دورية أكاديمية
أعرض في EDS

Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease.

التفاصيل البيبلوغرافية
العنوان: Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease.
المؤلفون: Silva CAO; Programa de Pós-graduação em Inovação Tecnológica, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., Morillo CA; Department of Cardiac Sciences, Cumming School of Medicine, Libin Cardiovascular Institute, University of Calgary, Calgary, AB, Canada., Leite-Castro C; Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., González-Otero R; Departamento de Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Javeriana, Bogotá, Colombia., Bessani M; Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil., González R; Instituto del Corazón de Bucaramanga, Bogotá, Colombia., Castellanos JC; Departamento de Dirección General, Hospital Universitario San Ignacio, Bogotá, Colombia., Otero L; Centro de Investigaciones Odontológicas, Facultad de Odontología, Pontificia Universidad Javeriana, Bogotá, Colombia.
المصدر: Frontiers in cardiovascular medicine [Front Cardiovasc Med] 2022 Dec 07; Vol. 9, pp. 1050409. Date of Electronic Publication: 2022 Dec 07 (Print Publication: 2022).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101653388 Publication Model: eCollection Cited Medium: Print ISSN: 2297-055X (Print) Linking ISSN: 2297055X NLM ISO Abbreviation: Front Cardiovasc Med Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Lausanne : Frontiers Media S.A., [2014]-
مستخلص: 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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Silva, Morillo, Leite-Castro, González-Otero, Bessani, González, Castellanos and Otero.)
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فهرسة مساهمة: Keywords: atrial fibrillation; coronary artery disease; machine learning; risk prediction; sleep apnea; survival analysis
تواريخ الأحداث: Date Created: 20221226 Latest Revision: 20230103
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC9768180
DOI: 10.3389/fcvm.2022.1050409
PMID: 36568544
قاعدة البيانات: MEDLINE
الوصف
تدمد:2297-055X
DOI:10.3389/fcvm.2022.1050409