Comparison of the performance of cardiovascular risk prediction tools in rural India: the Rishi Valley Prospective Cohort Study

Author:

Birhanu Mulugeta Molla1ORCID,Zengin Ayse1ORCID,Evans Roger G23ORCID,Joshi Rohina456ORCID,Kalyanram Kartik7ORCID,Kartik Kamakshi7ORCID,Danaei Goodarz8ORCID,Barr Elizabeth910ORCID,Riddell Michaela A1ORCID,Suresh Oduru17ORCID,Srikanth Velandai K1112ORCID,Arabshahi Simin1ORCID,Thomas Nihal13ORCID,Thrift Amanda G1ORCID

Affiliation:

1. Department of Medicine, School of Clinical Sciences at Monash Health, Monash University , Level 5, Block E, Monash Medical Centre, 246 Clayton Road, Melbourne, Victoria 3168 , Australia

2. Cardiovascular Disease Program, Biomedicine Discovery Institute and Department of Physiology, Monash University , Melbourne, Victoria , Australia

3. Pre-clinical Critical Care Unit, Florey Institute of Neuroscience and Mental Health, University of Melbourne , Melbourne, Victoria , Australia

4. Faculty of Medicine, School of Population Health, University of New South Wales , Sydney , Australia

5. George Institute for Global Health, University of New South Wales , Sydney, NSW , Australia

6. George Institute for Global Health , New Delhi , India

7. Rishi Valley Rural Health Centre , Madanapalle, Chittoor District, Andhra Pradesh , India

8. Department of Global Health and Population and Epidemiology, Harvard University T H Chan School of Public Health , Boston, MA , USA

9. Menzies School of Health Research, Charles Darwin University , Darwin, Northern Territory , Australia

10. Clinical Diabetes and Epidemiology, Baker Heart and Diabetes Institute , Melbourne, Victoria , Australia

11. Peninsula Clinical School, Central Clinical School, Monash University , Frankston, Victoria , Australia

12. National Centre for Healthy Ageing , Monash University and Peninsual Health, Melbourne, Victoria , Australia

13. Department of Endocrinology, Diabetes and Metabolism, Christian Medical College , Vellore, Tamil Nadu , India

Abstract

Abstract Aims We compared the performance of cardiovascular risk prediction tools in rural India. Methods and results We applied the World Health Organization Risk Score (WHO-RS) tools, Australian Risk Score (ARS), and Global risk (Globorisk) prediction tools to participants aged 40–74 years, without prior cardiovascular disease, in the Rishi Valley Prospective Cohort Study, Andhra Pradesh, India. Cardiovascular events during the 5-year follow-up period were identified by verbal autopsy (fatal events) or self-report (non-fatal events). The predictive performance of each tool was assessed by discrimination and calibration. Sensitivity and specificity of each tool for identifying high-risk individuals were assessed using a risk score cut-off of 10% alone or this 10% cut-off plus clinical risk criteria of diabetes in those aged >60 years, high blood pressure, or high cholesterol. Among 2333 participants (10 731 person-years of follow-up), 102 participants developed a cardiovascular event. The 5-year observed risk was 4.4% (95% confidence interval: 3.6–5.3). The WHO-RS tools underestimated cardiovascular risk but the ARS overestimated risk, particularly in men. Both the laboratory-based (C-statistic: 0.68 and χ2: 26.5, P = 0.003) and non–laboratory-based (C-statistic: 0.69 and χ2: 20.29, P = 0.003) Globorisk tools showed relatively good discrimination and agreement. Addition of clinical criteria to a 10% risk score cut-off improved the diagnostic accuracy of all tools. Conclusion Cardiovascular risk prediction tools performed disparately in a setting of disadvantage in rural India, with the Globorisk performing best. Addition of clinical criteria to a 10% risk score cut-off aids assessment of risk of a cardiovascular event in rural India.

Funder

National Health and Medical Research Council of Australia

Global Alliance for Chronic Diseases

Monash University

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine,Epidemiology

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