Five-year mortality and readmission rates in patients with heart failure in India: Results from the Trivandrum heart failure registry
Introduction
Heart failure (HF) is a global public health problem affecting more than 26 million people worldwide [1]. Most longitudinal outcome data for patients with HF are from high-income countries [2,3]. Available data from low- and middle-income countries (LMICs) are limited to short-term outcomes of patients with HF or single-centre studies and suggest that mortality rates for patients with HF are higher in LMICs than high-income countries [4,[5], [6], [7], [8]]. Further, wide heterogeneity in the causes and associated comorbidities in patients with HF across the globe [9,10] limits the extrapolation of long-term mortality data from high-income regions to LMICs.
Long-term outcome data and identifying factors associated with long-term prognosis are important for devising policy strategies to address the growing burden and management strategies for HF in India. We describe 5-year outcomes of patients with HF and evaluate factors associated with mortality in the Trivandrum Heart Failure Registry (THFR), which is India's first HF registry.
Section snippets
Study sample
The design of THFR has been reported previously [[5], [6], [7], [8]]. Briefly, THFR is a prospective, hospital-based registry of patients admitted with acute HF who were longitudinally followed over time. Patients were recruited from 16 hospitals in the region of Trivandrum, Kerala, India that cared for patients with HF from January 2013 to December 2013. The registry covered the Trivandrum city area (urban area: 215 km2, population: 957,000 people) and an adjacent suburban rural area,
Results
Baseline characteristics of 1205 participants (69% males, mean [SD] age 61.2 [13.7] years) are reported in Table 1. Ischaemic heart disease (72%) was the most common aetiology of HF followed by dilated cardiomyopathy and rheumatic heart disease (Table 2). Hypertension and diabetes mellitus were prevalent in 55% and 52% of the study participants, respectively. HFrEF was the most common group (62%), followed by HFpEF (20%) and HFmEF (18%). The in-hospital mortality was 8.4%. At discharge, beta
Discussion
We report that 59% of patients with HF in THFR died at 5 years, which represents the longest follow-up data of patients with HF in India. Our cohort of patients with acute HF is a much younger cohort compared to high-income country cohorts. The younger mean age of patients in the THFR also contrasts with the median age of 73 years among patients hospitalized with heart failure in China [13]. The in-hospital mortality of 8.4% and one year mortality of 30.8% is higher than the observed figures in
Conclusions
Three out of every 5 patients died during the 5-year follow-up period in the first HF registry in India. Only 25% of patients were discharged from the index hospitalization on GDMT and importantly, prescription of GDMT at discharge was associated with lower 5-year mortality rates in patients with HFrEF. Quality improvement interventions to increase evidence-based management and prevention of hospital readmissions may substantially improve outcomes of patients with HF in India.
Funding
We thank Indian Council of Medical Research (ICMR), India (Project No. 5/4/1-11/11-NCD-II - Comprehensive heart failure intervention program) and Indian Council of Medical Research (ICMR), India - Trivandrum Heart Failure Cohort. File number 50/1(5) / TF CVD / 16 / NCD-II for funding this study.
Disclosures
SH has received project funds from ICMR for Trivandrum HF Cohort, National HF registry and CARE HF (2019–2023). PJ is supported by a Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome Trust-Department of Biotechnology, India Alliance (2015–2020). MDH has received previous support from the World Heart Federation via Boehringer Ingelheim, Bupa, and Novartis and the American Heart Association, Verily, and AstraZeneca for work unrelated to this
CRediT authorship contribution statement
Conceptualization: SH, SG, SV, MS, GV, CB, TN, NR, KS, MH. Data curation: SH, SG, SV, MS, GV, CB, RB, TN, NP, KK, NR, KS. Formal analysis: PJ, SH, MH, AA. Funding acquisition: SH, SG. Methodology: SH, SG, MH. Project administration: SH SG. Writing - original draft; SH, SG, PJ, AA, MH. Writing - review & editing. SH, SG, PJ, AA, MH, SV, MS, GV, CB, RB, TN, NP, KK, NR, KS.
Acknowledgements
The authors thank Kochumoni, Suresh Babu, Vineeth Purushothaman, Anand Kumar, Ajeesh C, Krishna Sanker and Manas Chacko for data collection, data entry and follow-up data collection. The contributions of Dr. Jayapal and Dr. Arun of General Hospital, Dr. BVR Kumar of Jubilee hospital and Dr. Suman of KIMS hospital are acknowledged. We also thank Dr. Priya Sosa James, Dr. Abdul Salam and Dr. Anil Balachandran for data collection.
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