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

Abstract

Introduction

Most prognostic stratification tools in acute myocardial infarction (AMI) have been derived from populations including both women and men with only a small proportion of women.

Objective

Using supervised machine learning (ML), we assessed the performance of 2 models for 5-year mortality prediction after AMI, derived from men and women, to determine whether sex-specific models improved outcome prediction.

Method

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.

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 (Fig. 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). Conversely, the men-based model exhibited better accuracy in men than in women.

Conclusion

In a large multicentric cohort of STEMI patients, women- and men-based ML-models exhibited a good accuracy to predict 5-year all-cause mortality with a drop of accuracy when applied to the other sex. This suggests that sex-specific models might be superior to general models to predict mortality after AMI.