Representation of Multimorbidity and Frailty in the Development and Validation of Kidney Failure Prognostic Prediction Models: a Systematic Review

Overview

Journal BMC Med

Publisher Biomed Central

Specialty General Medicine

Date 2024 Oct 11

PMID 39394084

Authors

Heather Walker

Scott Day

Christopher H Grant

Catrin Jones

Robert Ker

Michael K Sullivan

Bhautesh Dinesh Jani

Katie Gallacher

Patrick B Mark

Affiliations

Soon will be listed here.

Abstract

Background: Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.

Methods: We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library-CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.

Results: Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.

Conclusions: There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.

Systematic Review Registration: This review has been registered on PROSPERO (CRD42022347295).

References

Ramspek C, Evans M, Wanner C, Drechsler C, Chesnaye N, Szymczak M . Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD. J Am Soc Nephrol. 2021; 32(5):1174-1186. PMC: 8259669. DOI: 10.1681/ASN.2020071077. View

Dai D, Alvarez P, Woods S . A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database. Clinicoecon Outcomes Res. 2021; 13:475-486. PMC: 8186939. DOI: 10.2147/CEOR.S313857. View

Ingwiller M, Krummel T, Dimitrov Y, Muller C, Ott J, Chantrel F . Evaluation of a predictive model of end-stage kidney disease in a French-based cohort. Int Urol Nephrol. 2022; 54(9):2335-2342. DOI: 10.1007/s11255-022-03138-z. View

Sullivan M, Carrero J, Jani B, Anderson C, McConnachie A, Hanlon P . The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function. BMC Med. 2022; 20(1):420. PMC: 9623942. DOI: 10.1186/s12916-022-02628-2. View

Prouvot J, Pambrun E, Couchoud C, Vigneau C, Roche S, Allot V . Low performance of prognostic tools for predicting dialysis in elderly people with advanced CKD. J Nephrol. 2021; 34(4):1201-1213. DOI: 10.1007/s40620-020-00919-6. View

Sullivan M, Jani B, Lees J, Welsh C, McConnachie A, Stanley B . Multimorbidity and the risk of major adverse kidney events: findings from the UK Biobank cohort. Clin Kidney J. 2021; 14(11):2409-2419. PMC: 8573008. DOI: 10.1093/ckj/sfab079. View

Landray M, Emberson J, Blackwell L, Dasgupta T, Zakeri R, Morgan M . Prediction of ESRD and death among people with CKD: the Chronic Renal Impairment in Birmingham (CRIB) prospective cohort study. Am J Kidney Dis. 2010; 56(6):1082-94. PMC: 2991589. DOI: 10.1053/j.ajkd.2010.07.016. View

Bundy J, Mills K, Anderson A, Yang W, Chen J, He J . Prediction of End-Stage Kidney Disease Using Estimated Glomerular Filtration Rate With and Without Race : A Prospective Cohort Study. Ann Intern Med. 2022; 175(3):305-313. PMC: 9083829. DOI: 10.7326/M21-2928. View

Zacharias H, Altenbuchinger M, Schultheiss U, Samol C, Kotsis F, Poguntke I . A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease. J Proteome Res. 2019; 18(4):1796-1805. DOI: 10.1021/acs.jproteome.8b00983. View

10.

Clegg A, Young J, Iliffe S, Rikkert M, Rockwood K . Frailty in elderly people. Lancet. 2013; 381(9868):752-62. PMC: 4098658. DOI: 10.1016/S0140-6736(12)62167-9. View

11.

Bai Q, Su C, Tang W, Li Y . Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep. 2022; 12(1):8377. PMC: 9120106. DOI: 10.1038/s41598-022-12316-z. View

12.

Haaskjold Y, Lura N, Bjorneklett R, Bostad L, Bostad L, Knoop T . Validation of two IgA nephropathy risk-prediction tools using a cohort with a long follow-up. Nephrol Dial Transplant. 2022; 38(5):1183-1191. PMC: 10157756. DOI: 10.1093/ndt/gfac225. View

13.

Lin Y, Shao H, Fonseca V, Anderson A, Batuman V, Shi L . A prediction model on incident ESKD among individuals with T2D and CKD. J Diabetes Complications. 2023; 37(4):108450. DOI: 10.1016/j.jdiacomp.2023.108450. View

14.

Marks A, Fluck N, Prescott G, Robertson L, Simpson W, Smith W . Looking to the future: predicting renal replacement outcomes in a large community cohort with chronic kidney disease. Nephrol Dial Transplant. 2015; 30(9):1507-17. DOI: 10.1093/ndt/gfv089. View

15.

Ye W, Ding X, Putnam N, Farej R, Singh R, Wang D . Development of clinical prediction models for renal and cardiovascular outcomes and mortality in patients with type 2 diabetes and chronic kidney disease using time-varying predictors. J Diabetes Complications. 2022; 36(5):108180. DOI: 10.1016/j.jdiacomp.2022.108180. View

16.

Berthoux F, Mohey H, Laurent B, Mariat C, Afiani A, Thibaudin L . Predicting the risk for dialysis or death in IgA nephropathy. J Am Soc Nephrol. 2011; 22(4):752-61. PMC: 3065230. DOI: 10.1681/ASN.2010040355. View

17.

Wu L, Wang G, Yang B, Liu X, Xie C, Feng X . Urinary Matrix Metalloproteinase 7 Activated by Oxidative Stress Predicts Kidney Prognosis in Myeloperoxidase-Antineutrophil Cytoplasmic Antibody-Associated Vasculitis. Antioxid Redox Signal. 2022; 37(4-6):246-256. DOI: 10.1089/ars.2021.0188. View

18.

Kovesdy C . Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011). 2022; 12(1):7-11. PMC: 9073222. DOI: 10.1016/j.kisu.2021.11.003. View

19.

Jahan S, Hale J, Malacova E, Hurst C, Kark A, Mallett A . Real world evaluation of kidney failure risk equations in predicting progression from chronic kidney disease to kidney failure in an Australian cohort. J Nephrol. 2023; 37(1):231-237. PMC: 10920458. DOI: 10.1007/s40620-023-01680-2. View

20.

Edmonston D, Wojdyla D, Mehta R, Cai X, Lora C, Cohen D . Single Measurements of Carboxy-Terminal Fibroblast Growth Factor 23 and Clinical Risk Prediction of Adverse Outcomes in CKD. Am J Kidney Dis. 2019; 74(6):771-781. PMC: 6875624. DOI: 10.1053/j.ajkd.2019.05.026. View