File(s) under permanent embargo
Prediction models used in the progression of chronic kidney disease: A scoping review
journal contribution
posted on 2023-02-08, 01:35 authored by DKE Lim, JH Boyd, E Thomas, A Chakera, S Tippaya, A Irish, J Manuel, K Betts, Suzanne RobinsonSuzanne RobinsonObjective
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.
History
Journal
PLoS ONEVolume
17Article number
e0271619Pagination
1-24Location
San Francisco, Calif.Publisher DOI
ISSN
1932-6203eISSN
1932-6203Language
EnglishPublication classification
C1 Refereed article in a scholarly journalEditor/Contributor(s)
Delanaye PIssue
7Publisher
Public Library of ScienceUsage metrics
Categories
No categories selectedKeywords
ARTIFICIAL-INTELLIGENCECKDFUTUREMultidisciplinary SciencesNEPHROLOGISTSRENAL-FAILURERISK PREDICTIONScience & TechnologyScience & Technology - Other TopicsVALIDATIONCLASSIFICATIONDelivery of Health CareDisease ProgressionHumansKidney Failure, ChronicRenal Insufficiency, ChronicKidney DiseaseNetworking and Information Technology R&DRenal and urogenitalGeneric health relevance3 Good Health and Well Being
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC