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Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease
Carlos A O Silva1, Carlos A Morillo2, Cristiano Leite-Castro3
1Programa de Pós-graduação em Inovação Tecnológica, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Insights
Patients with sleep apnea (SA) and coronary artery disease (CAD) have a higher risk of atrial fibrillation (AF). High blood pressure and chronic kidney disease also significantly increase AF risk, particularly in females.
Area of Science:
- Cardiology
- Pulmonology
- Nephrology
- Medical Informatics
Background:
- Patients with sleep apnea (SA) and coronary artery disease (CAD) face elevated risks of atrial fibrillation (AF).
- Understanding the interplay of these conditions is crucial for predicting AF development.
Purpose of the Study:
- To assess the roles of CAD and SA in AF risk using cluster and survival analyses.
- To develop a predictive risk model for AF in a Latin-American population.
Main Methods:
- Analysis of electronic medical records from 22,302 individuals using K-means clustering and k-nearest neighbor (kNN) algorithms.
- Survival analysis was employed, considering age, sex, and comorbidities over a 9-year period.
- Machine learning algorithms were utilized to identify significant risk factors for AF.
Main Results:
- CAD and high blood pressure (HBP) were prevalent in the AF group; HBP was most common in CAD patients; HBP and CAD were prevalent in SA patients.
- HBP, CAD, and SA each conferred a ~1.5-fold increased risk of developing AF.
- Chronic kidney disease (CKD) was associated with developing AF approximately 50% earlier (HR: 3.36).
- HBP, CAD, and SA contributed to earlier AF development in females compared to males within specific age groups.
Conclusions:
- Machine learning models identified CAD, SA, HBP, and CKD as significant risk factors for AF.
- These findings are particularly relevant for risk stratification in the Latin-American population.
Background:
Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF.
Methods:
Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis.
Results:
The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18-1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09-1.96, p = 0.01; HR: 1.54, 95% CI: 1.22-1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46-7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50-64 years were HBP (HR: 3.75 95% CI: 1.08-13, p = 0.04) CAD and SA in the group of 60-75 years were (HR: 2.4 95% CI: 1.18-4.86, p = 0.02; HR: 2.51, 95% CI: 1.14-5.52, p = 0.02, respectively).
Conclusion:
Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin-American population.
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