This study explored infection risk factors within one month post-kidney transplantation (KT) and developed a clinical prediction model. We retrospectively analyzed clinical data from KT patients treated at our hospital (January 2015-December 2024). Patients were categorized into infection or control groups based on 1-month postoperative infection status. Infection incidence and risk factors were analyzed, and a multivariate logistic regression model was developed. The receiver operating characteristic (ROC) curves and a nomogram were generated. Patients were randomly split 7:3 into training and validation sets to assess model performance. A total of 410 patients were included in this study, of whom 131 had postoperative infection, with an incidence rate of 31.95%. Multivariate logistic regression analysis showed that history of smoking (odds ratio (OR) = 2.96, 95% confidence interval (CI) (1.20-7.27)), drainage tube indwelling time (OR = 1.41, 95% CI (1.17-1.71)), catheter indwelling time (OR = 1.66, 95% CI (1.36-2.03)) and albumin (ALB) (OR = 0.78, 95% CI (0.71-0.86)) and haemoglobin (HGB) (OR = 0.70, 95% CI (0.59-0.83)) levels were independent risk factors for early infection after KT (p < 0.05). The area under the ROC curve of the training set was 0.954 (95% CI (0.925-0.982)), the specificity was 0.855 and the sensitivity was 0.896. In the validation set, the area under the ROC curve was 0.914 (95% CI (0.861-0.967)), the specificity was 0.832 and the sensitivity was 0.903. The Hosmer-Lemeshow goodness-of-fit test of the model showed that the training set χ2 = 6.962 (p = 1.000) and the validation set χ2 = 8.813 (p = 0.450). Multivariate risk factors were used to construct a nomogram model, and the calibration curve was consistent with the ideal curve, suggesting that the model had good stability. The clinical decision curve showed that it had good clinical value. History of smoking, drainage tube indwelling time, catheter indwelling time and ALB and HGB levels are the risk factors of infection after KT. The model based on these factors can effectively predict the occurrence of infection after KT.
Xu et al. (Wed,) studied this question.