Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective cohort study based on the MIMIC-IV database - Zhao, Ke; Ren, Qian; Chen, Lin; Zhang, Ying; Wang, Guolin (Frontiers in Cellular and Infection Microbiology, 2024)
BackgroundSAKI is a common and serious complication of sepsis, contributing significantly to high morbidity and mortality, especially in patients requiring RRT. Early identification of high-risk patients enables timely interventions and improvement in clinical outcomes. The objective of this study was to develop and validate a predictive model for in-hospital mortality in patients with SAKI receiving RRT.MethodsPatients with SAKI receiving RRT from the MIMIC-IV database were retrospectively enrolled and randomly assigned to either the training cohort or the testing cohort in a 7:3 ratio. LASSO regression and Boruta algorithm were utilized for feature selection. Subsequently, three machine learning models—CART, SVM and LR—were constructed, and their predictive efficacy was assessed using a comprehensive set of performance indicators. Feature importance analysis was performed to determine the contribution of each feature to a model’s predictions. Finally, DCA was employed to evaluate the clinical utility of the prediction models. Additionally, a clinical nomogram was developed to facilitate the interpretation and visualization of the LR model.ResultsA total of 1663 adults were ultimately enrolled and randomly allocated into the training cohort (n = 1164) or the testing cohort (n = 499). Twenty-eight variables were evaluated for feature selection, with eight ultimately retained in the final model: age, MAP, RR, lactate, Cr, PT-INR, TBIL and CVP. The LR model demonstrated commendable performance, exhibiting robust discrimination in both the training cohort (AUROC: 0.73 (95% CI 0.70–0.76); AUPRC: 0.75 (95% CI 0.72–0.79); accuracy: 0.66 (95% CI 0.63–0.68)) and the testing cohort (AUROC: 0.72 (95% CI 0.68-0.76); AUPRC: 0.73 (95% CI 0.67–0.79); accuracy: 0.65 (95% CI 0.61–0.69)). Furthermore, there was good concordance between predicted and observed values in both the training cohort (χ2 = 4.41, p = 0.82) and the testing cohort (χ2 = 4.16, p = 0.84). The results of the DCA revealed that the LR model provided a greater net benefit compared to other prediction models.ConclusionsThe LR model exhibited superior performance in predicting in-hospital mortality in patients with SAKI receiving RRT, suggesting its potential utility in identifying high-risk patients and guiding clinical decision-making.