Development and validation of a predictive model for invasive ventilation risk within 48 hours of admission in patients with early sepsis-associated acute kidney injury.

Objective: To identify patients with early sepsis-associated acute kidney injury (SA-AKI) at high risk of requiring invasive ventilation within 48 h of admission, facilitating timely interventions to improve prognosis. Methods: This retrospective study included patients with early SA-AKI admitted to Dongyang People's Hospital between January 2011 and October 2024 and Yiwu Tianxiang Dongfang Hospital between January 2016 and December 2024. Variables included age, blood parameters, and vital signs at admission. Patients were divided into training and validation cohorts. Independent risk factors were identified in the training cohort, and a nomogram was developed. The discriminatory ability was assessed using the area under the receiver operating characteristic curves (AUC). Calibration was assessed using GiViTI calibration plots, while clinical utility was evaluated via decision curve analysis (DCA). Validation was performed in the internal and external validation groups. Additional models based on Sequential Organ Failure Assessment (SOFA) and National Early Warning Score (NEWS) scores, machine learning models including Support Vector Machine (SVM), C5.0, Extreme Gradient Boosting (XGBoost), and an ensemble model were compared with the nomogram on the discrimination power using DeLong's test. Results: The key independent risk factors for invasive ventilation in patients with early SA-AKI included lactate, pro-BNP, albumin, peripheral oxygen saturation, and pulmonary infection. The nomogram demonstrated an AUC of 0.857 in the training cohort (Hosmer-Lemeshow Conclusion: The nomogram developed in this study is capable of accurately predicting the risk of invasive ventilation in SA-AKI patients within 48 h of admission, offering a valuable tool for early clinical decision-making.