Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.
Cardiovascular disease (CVD) risk in India is currently assessed using the World Health Organization/International Society for Hypertension (WHO/ISH) risk prediction charts since no population-specific models exist. The WHO/ISH risk prediction charts have two versions-one with total cholesterol as a predictor (the high information (HI) model) and the other without (the low information (LI) model). However, information on the WHO/ISH risk prediction charts including guidance on which version to use and when, as well as relative performance of the LI and HI models, is limited. This article aims to, firstly, quantify the relative performance of the LI and HI WHO/ISH risk prediction (for WHO-South East Asian Region D) using data from rural India. Secondly, we propose a pre-screening (simplified) point-of-care (POC) test to identify patients who are likely to benefit from a total cholesterol (TC) test, and subsequently when the LI model is preferential to HI model. Analysis was performed using cross-sectional data from rural Andhra Pradesh collected in 2005 with recorded blood cholesterol measurements (N = 1066). CVD risk was computed using both LI and HI models, and high risk individuals who needed treatment(THR) were subsequently identified based on clinical guidelines. Model development for the POC assessment of a TC test was performed through three machine learning techniques: Support Vector Machine (SVM), Regularised Logistic Regression (RLR), and Random Forests (RF) along with a feature selection process. Disagreement in CVD risk predicted by LI and HI WHO/ISH models was 14.5% (n = 155; p<0.01) overall and comprised 36 clinically relevant THR patients (31% of patients identified as THR by using either model). Using two patient-specific parameters (age, systolic blood pressure), our POC assessment can pre-determine the benefit of TC testing and choose the appropriate risk model (out-of-sample AUCs:RF-0.85,SVM-0.84,RLR:0.82 and maximum sensitivity-98%). The identification of patients benefitting from a TC test for CVD risk stratification can aid planning for resource-allocation and save costs for large-scale screening programmes.
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Arvind
A
Raghu
The George Institute of Global Health, Hyderabad, India; University of Sydney, Sydney, NSW, Australia.
Devarsetty
D
Praveen
The George Institute of Global Health, Sydney, Australia.
David
D
Peiris
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Lionel
L
Tarassenko
Emory University, Atlanta, United States of America; Georgia Institute of Technology, Atlanta, United States of America.
Gari
G
Clifford
D044466:Asian People; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000209:etiology*; D002784:Cholesterol / Q000097:blood; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D006973:Hypertension / Q000097:blood / Q000453:epidemiology* / Q000209:etiology*; D007194:India / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D019095:Point-of-Care Systems; D018570:Risk Assessment; D012307:Risk Factors; D012952:Societies; D060388:Support Vector Machine; D014944:World Health Organization
D002784:Cholesterol