In-Hospital and Three-Year Outcomes of Heart Failure Patients in South India: The Trivandrum Heart Failure Registry.

BACKGROUND: Long-term data on outcomes of participants hospitalized with heart failure (HF) from low- and middle-income countries are limited. METHODS AND RESULTS: In the Trivandrum Heart Failure Registry (THFR) in 2013, 1205 participants from 18 hospitals in Trivandrum, India, were enrolled. Data were collected on demographics, clinical presentation, treatment, and outcomes. We performed survival analyses, compared groups and evaluated the association between heart failure (HF) type and mortality, adjusting for covariates that predicted mortality in a global HF risk score. The mean (standard deviation) age of participants was 61.2 (13.7) years. Ischemic heart disease was the most common cause (72%). The in-hospital mortality rate was higher for participants with HF with reduced ejection fraction (HFrEF; 9.7%) compared with those with HF with preserved ejection fraction (HFpEF; 4.8%; P = .003). After 3 years, 540 (44.8%) participants had died. The all-cause mortality rate was lower for participants with HFpEF (40.8%) compared with HFrEF (46.2%; P = .049). In multivariable models, older age (hazard ratio [HR] 1.24 per decade, 95% confidence interval [CI] 1.15-1.33), New York Heart Association functional class IV symptoms (HR 2.80, 95% CI 1.43-5.48), and higher serum creatinine (HR 1.12 per mg/dL, 95% CI 1.04-1.22) were associated with all-cause mortality. CONCLUSIONS: Participants with HF in the THFR have high 3-year all-cause mortality. Targeted hospital-based quality improvement initiatives are needed to improve survival during and after hospitalization for HF.

One-year mortality outcomes and hospital readmissions of patients admitted with acute heart failure: Data from the Trivandrum Heart Failure Registry in Kerala, India.

BACKGROUND: There are sparse data on outcomes of patients with heart failure (HF) from India. The objective was to evaluate hospital readmissions and 1-year mortality outcomes of patients with HF in Kerala, India. METHODS: We followed 1,205 patients enrolled in the Trivandrum Heart Failure Registry for 1 year. A trained research nurse contacted each participant every 3 months using a structured questionnaire which included hospital readmission and mortality information. RESULTS: The mean (SD) age was 61.2 (13.7) years, and 31% were women. One out of 4 (26%) participants had HF with preserved ejection fraction. Only 25% of patients with HF with reduced ejection fraction received guideline-directed medical therapy at discharge. Cumulative all-cause mortality at 1 year was 30.8% (n = 371), but the greatest risk of mortality was in the first 3 months (18.1%). Most deaths (61%) occurred in patients younger than 70 years. One out of every 3 (30.2%) patients was readmitted at least once over 1 year. The hospital readmission rates were similar between HF with preserved ejection fraction and HF with reduced ejection fraction patients. New York Heart Association functional class IV status and lack of guideline-directed medical treatment after index hospitalization were associated with increased likelihood of readmission. Similarly, older age, lower education status, nonischemic etiology, history of stroke, higher serum creatinine, lack of adherence to guideline-directed medical therapy, and hospital readmissions were associated with increased 1-year mortality. CONCLUSIONS: In the Trivandrum Heart Failure Registry, 1 of 3 HF patients died within 1 year of follow-up during their productive life years. Suboptimal adherence to guideline-directed treatment is associated with increased propensity of readmission and death. Quality improvement programs aiming to improve adherence to guideline-based therapy and reducing readmission may result in significant survival benefits in the relatively younger cohort of HF patients in India.

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.