIVE: To develop and validate a risk prediction model of treatment failure in patients with peritoneal dialysis-associated peritonitis (PDAP). METHODS: We retrospectively analyzed the data of patients undergoing peritoneal dialysis (PD) in 3 dialysis centers in Jilin Province who developed PDAP between January 1, 2013 and December 31, 2019. The data collected from the Second Hospital of Jilin University and Second Division of First Hospital of Jilin University) were used as the training dataset and those from Jilin Central Hospital as the validation dataset. We developed a nomogram for predicting treatment failure using a logistic regression model with backward elimination. The performance of the nomogram was assessed by analyzing the C-statistic and the calibration plots. We also plotted decision curves to evaluate the clinical efficacy of the nomogram. RESULTS: A total of 977 episodes of PDAP were included in the analysis (625 episodes in the training dataset and 352 episodes in the validation dataset). During follow-up, 78 treatment failures occurred in the training dataset and 35 in the validation dataset. A multivariable logistic regression prediction model was established, and the predictors in the final nomogram model included serum albumin, peritoneal dialysate white cell count on day 5, PD duration, and type of causative organisms. The nomogram showed a good performance in predicting treatment failure, with a C-statistic of 0.827 (95% CI: 0.784-0.871) in the training dataset and of 0.825 (95% CI: 0.743-0.908) in the validation dataset. The nomogram also performed well in calibration in both the training and validation datasets. CONCLUSION: The established nomogram has a good accuracy in estimating the risk of treatment failure in PDAP patients.
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