An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK

BMC Medicine

Research Article

Gary S. Collins¹  Sabine van der Veer²  Mattia Prosperi³  Iain Buchan⁴  Benjamin Brown⁴  Paolo Fraccaro⁴  Niels Peek⁴  Donal O’Donoghue⁵ 

[1] Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK;Health eResearch Centre, Farr Institute for Health Informatics Research, Manchester, UK;Centre for Health Informatics, Institute of Population Health, The University of Manchester, Vaughan House, Portsmouth St, M13 9GB, Manchester, UK;Health eResearch Centre, Farr Institute for Health Informatics Research, Manchester, UK;Centre for Health Informatics, Institute of Population Health, The University of Manchester, Vaughan House, Portsmouth St, M13 9GB, Manchester, UK;Department of Epidemiology, University of Florida, Gainesville, FL, USA;NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Institute of Population Health, The University of Manchester, Manchester, UK;Health eResearch Centre, Farr Institute for Health Informatics Research, Manchester, UK;Centre for Health Informatics, Institute of Population Health, The University of Manchester, Vaughan House, Portsmouth St, M13 9GB, Manchester, UK;Renal Clinic, Salford Royal NHS Trust, Salford, UK;

关键词: Chronic kidney disease;    Clinical prediction models;    eGFR;    Decision support;    Electronic health records;    Model validation;    Model calibration;   

DOI  :  10.1186/s12916-016-0650-2

 received in 2016-04-08, accepted in 2016-06-27,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundChronic kidney disease (CKD) is a major and increasing constituent of disease burdens worldwide. Early identification of patients at increased risk of developing CKD can guide interventions to slow disease progression, initiate timely referral to appropriate kidney care services, and support targeting of care resources. Risk prediction models can extend laboratory-based CKD screening to earlier stages of disease; however, to date, only a few of them have been externally validated or directly compared outside development populations. Our objective was to validate published CKD prediction models applicable in primary care.MethodsWe synthesised two recent systematic reviews of CKD risk prediction models and externally validated selected models for a 5-year horizon of disease onset. We used linked, anonymised, structured (coded) primary and secondary care data from patients resident in Salford (population ~234 k), UK. All adult patients with at least one record in 2009 were followed-up until the end of 2014, death, or CKD onset (n = 178,399). CKD onset was defined as repeated impaired eGFR measures over a period of at least 3 months, or physician diagnosis of CKD Stage 3–5. For each model, we assessed discrimination, calibration, and decision curve analysis.ResultsSeven relevant CKD risk prediction models were identified. Five models also had an associated simplified scoring system. All models discriminated well between patients developing CKD or not, with c-statistics around 0.90. Most of the models were poorly calibrated to our population, substantially over-predicting risk. The two models that did not require recalibration were also the ones that had the best performance in the decision curve analysis.ConclusionsIncluded CKD prediction models showed good discriminative ability but over-predicted the actual 5-year CKD risk in English primary care patients. QKidney, the only UK-developed model, outperformed the others. Clinical prediction models should be (re)calibrated for their intended uses.

【 授权许可】

CC BY   
© The Author(s). 2016