Article Text
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.
- screening
- epidemiology
- community-based survey
Data availability statement
No additional data are available.
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Data availability statement
No additional data are available.
Supplementary materials
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Footnotes
Handling editor Sanne Peters
Contributors CB, DK, MMR and SA were responsible for study design, data analysis and interpretation and manuscript development and revision. JH, YZ, NSV and RS were responsible for data analysis and manuscript revision. RG, PJ, SM, VM, MA, SP, KMN, NT and DP participated in data interpretation and manuscript revision.
Funding 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. 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). UDAY was supported by an educational grant under the Lilly NCD Partnership Programme.
Disclaimer The funding sources had no role in the study design, data collection and analysis, data interpretation, writing of the manuscript, or decision to submit the manuscript for publication.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.