Establishment of a prediction model for long-term infections in patients undergoing peritoneal dialysis
Background and ObjectivePeritoneal dialysis (PD)-associated infections are the primary contributors to PD technique failure and patient mortality. Given these reasons, this study aims to identify independent risk factors for long-term infections in PD individuals and to construct an effective clinic...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1596403/full |
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| Summary: | Background and ObjectivePeritoneal dialysis (PD)-associated infections are the primary contributors to PD technique failure and patient mortality. Given these reasons, this study aims to identify independent risk factors for long-term infections in PD individuals and to construct an effective clinical prediction model using multivariate analysis.MethodsThis study retrospectively analyzed 214 participants with ESRD who underwent PD catheterization at Wuxi No. 2 People’s Hospital. Based on whether they developed infections or not after 3 months treatment of regular peritoneal dialysis, all patients were categorized into two cohorts: infected (n = 67) and non-infected (n = 147). A comparison of clinical indicators was made between the two cohorts, and independent risk factors were initially determined through the means of univariate and multivariate logistic regression analyses for infections in PD patients. Via R software, we constructed a nomogram prediction model, its performance was validated.ResultsAge (p = 0.004), surgical incision length (p = 0.018), and Prognostic Nutritional Index (PNI, p < 0.001) were identified as independent risk factors for long-term infections in PD patients. Based on the three significant predictors, we constructed a nomogram model, of which predictive performance was assessed through analysis of the ROC curve, which revealed area under the curve (AUC) values of 0.807, demonstrating good discriminative ability of the prediction model for long-term infection risk in PD patients.ConclusionAdvanced age, lower PNI, and longer surgical incision length are closely linked to the occurrence of infections in PD individuals. The nomogram model which was based on this study showed high efficacy in predicting long-term infections and can serve as a reference to recognize individuals with a high likelihood of complications for medical caregivers as early as possible. |
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| ISSN: | 2296-858X |