A LASSO-derived risk model for long-term mortality in Chinese patients with acute coronary syndrome

Abstract

Abstract Background The formal risk assessment is essential in the management of acute coronary syndrome (ACS). In this study, we develop a risk model for the prediction of 3-year mortality for Chinese ACS patients with machine learning algorithms. Methods A total of 2174 consecutive patients who underwent angiography with ACS were enrolled. The missing data among baseline characteristics were imputed using the MissForest algorithm based on random forest method. In model development, a least absolute shrinkage and selection operator (LASSO) derived Cox regression with internal tenfold cross-validation was used to identify the predictors for 3-year mortality. The clinical performance was assessed with decision curve analysis. Results The average follow-up period was 27.82 ± 13.73 months; during the 3 years of follow up, 193 patients died (mortality rate 8.88%). The Kaplan–Meier estimate of 3-year mortality was 0.91 (95% confidence interval (CI): 0.890.92). After feature selection, 6 predictors were identified: Age,” “Creatinine,” “Hemoglobin,” “Platelets,” “aspartate transaminase (AST)” and “left ventricular ejection fraction (LVEF)”. At tenfold internal validation, our risk model performed well in both discrimination (area under curve (AUC) of receiver operating characteristic (ROC) analysis was 0.768) and calibration (calibration slope was approximately 0.711). As a comparison, the AUC and calibration slope were 0.701 and 0.203 in Global Registry of Acute Coronary Events (GRACE) risk score, respectively. Additionally, the highest net benefit of our model within the entire range of threshold probability for clinical intervention by decision curve analysis demonstrated the superiority of it in daily practice. Conclusion Our study developed a prediction model for 3-year morality in Chinese ACS patients. The methods of missing data imputation and model derivation base on machine learning algorithms improved the ability of prediction. . Trial registration ChiCTR, ChiCTR-OOC-17010433. Registered 17 February 2017–Retrospectively registered