Prediction models used in the progression of chronic kidney disease: A scoping review

David K E Lim; James H Boyd; Elizabeth Thomas; Aron Chakera; Sawitchaya Tippaya; Ashley Irish; Justin Manuel; Kim Betts; Suzanne Robinson

doi:10.1371/journal.pone.0271619

Prediction models used in the progression of chronic kidney disease: A scoping review

PLoS One. 2022 Jul 26;17(7):e0271619. doi: 10.1371/journal.pone.0271619. eCollection 2022.

Authors

David K E Lim¹, James H Boyd^{1

2}, Elizabeth Thomas^{1

3}, Aron Chakera^{3

4}, Sawitchaya Tippaya⁵, Ashley Irish⁶, Justin Manuel⁶, Kim Betts¹, Suzanne Robinson^{1

7}

Affiliations

¹ Curtin School of Population Health, Curtin University, Perth, WA, Australia.
² La Trobe University, Melbourne, Bundoora, VIC, Australia.
³ Medical School, The University of Western Australia, Perth, WA, Australia.
⁴ Renal Unit, Sir Charles Gairdner Hospital, Perth, WA, Australia.
⁵ Curtin Institute for Computation, Curtin University, Perth, WA, Australia.
⁶ WA Country Health Service, Perth, WA, Australia.
⁷ Deakin Health Economics, Deakin University, Burwood, VIC, Australia.

Abstract

Objective: To provide a review of prediction models that have been used to measure clinical or pathological progression of chronic kidney disease (CKD).

Design: Scoping review.

Data sources: Medline, EMBASE, CINAHL and Scopus from the year 2011 to 17th February 2022.

Study selection: All English written studies that are published in peer-reviewed journals in any country, that developed at least a statistical or computational model that predicted the risk of CKD progression.

Data extraction: Eligible studies for full text review were assessed on the methods that were used to predict the progression of CKD. The type of information extracted included: the author(s), title of article, year of publication, study dates, study location, number of participants, study design, predicted outcomes, type of prediction model, prediction variables used, validation assessment, limitations and implications.

Results: From 516 studies, 33 were included for full-text review. A qualitative analysis of the articles was compared following the extracted information. The study populations across the studies were heterogenous and data acquired by the studies were sourced from different levels and locations of healthcare systems. 31 studies implemented supervised models, and 2 studies included unsupervised models. Regardless of the model used, the predicted outcome included measurement of risk of progression towards end-stage kidney disease (ESKD) of related definitions, over given time intervals. However, there is a lack of reporting consistency on details of the development of their prediction models.

Conclusions: Researchers are working towards producing an effective model to provide key insights into the progression of CKD. This review found that cox regression modelling was predominantly used among the small number of studies in the review. This made it difficult to perform a comparison between ML algorithms, more so when different validation methods were used in different cohort types. There needs to be increased investment in a more consistent and reproducible approach for future studies looking to develop risk prediction models for CKD progression.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Delivery of Health Care
Disease Progression
Humans
Kidney Failure, Chronic*
Renal Insufficiency, Chronic*

Grants and funding

This research is supported by the Digital Health CRC Limited (DHCRC) and is part of a larger 4-year collaborative partnership between Curtin University, La Trobe University, WA Department of Health, WA Country Health Services, WA Primary Health Alliance, and the DHCRC. The DHCRC is funded under the Commonwealth's Cooperative Research Centres (CRC) Program, project ID DHCRC-0073. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.