Machine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score

Abstract

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Despite the introduction of mechanical circulatory support (MCS), short-term mortality rates in cardiogenic shock (CS) remain high. Prior CS risk scores have limited reliability because of selected derivation cohorts or inadequate validation. Purpose The aim of this study is to develop and validate externally the risk prediction model for in-hospital mortality in CS patients using machine learning (ML) algorithms. Methods 1247 patients of all-cause CS from the RESCUE registry (Retrospective and Prospective Observational Study to Investigate Clinical Outcomes and Efficacy of Left Ventricular Assist Device for Korean Patients with CS) between January 2014 and December 2018 were analyzed. Patients were randomly divided into two groups for training and testing at a ratio of 7 to 3. Risk prediction models were estimated with the logistic regression analysis and four ML algorithms: Least absolute shrinkage and selection operator analysis (LASSO), Support vector machine (SVM), Extreme Gradient Boosting (XGB), and Random Forest classifier. Internal validation with the RESCUE registry and external validation with the single center CS registry including 678 patients were performed. Results All four ML models made an agreement with the seven important variables associated with mortality as follows; age, vasoactive inotropic score, left ventricular ejection fraction, lactic acid, cardiac arrest at presentation, requiring continuous renal replacement therapy, and mechanical ventilator. The logistic regression analysis was performed with the risk factors which were derived by ML models. The logistic regression model named RESCUE score showed the predictive performance with an area under the curve (AUC) of 0.86 [95% CI 0.83-0.88] for in-hospital mortality. External validation showed excellent discrimination with an AUC of 0.80 [95% CI 0.76-0.84]. Decision curve analysis indicated that the nomogram conferred high clinical net benefit. Conclusion The RESCUE score using advanced machine learning techniques has excellent predictive performance for the in-hospital mortality of CS regardless of the underlying cause. This model may be useful as a tool to estimate the risk stratification of CS patients.