Development and validation of a predictive model for the diagnosis of rheumatic heart disease in low-income countries based on two cross-sectional studies

Authors

Madhab Ray, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA.
Santanu Guha, Department of Cardiology, Kolkata Medical College, Kolkata, India.
Ranga Raj Dhungana, Himalayan Association for STI-AIDS, Kathmandu, Nepal.
Avik Karak, Department of Cardiology, Kolkata Medical College, Kolkata, India.
Basabendra Choudhury, Department of Cardiology, Kolkata Medical College, Kolkata, India.
Bipasha Ray, University of Michigan, Ann Arbor, MI, USA.
Haroon Zubair, Advocate Aurora HealthFollow
Meghna Ray, Emory University, Atlanta, GA, USA.
Srijan Sengupta, North Carolina State University, Raleigh, NC, USA.
Deepak L. Bhatt, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, USA.Follow
Robert J. Goldberg, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.
Harry P. Selker, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA.

Recommended Citation

Ray M, Guha S, Dhungana RR, et al. Development and validation of a predictive model for the diagnosis of rheumatic heart disease in low-income countries based on two cross-sectional studies. Int J Cardiol Cardiovasc Risk Prev. 2023;18:200195. Published 2023 Jul 3. doi:10.1016/j.ijcrp.2023.200195

Affiliations

Aurora St Luke's Medical Center

Abstract

Objectives: We developed a questionnaire-based risk-scoring system to identify children at risk for rheumatic heart disease (RHD) in rural India. The resulting predictive model was validated in Nepal, in a population with a similar demographic profile to rural India.

Methods: The study involved 8646 students (mean age 13.0 years, 46% boys) from 20 middle and high schools in the West Midnapore district of India. The survey asked questions about the presence of different signs and symptoms of RHD. Students with possible RHD who experienced sore throat and joint pain were offered an echocardiogram to screen for RHD. Their findings were compared with randomly selected students without these symptoms. The data were analyzed to develop a predictive model for identifying RHD.

Results: Based on our univariate analyses, seven variables were used for building a predictive model. A four-variable model (joint pain plus sore throat, female sex, shortness of breath, and palpitations) best predicted the risk of RHD with a C-statistic of 0.854. A six-point scoring system developed from the model was validated among similarly aged children in Nepal.

Conclusions: A simple questionnaire-based predictive instrument could identify children at higher risk for this disease in low-income countries where RHD remains prevalent. Echocardiography could then be used in these high-risk children to detect RHD in its early stages. This may support a strategy for more effective secondary prophylaxis of RHD.

Type

Article

PubMed ID

37455788