Early detection of chronic kidney disease in low-income and middle-income countries: Development and validation of a point-of-care screening strategy for India

• Type of Material: Article
• Authors: Christina Bradshaw, Stanford University School of Medicine; Dimple Kondal, Public Health Foundation of India; Maria E. Montez-Rath, Stanford University School of Medicine; Jialin Han, Stanford University School of Medicine; Yuanchao Zheng, Stanford University School of Medicine; Roopa Shivashankar, Public Health Foundation of India and Centre for Chronic Disease Control; Ruby Gupta, Public Health Foundation of India; Nikhil Srinivasapura Venkateshmurthy, Public Health Foundation of India; Prashant Jarhyan, Public Health Foundation of India; Sailesh Mohan, Public Health Foundation of India; Viswanathan Mohan, Madras Diabetes Research Foundation; Mohammed K Ali, Emory University; Shivani Patel, Emory University; K.M. Venkat Narayan, Emory University; Nikhil Tandon, All India Institute of Medical Sciences, New Delhi; Dorairaj Prabhakaran, Emory University; Shuchi Anand, Stanford University School of Medicine
• Language: English
• Date: 2019-09-01
• Publisher: BMJ Publishing Group: Open Access
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © Author(s) (or their employer(s)) 2019.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1136/bmjgh-2019-001644
• Title of Journal or Parent Work: BMJ Global Health
• ISSN: 2059-7908
• Volume: 4
• Issue: 5
• Start Page: e001644
• End Page: e001644
• Grant/Funding Information: CB was supported by the National Institutes of Health (NIH) Fogarty Global Health Equity Scholar, grant number R25TW9338.; SA was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the NIH, grant number 5K23DK101826.; UDAY was supported by an educational grant under the Lilly NCD Partnership Programme.; The CARRS-I and CARRS-II studies were supported by National Heart, Lung, and Blood Institute of the NIH, Department of Health and Human Services (Contract No. HHSN268200900026C) and United Health Group (Minneapolis, MN, USA).
• Keywords: community-based survey; screening; epidemiology
• Research Categories: Health Sciences, Epidemiology

Abstract

Introduction Although deaths due to chronic kidney disease (CKD) have doubled over the past two decades, few data exist to inform screening strategies for early detection of CKD in low-income and middle-income countries. Methods Using data from three population-based surveys in India, we developed a prediction model to identify a target population that could benefit from further CKD testing, after an initial screening implemented during home health visits. Using data from one urban survey (n=8698), we applied stepwise logistic regression to test three models: one comprised of demographics, self-reported medical history, anthropometry and point-of-care (urine dipstick or capillary glucose) tests; one with demographics and self-reported medical history and one with anthropometry and point-of-care tests. The gold-standard' definition of CKD was an estimated glomerular filtration rate <60 mL/min/1.73 m 2 or urine albumin-to-creatinine ratio ≥30 mg/g. Models were internally validated via bootstrap. The most parsimonious model with comparable performance was externally validated on distinct urban (n=5365) and rural (n=6173) Indian cohorts. Results A model with age, sex, waist circumference, body mass index and urine dipstick had a c-statistic of 0.76 (95% CI 0.75 to 0.78) for predicting need for further CKD testing, with external validation c-statistics of 0.74 and 0.70 in the urban and rural cohorts, respectively. At a probability cut-point of 0.09, sensitivity was 71% (95% CI 68% to 74%) and specificity was 70% (95% CI 69% to 71%). The model captured 71% of persons with CKD and 90% of persons at highest risk of complications from untreated CKD (ie, CKD stage 3A2 and above). Conclusion A point-of-care CKD screening strategy using three simple measures can accurately identify high-risk persons who require confirmatory kidney function testing.