101 machine learning algorithms for predicting mortality in critically ill patients with spinal cord injury and sarcopenia: a multi-database study

Critically ill patients with spinal cord injury (SCI) and those at high risk for sarcopenia represent vulnerable populations with heightened mortality risks. Traditional prognostic scores often fail to capture the unique pathophysiology of these patients. This study aimed to develop and validate machine learning-based models using easily obtainable clinical features to stratify mortality risk for patients with SCI, high-risk sarcopenia, or both conditions. This retrospective observational study utilized data from the MIMIC-IV (training set; n = 7904) and eICU-CRD (validation set) databases. Patient cohorts were identified based on diagnostic codes for spinal cord injury and high-risk features for sarcopenia, as per established expert consensus. A comprehensive set of clinical variables was extracted, including demographics, comorbidities, ICU severity scores, and laboratory parameters. The primary outcome was all-cause mortality. To construct a prognostic model, we employed an integrated computational approach encompassing ten machine learning algorithms, generating 101 predictive models. The optimal model was selected based on the highest Harrell’s C-index under 10-fold cross-validation. Patients were stratified into high- and low-risk groups using the model-derived risk score. Predictive performance was assessed using the C-index, with survival differences between risk groups validated by Kaplan-Meier analysis. The final model was presented as an interactive nomogram, and its clinical utility was evaluated using calibration curves, decision curve analysis, and time-dependent receiver operating characteristic curves. Statistical analyses included Cox regression and subgroup analyses to ensure the robustness of the findings. A total of 1547 patients with SCI and 4981 patients at high risk of sarcopenia were included. Sarcopenia was significantly associated with increased mortality among SCI patients. Across all cohorts, eight overlapping prognostic features—age, INR, PT, PTT, anion gap, hematocrit, hemoglobin, and platelet count—were identified as key determinants of survival. The RSF model demonstrated robust discrimination in the training cohort (C-index range: 0.88–0.91) and acceptable performance in the external validation cohort (C-index range: 0.62–0.70). Patients stratified into high-risk groups consistently exhibited significantly worse survival outcomes. Each 1-SD increase in the standardized risk score was strongly associated with mortality in both databases. Compared with the Charlson Comorbidity Index, OASIS, and Glasgow Coma Scale, the proposed risk score achieved significantly superior predictive performance, with marked improvements in NRI and IDI. Nomograms derived from Cox regression showed excellent calibration, strong discrimination, and favorable clinical net benefit across all three patient cohorts. We developed and externally validated robust, interpretable machine learning–based prognostic signatures for mortality prediction in patients with SCI, high-risk sarcopenia, and their co-occurrence. The proposed risk score and nomograms outperform traditional clinical scoring systems and provide reliable tools for individualized risk stratification and clinical decision-making in critically ill populations.