Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance.

Chen, Fangyue, Kantagowit, Piyawat, Nopsopon, Tanawin, Chuklin, Arisa and Pongpirul, Krit ORCID: 0000-0003-3818-9761 (2023) Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance. PloS one, 18 (2). e0278729-e0278729.

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Official URL: http://dx.doi.org/10.1371/journal.pone.0278729

Abstract

Chronic Kidney disease (CKD) is an important yet under-recognized contributor to morbidity and mortality globally. Machine-learning (ML) based decision support tools have been developed across many aspects of CKD care. Notably, algorithms developed in the prediction and diagnosis of CKD development and progression may help to facilitate early disease prevention, assist with early planning of renal replacement therapy, and offer potential clinical and economic benefits to patients and health systems. Clinical implementation can be affected by the uncertainty surrounding the methodological rigor and performance of ML-based models. This systematic review aims to evaluate the application of prognostic and diagnostic ML tools in CKD development and progression. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for CKD prediction and diagnosis have been registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022356704, CRD42022372378). A systematic search will be undertaken of PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), the Web of Science, and the IEEE Xplore digital library. Studies in which ML has been applied to predict and diagnose CKD development and progression will be included. The primary outcome will be the comparison of the performance of ML-based models with non-ML-based models. Secondary analysis will consist of model use cases, model construct, and model reporting quality. This systematic review will offer valuable insight into the performance and reporting quality of ML-based models in CKD diagnosis and prediction. This will inform clinicians and technical specialists of the current development of ML in CKD care, as well as direct future model development and standardization.

Item Type:	Article
Uncontrolled Keywords:	Humans, Renal Insufficiency, Chronic, Meta-Analysis as Topic, Machine Learning, Systematic Reviews as Topic, Continuous Renal Replacement Therapy
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	06 Mar 2024 10:58
Last Modified:	06 Mar 2024 10:58
DOI:	10.1371/journal.pone.0278729
Open Access URL:	https://doi.org/10.1371/journal.pone.0278729
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3179186