Looking to the future: predicting renal replacement outcomes in a large community cohort with chronic kidney disease

Marks, Angharad, Fluck, Nicholas, Prescott, Gordon orcid iconORCID: 0000-0002-9156-2361, Robertson, Lynn, Simpson, William G., Smith, William Cairns and Black, Corri (2015) Looking to the future: predicting renal replacement outcomes in a large community cohort with chronic kidney disease. Nephrology Dialysis Transplantation, 30 (9). pp. 1507-1517. ISSN 0931-0509

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Official URL: https://doi.org/10.1093/ndt/gfv089


Chronic kidney disease (CKD) is common and important due to poor outcomes. An ability to stratify CKD care based on outcome risk should improve care for all. Our objective was to develop and validate 5-year outcome prediction tools in a large population-based CKD cohort. Model performance was compared with the recently reported ‘kidney failure risk equation’ (KFRE) models.

Those with CKD in the Grampian Laboratory Outcomes Mortality and Morbidity Study-I (3396) and -II (18 687) cohorts were used to develop and validate a renal replacement therapy (RRT) prediction tool. The discrimination, calibration and overall performance were assessed. The net reclassification index compared performance of the developed model and the 3- and 4-variable KFRE model to predict RRT in the validation cohort.

The developed model (with measures of age, sex, excretory renal function and proteinuria) performed well with a C-statistic of 0.938 (0.918–0.957) and Hosmer–Lemeshow (HL) χ2 statistic 4.6. In the validation cohort (18 687), the developed model falsely identified fewer as high risk (414 versus 3278 individuals) compared with the KFRE 3-variable model (measures of age, sex and excretory renal function), but had more false negatives (58 versus 21 individuals). The KFRE 4-variable model could only be applied to 2274 individuals because of a lack of baseline urinary albumin creatinine ratio data, thus limiting its use in routine clinical practice.

CKD outcome prediction tools have been developed by ourselves and others. These tools could be used to stratify care, but identify both false positives and -negatives. Further refinement should optimize the balance between identifying those at increased risk with clinical utility for stratifying care.

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