SynthesisBMC medical informatics and decision making2022
Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.
Synthesis in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.Clinical and experimental medicine · 2025Pooled it
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.BMC medical informatics and decision making · 2026Article
- Predicting Chronic Kidney Disease from Biomarkers: An Explainable Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Identification and verification of feature biomarkers associated with CD14Journal of diabetes investigation · 2025Article
- Protocol for an Integrative Meta-Analysis of the Application of Machine Learning Algorithms in the Prediction of Chronic Disease Risks and Outcomes.Chronic diseases and translational medicine · 2025Article
- Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.BMC nephrology · 2025Article
- Article
- Article
- Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.Frontiers in medicine · 2025Article
- External validation of a 2-year all-cause mortality prediction tool developed using machine learning in patients with stage 4-5 chronic kidney disease.Journal of nephrology · 2024Article
- Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
- Preoperative Age and Its Impact on Long-Term Renal Functional Decline after Robotic-Assisted Partial Nephrectomy: Insights from a Tertiary Referral Center.Medicina (Kaunas, Lithuania) · 2024Article
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.Healthcare informatics research · 2023Article
- An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.Frontiers in medicine · 2023Article
- Interpretable machine learning for predicting chronic kidney disease progression risk.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression.
methodsWe searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model.
resultsFifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I
conclusionTaking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.