Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study
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Design Multinational, longitudinal, population based, cohort study.
Settings Linked population health data from Canada (training and temporal testing), and Denmark and Scotland (geographical testing).
Participants People with newly recorded chronic kidney disease at stage G3b-G4, estimated glomerular filtration rate (eGFR) 15-44 mL/min/1.73 m2.
Modelling The super learner algorithm selected the best performing regression models or machine learning algorithms (learners) based on their ability to predict kidney failure and mortality with minimised cross-validated prediction error (Brier score, the lower the better). Prespecified learners included age, sex, eGFR, albuminuria, with or without diabetes, and cardiovascular disease. The index of prediction accuracy, a measure of calibration and discrimination calculated from the Brier score (the higher the better) was used to compare KDpredict with the benchmark, kidney failure risk equation, which does not account for the competing risk of death, and to evaluate the performance of KDpredict mortality models.
Results 67 942 Canadians, 17 528 Danish, and 7740 Scottish residents with chronic kidney disease at stage G3b to G4 were included (median age 77-80 years; median eGFR 39 mL/min/1.73 m2). Median follow-up times were five to six years in all cohorts. Rates were 0.8-1.1 per 100 person years for kidney failure and 10-12 per 100 person years for death. KDpredict was more accurate than kidney failure risk equation in prediction of kidney failure risk: five year index of prediction accuracy 27.8% (95% confidence interval 25.2% to 30.6%) versus 18.1% (15.7% to 20.4%) in Denmark and 30.5% (27.8% to 33.5%) versus 14.2% (12.0% to 16.5%) in Scotland. Predictions from kidney failure risk equation and KDpredict differed substantially, potentially leading to diverging treatment decisions. An 80-year-old man with an eGFR of 30 mL/min/1.73 m2 and an albumin-to-creatinine ratio of 100 mg/g (11 mg/mmol) would receive a five year kidney failure risk prediction of 10% from kidney failure risk equation (above the current nephrology referral threshold of 5%). The same man would receive five year risk predictions of 2% for kidney failure and 57% for mortality from KDpredict. Individual risk predictions from KDpredict with four or six variables were accurate for both outcomes. The KDpredict models retrained using older data provided accurate predictions when tested in temporally distinct, more recent data.
Conclusions KDpredict could be incorporated into electronic medical records or accessed online to accurately predict the risks of kidney failure and death in people with moderate to severe CKD. The KDpredict learning strategy is designed to be adapted to local needs and regularly revised over time to account for changes in the underlying health system and care processes.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | e078063 |
| Tidsskrift | BMJ |
| Vol/bind | 385 |
| Antal sider | 12 |
| ISSN | 0959-8146 |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:
Funding: We disclose the following financial support for the research, authorship, or publication of this article: PL received post-doctoral fellowships from the Canadian Institutes of Health Research (Funding Reference Number (FRN) MFE-152465) and the Libin Cardiovascular Institute of Alberta during the design and analytic phases of this work, and received the Kidney Research Scientist Core Education and National Training (KRESCENT) New Investigator Award, co-sponsored by the Kidney Foundation of Canada and Canadian Institutes of Health Research, during the dissemination phase of this work (FRN 2023KNIA-1058404). PR held Canadian Institutes for Health Research funding (FRN 173359) to support studies in chronic kidney disease and was supported by the Baay Chair in Kidney Research at the University of Calgary. CFC received funding from the Independent Research Fund Denmark (FRN 0134-00407B). SKJ received funding from Aarhus University, the AP Moller Foundation (FRN19-L-0332), and the Health Research Foundation of the Central Denmark Region. AM has nothing to declare. RRQ held Canadian Institutes for Health Research funding to support home therapies for kidney failure. SS was supported by a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences, Welcome Trust, Medical Research Council, British Heart Foundation, Arthritis Research UK, the Royal College of Physicians and Diabetes UK (SGL020\\1076). The funding organisations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Funding Information:
Disclaimers: This study is based in part on data provided by Alberta Health and Alberta Health Services. The interpretation and conclusions contained herein are those of the researchers and do not represent the views of the Government of Alberta or Alberta Health Services. Neither the Government of Alberta, Alberta Health, nor Alberta Health Services express any opinion in relation to this study. We acknowledge the support of the Grampian data safe haven (DaSH) facility within the Aberdeen Centre for Health Data Science and the associated financial support of the University of Aberdeen, and NHS Research Scotland (through NHS Grampian investment in DaSH). For more information, visit the DaSH website: http://www.abdn.ac.uk/iahs/facilities/grampian-data-safe-haven.php . The Danish data are provided by the Danish Health Data Authority (website: https://sundhedsdatastyrelsen.dk/da/english ). The work was supported by Aarhus University and Aarhus University Hospital, but the interpretation and conclusions are those of the researchers.
Funding Information:
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest and declare: no support from any organisation for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. RQ has received speaker fees, attended advisory boards, and received research support from Baxter Corporation. RQ co-owns a Canadian patent for the Dialysis Measurement Analysis and Reporting System.
Publisher Copyright:
© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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