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

Abstract

IntroductionAlthough 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.MethodsUsing 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.ResultsA 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).ConclusionA point-of-care CKD screening strategy using three simple measures can accurately identify high-risk persons who require confirmatory kidney function testing.

Detecting chronic kidney disease in diabetic adults by estimating glomerular filtration rate and Serum Creatinine

“…16,17 In order to study CKD provoking factors, sample was divided into two groups; with and without CKD, hypertension prevalence was higher in those with CKD, in agreement with Bradshaw et al, Narenpitak et al, Hooi et al, and Jolly et al; DM and hypertension are independent risk factors and responsible for 15% of CKD. 4,30,[36][37][38][39] Heart disease prevalence was higher in those with CKD in agreement with Bradshaw et al and (Jolly et al; cardiovascular risk increase as eGFR drop, death related to heart disease was 2-3 times in those with stage 3 and 4 CKD, respectively, than that with normal kidney function. 4,21,36,38 Those with CKD are more likely to be unemployed with no income which is in agreement with Bradshaw et al Females' percent was higher in those with CKD which is in agreement with Hooi et al 37 ; as age-related diabetic kidney disease progression differ between sexes.…”

“…4,30,[36][37][38][39] Heart disease prevalence was higher in those with CKD in agreement with Bradshaw et al and (Jolly et al; cardiovascular risk increase as eGFR drop, death related to heart disease was 2-3 times in those with stage 3 and 4 CKD, respectively, than that with normal kidney function. 4,21,36,38 Those with CKD are more likely to be unemployed with no income which is in agreement with Bradshaw et al Females' percent was higher in those with CKD which is in agreement with Hooi et al 37 ; as age-related diabetic kidney disease progression differ between sexes. 40 Those living in periphery are more likely to have CKD; in low income countries where unaffordable costly health service, unawareness, 41 unemployment and DM epidemic in Iraq, making CKD a leading cause of death and lately referred CKD.…”

“…40 Those living in periphery are more likely to have CKD; in low income countries where unaffordable costly health service, unawareness, 41 unemployment and DM epidemic in Iraq, making CKD a leading cause of death and lately referred CKD. 36,[42][43][44] Study sample was categorized to five stages according to eGFR level 19 using two equations; standard CKD-EPI (the most accepted index one 23 ) and CG, comparing between above equations showed strong agreement: 85.5%, kappa: 0.73 (95% CI: 0.68-0.77, p < 0.001), 96.1% of CKD patients were with stage 3 that was higher by depending on CG which is in agreement with Zaman et al that found strong agreement: 70.9%, kappa: 0.56 (95% CI: 0.44-0.67, p < 0.001) and considered high CG prevalence as overestimation compared to CKD-EPI, 31 and in agreement with Kitiyakara et al 2012; 45 that is related to continuously increasing CKD (stage 3) prevalence among adult in correlation with increasing DM, advanced age, and obesity. 19 CG doesn't take diverse people in consideration and requires weight and height which isn't available in laboratories while CKD-EPI does not; CKD-EPI is better to assess CKD prevalence in diabetics despite strong agreement between them.…”

Burden of Chronic Kidney Disease and Its Risk-Attributable Burden in 137 Low-and Middle-Income Countries, 1990-2019: Results From The Global Burden of Disease Study 2019

“…Chronic kidney disease (CKD) is a signi cant global health problem and one of the top 20 causes of death worldwide. It is a serious threat to people's life and health [20,21]. CKD DALYs is calculated by adding the YLLs and the YLDs, we found that the age-standardized YLL rate is much higher than the YLD rate at the global level and in various income regions, suggesting that premature death is the main cause of CKD burden.…”

Clinical, Operative, and Economic Outcomes of the Point-of-Care Blood Gases in the Nephrology Department of a Third-Level Hospital

“…[2][3][4]7,8,11,12 Publications available about POCT methods aimed at nephrologic patients are mostly for diagnosis and do not include BGA. [13][14][15][16] To the best of our knowledge, the present study assesses clinical, operative, and economic outcomes of POCT BGA in a nephrology department for the first time.…”