A new risk score to assess atrial fibrillation risk in hypertensive patients (ESCARVAL-RISK Project.

This study aimed to assess atrial fibrillation (AF) incidence and predictive factors in hypertensive patients and to formulate an AF risk assessment score that can be used to identify the patients most likely to develop AF. This was a cohort study of patients recruited in primary healthcare centers. Patients aged 40 years or older with hypertension, free of AF and with no previous cardiovascular events were included. Patients attended annual visits according to clinical practice until the end of study or onset of AF. The association between AF incidence and explanatory variables (age, sex, body mass index, medical history and other) was analyzed. Finally, 12,206 patients were included (52.6% men, and mean age was 64.9 years); the mean follow-up was 36.7 months, and 394 (3.2%) patients were diagnosed with AF. The incidence of AF was 10.5/1000 person-years. Age (hazard ratio [HR] 1.06 per year; 95% confidence interval [CI] 1.05-1.08), male sex (HR 1.88; 95% CI 1.53-2.31), obesity (HR 2.57; 95% CI 1.70-3.90), and heart failure (HR 2.44; 95% CI 1.45-4.11) were independent predictors (p < 0.001). We propose a risk score based on significant predictors, which enables the identification of people with hypertension who are at the greatest risk of AF.

Machine learning to predict cardiovascular risk.

AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales. METHODS: We calculated cardiovascular risk by means of 15 machine-learning methods and using the SCORE and REGICOR scales and in 38 527 patients in the Spanish ESCARVAL RISK cohort, with 5-year follow-up. We considered patients to be at high risk when the risk of a cardiovascular event was over 5% (according to SCORE and machine learning methods) or over 10% (using REGICOR). The area under the receiver operating curve (AUC) and the C-index were calculated, as well as the diagnostic accuracy rate, error rate, sensitivity, specificity, positive and negative predictive values, positive likelihood ratio, and number needed to treat to prevent a harmful outcome. RESULTS: The method with the greatest predictive capacity was quadratic discriminant analysis, with an AUC of 0.7086, followed by Naive Bayes and neural networks, with AUCs of 0.7084 and 0.7042, respectively. REGICOR and SCORE ranked 11th and 12th, respectively, in predictive capacity, with AUCs of 0.63. Seven machine learning methods showed a 7% higher predictive capacity (AUC) as well as higher sensitivity and specificity than the REGICOR and SCORE scales. CONCLUSIONS: Ten of the 15 machine learning methods tested have a better predictive capacity for cardiovascular events and better classification indicators than the SCORE and REGICOR risk assessment scales commonly used in clinical practice in Spain. Machine learning methods should be considered in the development of future cardiovascular risk scales.