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

Nephrology Dialysis Transplantation, Aug 2015

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.

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

Nephrol Dial Transplant Looking to the future: predicting renal replacement outcomes in a large community cohort with chronic kidney disease Angharad Marks 0 1 2 Nicholas Fluck 0 1 Gordon J Prescott 0 2 Lynn Robertson 0 2 William G Simpson 0 1 William Cairns Smith 0 2 Corri Black 0 1 2 0 The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved 1 NHS Grampian , Aberdeen , UK 2 Aberdeen Applied Renal Research Collaboration, Division of Applied Health Sciences, University of Aberdeen , Aberdeen , UK Correspondence and offprint requests to: Angharad Marks; E-mail: I N T R O D U C T I O N chronic kidney disease; outcome; risk prediction - A B S T R AC T 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 Cstatistic 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. In the UK, over 3.6 million adults are estimated to have chronic kidney disease (CKD) [1]; 23 million in the USA [2, 3]. While many remain undiagnosed, recognition is improving rapidly and more are coming to medical attention [4]. People with CKD are at increased risk of mortality, cardiovascular disease and progressive kidney function decline [leading to renal replacement therapy (RRT)] [5, 6]. Progression to poor outcomes is highly variable and only a small proportion will require RRT [4]. Important opportunities therefore exist for improving care, maintaining function, reducing progression and minimizing and managing complications. People with CKD often present to primary care are often elderly and frequently have multiple morbidities. An ability to identify which patients would benefit most from interventions including referral to specialist services is key. Stratification of patients by predicted risk of future outcomes would potentially enable care pathways to be optimized [7]. The literature regarding prognosis prediction in CKD has been recently reviewed [8, 9] and the processes involved summarized [10]. Of the studies identified in the reviews, 10 predicted progression of CKD or renal failure, three cardiovascular events and five all-cause mortality. All but two of the progression prediction models [11, 12] were developed in patients referred to nephrology services. Thus, model utility in other contexts, particularly the community, is not clear [13, 14]. Some models used variables not routinely available in clinical practice, e.g. cystatin C. Very few models have been externally validated. None have been applied in clinical practice. Although Tangri et al. [15] developed models using a population referred to nephrology services, these models contain commonly available variables (including measures of age, sex and excretory renal function), which were externally validated by the authors in another referred population; and model performance has since been reported in 595 referred individuals [14]. Unlike many prediction model studies, model performance metrics including discrimination, calibration and reclassification [10] were reported. Thus, these ‘kidney failure risk equation’ (KFRE) models have the best evidence for their use to predict risk in CKD [15]. We aimed to report the development and validation of models to predict first outcome (mortality or RRT initiation) by 5 years in a large community-based CKD cohort. We compared the perform (...truncated)


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Angharad Marks, Nicholas Fluck, Gordon J Prescott, Lynn Robertson, William G Simpson, William Cairns Smith, Corri Black. Looking to the future: predicting renal replacement outcomes in a large community cohort with chronic kidney disease, Nephrology Dialysis Transplantation, 2015, pp. 1507-1517, 30/9, DOI: 10.1093/ndt/gfv089