Development and Validation of Nomogram for Predicting the Risk of Community-acquired Pneumonia after Kidney Transplantation of Deceased Donors

Background: Community-acquired pneumonia (CAP) is one of the main complications associated with kidney transplantation recipient (KTR). In order to retrospectively analyze KT cases from deceased donors (DD), and construct a nomogram that could effectively assess the risk of CAP. Methods: We employed logistic regression and the least absolute shrinkage and selection operator (LASSO) to identify predictors from 238 cases collected at Department of Urology, The First Affiliated Hospital of Anhui Medical University, between January 1, 2018, and May 31, 2023. The dataset comprised 6 demographic and 18 clinical indicators, which were used for training and validation. A nomogram was constructed using these predictors, and its effectiveness was evaluated through receiver operating characteristic (ROC) analysis, calibration curves, and clinical decision analysis. Internal validation further confirmed the model's predictive accuracy.Results: The predictive factors screened in terms of demographic data included recipient/donor age and body mass index. The clinical data screened eight predictors to obtain ‘RiskScore’. The Area under Curve value for the nomogram constructed using the aforementioned predictors was recorded to be 0.779. The calibration curve showed that the model exhibited better predictive performance. In particular, DCA showed that in cases where the probability for the prediction of CAP was 0.13–0.61, the clinical intervention was recommended. The internally verified data established a better predictive ability of the model. Conclusion: The study constructed an effective and concise prediction model based on clinical data, providing an important reference baseline for the prevention of CAP in KTR.