Sex-specific models to predict 5-year mortality after ST-elevation myocardial infarction using machine learning: insight from FAST-MI registry

Abstract

Abstract Background Most prognostic stratification tools in acute myocardial infarction (AMI) have been derived from populations including both women and men and including only a small proportion of women. Purpose Using supervised machine learning (ML), we examined the performance of 2 models assessing 5-year mortality after AMI, one derived from a population of women only and one from a population of men only, to determine whether sex-specific models improved outcome prediction. Methods This cohort study used the French registry on acute ST-elevation and non-ST-elevation myocardial infarction (FAST-MI) 2010 and 2015 surveys. This multicentric registry led in more than 200 French hospitals, enrolled all consecutive patients with acute myocardial infarction during a 1-month recruitment period. Our analysis included all men and women presenting STEMI who underwent invasive coronary angiography (ICA). To build and validate the models, the data set were split with a 70%/30% ratio into training and testing sets. The primary outcome was 5-year all-cause mortality. Then, 52 clinical, laboratory, ECG, echocardiographic, and ICA parameters were evaluated for feature selection using Boruta algorithm. Different supervised machine learning algorithms, including random forest (RF), were assessed for model building, and their performance were compared in women and men. To compare prediction accuracy according to sex, we evaluate the performance of the women-based ML model in men from the same cohort. Results 1,189 consecutive women and 3,685 men with STEMI (mean age 61±13 and 69±15 years, respectively) were recruited; 12% of men and 20% of women experienced 5-year all-cause mortality. Using Boruta algorithm, the 10 most important variables for prediction were selected (Figure 1). For women-based ML model building, the RF algorithm exhibited the best performance to predict mortality with an area under the receiver-operating characteristic curve (ROC-AUC) of 0.82 (95% CI: 0.77 – 0.88), an area under the precision-recall curve (PR-AUC) of 0.59 (95% CI: 0.54 – 0.64); and a F1-score of 0.58. The women-based ML model exhibited lower performance in men (ROC-AUC: 0.78; PR-AUC: 0.43; Figure 2). Conversely, the men-based model exhibited better accuracy in men than in women. Conclusion In a large multicentric cohort of STEMI patients, a women-based ML-model exhibited a good accuracy to predict 5-year all-cause mortality in women, but a drop of accuracy when applied to men. In mirror, the accuracy of the men-based model was lower in women. This suggests that sex-specific models might be superior to general models to predict mortality after AMI.Features selection and ML model buildingML model performance according to sex