One-year clinical outcome of patients with nonvalvular atrial fibrillation: Insights from KERALA-AF registry.

BackgroundWe report patient characteristics, treatment pattern and one-year clinical outcome of nonvalvular atrial fibrillation (NVAF) from Kerala, India. This cohort forms part of Kerala Atrial Fibrillation (KERALA-AF) registry which is an ongoing large prospective study.MethodsKERALA-AF registry collected data of adults with previously or newly diagnosed atrial fibrillation (AF) during April 2016 to April 2017. A total of 3421 patients were recruited from 53 hospitals across Kerala state. We analysed one-year follow-up outcome of 2507 patients with NVAF.ResultsMean age at recruitment was 67.2 years (range 18-98) and 54.8% were males. Main co-morbidities were hypertension (61.2%), hyperlipidaemia (46.2%) and diabetes mellitus (37.2%). Major co-existing diseases were chronic kidney disease (42.1%), coronary artery disease (41.6%), and chronic heart failure (26.4%). Mean CHA2DS2-VASc score was 3.18 (SD ± 1.7) and HAS-BLED score, 1.84 (SD ± 1.3). At baseline, use of oral anticoagulants (OAC) was 38.6% and antiplatelets 32.7%. On one-month follow-up use of OAC increased to 65.8% and antiplatelets to 48.3%. One-year all-cause mortality was 16.48 and hospitalization 20.65 per 100 person years. The main causes of death were cardiovascular (75.0%), stroke (13.1%) and others (11.9%). The major causes of hospitalizations were acute coronary syndrome (35.0%), followed by arrhythmia (29.5%) and heart failure (8.4%).ConclusionsDespite high risk profile of patients in this registry, use of OAC was suboptimal, whereas antiplatelets were used in nearly half of patients. A relatively high rate of annual mortality and hospitalization was observed in patients with NVAF in Kerala AF Registry

Predicting stroke in Asian patients with atrial fibrillation using machine learning: A report from the KERALA-AF registry, with external validation in the APHRS-AF registry

Atrial fibrillation (AF) is a significant risk factor for stroke. Based on the higher stroke associated with AF in the South Asian population, we constructed a one-year stroke prediction model using machine learning (ML) methods in KERALA-AF South Asian cohort. External validation was performed in the prospective APHRS-AF registry. We studied 2101 patients and 83 were to patients with stroke in KERALA-AF registry. The random forest showed the best predictive performance in the internal validation with receiver operator characteristic curve (AUC) and G-mean of 0.821 and 0.427, respectively. In the external validation, the light gradient boosting machine showed the best predictive performance with AUC and G-mean of 0.670 and 0.083, respectively. We report the first demonstration of ML's applicability in an Indian prospective cohort, although the more modest prediction on external validation in a separate multinational Asian registry suggests the need for ethnic-specific ML models