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

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.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

“…The results indicated a decrease in the ASR in the microalbuminuria and MAC groups compared to the newly diagnosed diabetes and normal glucose tolerance subjects, suggesting its potential diagnostic value for the early detection of DN among Asian Indians. In conjunction with these studies, several other research works [81][82][83][84][85][86][87][88][89][90][91] have identified different biomarkers using urine, plasma, and tissue, aiding in the early detection of DKD through diverse metabolic pathways using the metabolomics approach tabulated in Table 2.…”

Cannabis and cannabinoids in dermatology: protocol for a systematic review and meta-analysis of quantitative outcomes

“…We will qualitatively analyse the studies and their results in accordance with Standard 4.2 and Chapter Open access 4 of Finding What Works in Healthcare: Standards for Systematic Review. 24 We will analyse the studies following the study outcomes, discuss the details of each performance of cannabis and cannabinoids in alleviating dermatological conditions or diseases and evaluate the risk of bias.…”