Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context - Chunli Liu; Jie Shi; Fengjuan Wang; Duo Li; Yu Luo; Bofan Yang; Yunlong Zhao; Li Zhang; Dingwei Yang; Heng Jin; Jie Song; Xiaoqin Guo; et al. (EClinicalMedicine)
Summary: Background: Rhabdomyolysis (RM) is a complex clinical syndrome with heterogeneous progression patterns among patients of varying severity. Early and accurate prediction of acute kidney injury (AKI), disease severity, renal replacement therapy (RRT) requirements, and mortality risk is essential for timely identification of high-risk individuals, personalized treatment planning, and optimal allocation of healthcare resources. We aimed to develop and externally validate an interpretable multi-task machine learning (ML) model to predict four clinical outcomes in patients with rhabdomyolysis: AKI, disease severity, the need for RRT, and in-hospital mortality. Methods: We conducted a retrospective study using three data sources: the eICU Collaborative Research Database (eICU-CRD), the Medical Information Mart for Intensive Care IV (MIMIC-IV), and electronic medical records from four tertiary hospitals in China. Data from eICU-CRD and MIMIC-IV were combined to form the derivation cohort for model training and internal validation, while data from the Chinese hospitals served as the external validation cohort. We analyzed 1429 patients from 2008 to 2019 in the derivation cohort and 362 patients from 2016 to 2022 in the external validation cohort. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria, based on serum creatinine levels and urine output. Twenty-two clinical features available within the first 24 h of admission were selected to develop the prediction models. Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). To improve interpretability, feature importance was assessed using the SHapley Additive exPlanation (SHAP) method. Findings: 1429 patients were included in the derivation cohort (69.4% developed AKI, 36.7% were classified as having severe disease, 12.1% required RRT, and 9.8% had in-hospital mortality). 362 patients were included in the external validation cohort (27.9% developed AKI, 25.7% had severe disease, 27.3% required RRT, and 4.1% had in-hospital mortality). Among all evaluated models, the random forest (RF) algorithm exhibited the highest overall discriminative performance across the four prediction tasks. Based on feature importance rankings, interpretable final models were developed for each task using the top five contributing features. These models demonstrated robust predictive accuracy for AKI, disease severity, RRT requirements, and in-hospital mortality, with AUCs and corresponding 95% confidence intervals (CIs) of 0.914 (0.875–0.944), 0.909 (0.869–0.940), 0.888 (0.844–0.921), and 0.823 (0.773–0.865) in the internal validation cohort, and 0.906 (0.871–0.934), 0.856 (0.815–0.890), 0.852 (0.811–0.887), and 0.832 (0.789–0.869) in the external validation cohort, respectively. To support clinical implementation, a web- and Android-based decision support system was developed and is currently undergoing pilot testing in multiple hospitals. Interpretation: We developed and validated an interpretable multi-task ML model capable of accurately predicting key clinical outcomes in patients with RM. To improve clinical applicability, a user-friendly decision support system was implemented, incorporating interactive features to support frontline healthcare providers in real-time risk stratification and individualized management of RM. Funding: National Key Research and Development Program of China (Nos. 2021YFC3002202 and 2023YFF1204104).