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

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.

A comparison of waiting times for assessment and epilepsy surgery between a Canadian and a Mexican referral center.

Objective: To provide a comprehensive transnational overview of wait times for epilepsy surgery in Canada and Mexico. Methods: We reviewed all cases referred for epilepsy surgery between 2007 and 2015 at the Saskatchewan Epilepsy Program Royal University Hospital (SEP) (n = 70; Saskatoon, Canada) and the National Institute of Neurology and Neurosurgery (NINN) (n = 76; Mexico City, Mexico) and compared wait times, calculated as the time from diagnosis of epilepsy on assessment at an epilepsy center to epilepsy surgery. Results: Mean wait times were similar across centers. Mean patient age was 37.4 ± 9 years (NINN) and 36.7 ± 13.2 years (SEP). The mean time from epilepsy diagnosis to referral was 18.9 (NINN) and 16.9 years (SEP), p = 0.30; first consult with the epileptologist, 19.7 (NINN) and 17.4 years (p = 0.23); neuropsychology consult, 21.4 (NINN) and 17.9 years (SEP); video electroencephalogram (video-EEG) telemetry, 21.1 (NINN) and 18.6 months (SEP); initial neurosurgical consult, 21.9 (NINN) and 19.1 years (SEP) (p = 0.35); and epilepsy surgery, 19.7 (NINN) and 19.6 years (SEP) (p = 0.29). Significance: This is the first study to compare wait times between Canada and Mexico. Despite disparity in their health delivery systems and financial resources, surgical wait times appeared to be protracted in both nations, confirming that delayed treatment is a universal problem that requires collaborative scrutiny.