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Validation of the kidney failure risk equation in predicting the risk of progression to kidney failure in a multi-ethnic Singapore chronic kidney disease cohort. | LitMetric

AI Article Synopsis

Article Abstract

Introduction: The Kidney Failure Risk Equation (KFRE) was developed to predict the risk of progression to end-stage kidney disease (ESKD). Although the KFRE has been validated in multinational cohorts, the Southeast Asian population was under-represented. This study aimed to validate the KFRE in a multi-ethnic Singapore chronic kidney disease (CKD) cohort.

Methods: Stage 3-5 CKD patients referred to the renal medicine department at Singapore General Hospital in 2009 were included. The primary outcome (time to ESKD) was traced until 30 June 2017. The eight- and four-variable KFRE (non-North America) models using age, gender, estimated glomerular filtration rate, urine albumin-creatinine ratio, serum albumin, phosphate, bicarbonate and calcium were validated in our cohort. Cox regression, likelihood ratio (Χ), adequacy index, Harrell's C-index and calibration curves were calculated to assess the predictive performance, discrimination and calibration of these models on the cohort.

Results: A total of 1,128 patients were included. During the study period, 252 (22.3%) patients reached ESKD at a median time to ESKD of 84.8 (range 0.1-104.7) months. Both the eight- and four-variable KFRE models showed excellent predictive performance and discrimination (eight-variable: C-index 0.872, 95% confidence interval [CI] 0.850-0.894, adequacy index 97.3%; four-variable: C-index 0.874, 95% CI 0.852-0.896, adequacy index 97.9%). There was no incremental improvement in the prediction ability of the eight-variable model over the four-variable model in this cohort.

Conclusion: The KFRE was validated in a multi-ethnic Singapore CKD cohort. This risk score may help to identify patients requiring early renal care.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329555PMC
http://dx.doi.org/10.11622/smedj.2020170DOI Listing

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