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

Angharad Marks, Nicholas Fluck, Gordon J Prescott, Lynn Robertson, William G. Simpson, William Cairns Smith, Corri Black

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27 Citations (Scopus)
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Background 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.

Methods 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.

Results 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.

Conclusions 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.
Original languageEnglish
Pages (from-to)1507-1517
Number of pages11
JournalNephrology Dialysis Transplantation
Issue number9
Early online date5 May 2015
Publication statusPublished - Sept 2015

Bibliographical note

This work was supported by the Chief Scientists Office for Scotland [CZH/4/656], NHS Grampian Endowment Research Fund and NHS Grampian Renal Endowment. We thank Information
Services Division Scotland, Scottish Renal Registry and NHS Grampian who provided data. We also thank the University of Aberdeen Data Safe Haven, who hosted the data and provided data management support and the linkage service, and also the staff of NHS Grampian Renal Unit.


  • chronic kidney disease
  • outcome
  • risk prediction


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