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

Bradshaw, Christina and Kondal, D and Montez-Rath, M E and Han, Jialin and Zheng, Yuanchao and Shivashankar, R and Gupta, R and Srinivasapura Venkateshmurthy, Nikhil and Jarhyan, Prashant and Mohan, S and Mohan, V and Ali, M K and Patel, S and Narayan, K M V and Tandon, N and Prabhakaran, D and Anand, S (2019) 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. BMJ Global Health, 4 (5). e001644. ISSN 2059-7908

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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 m2 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.

Item Type:Article
Official URL/DOI:http://dx.doi.org/10.1136/bmjgh-2019-001644
Uncontrolled Keywords:community-based survey; epidemiology; screening.
Subjects:Diabetes Epidemiology
Divisions:Department of Epidemiology
Department of Diabetology
ID Code:1245
Deposited By:surendar radha
Deposited On:05 Aug 2021 10:23
Last Modified:05 Aug 2021 10:23

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