A simplified lactate-vasopressor-SpO2 machine learning model demonstrated superior discrimination for predicting in-hospital death in SIMI patients compared to the SOFA score (AUC 0.727 vs 0.623).
Observational (n=7,309)
Does a lactate-based machine learning model improve the prediction of in-hospital death in critically ill patients with sepsis-induced myocardial injury compared to the SOFA score?
A simplified machine learning model incorporating lactate, vasopressor use, and SpO2 offers superior predictive accuracy for in-hospital mortality in sepsis-induced myocardial injury compared to the traditional SOFA score.
Absolute Event Rate: 0.767% vs 0.623%
Background To obtain the essential factors and construct an interpretable model for predicting in‐hospital death in critically ill patients with sepsis‐induced myocardial injury (SIMI) using machine learning (ML) methods. Methods This retrospective study collected data from the MIMIC‐IV database. We employed random forest (RF), XG Boost, and logistic regression (LR) algorithms to rank variable importance, identifying lactate as a core factor. Next, the association of lactate with in‐hospital death in SIMI was analyzed using LR analysis. A comprehensive lactate‐based LR model was subsequently constructed and interpreted by the Shapley additive explanation (SHAP). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), integrated discrimination improvement (IDI), and calibration plots. Internal validation was performed to assess the model’s generalizability. Results Among 7309 enrolled patients, 1526 experienced in‐hospital death. Lactate elevation was independently associated with increased in‐hospital mortality in SIMI patients ( p < 0.05). The SHAP analysis indicated that lactate, vasopressor use, and SpO 2 were the top three contributors to predictions in the lactate‐based model. Both the comprehensive lactate‐based model and the simplified lactate–vasopressor–SpO 2 model demonstrated superior discriminative ability (AUCs: 0.767 and 0.727, respectively), compared to the Sequential Organ Failure Assessment (SOFA) score (AUC: 0.623), with consistently greater clinical net benefit and improved reclassification. All models showed good calibration. Conclusion Lactate, as the key factor, was positively associated with in‐hospital death in SIMI. The simplified lactate–vasopressor–SpO 2 model offers an optimal balance of predictive accuracy, interpretability, and clinical practicality for risk stratification.
Liu et al. (Thu,) conducted a observational in Sepsis-induced myocardial injury (SIMI) (n=7,309). Lactate-based machine learning models vs. Sequential Organ Failure Assessment (SOFA) score was evaluated on In-hospital death prediction (AUC). A simplified lactate-vasopressor-SpO2 machine learning model demonstrated superior discrimination for predicting in-hospital death in SIMI patients compared to the SOFA score (AUC 0.727 vs 0.623).