{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:24:55Z","timestamp":1787250295479,"version":"build-2736575974"},"reference-count":24,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Machine learning (ML) models are increasingly used to predict acute kidney injury (AKI), but validation quality and clinical readiness remain uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This systematic review and meta-analysis aimed to summarize discrimination performance and implementation-relevant gaps for AKI occurrence and post-AKI mortality prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We searched the Cochrane Library, Embase, PubMed, and Web of Science through January 23, 2025. Eligible studies developed or validated ML-based prediction models and reported the area under the receiver operating characteristic curve (AUC). Two reviewers screened studies, extracted data, and assessed risk of bias using the PROBAST+AI (Prediction Model Risk Of Bias Assessment Tool+Artificial Intelligence). Logit-transformed AUCs were pooled using restricted maximum likelihood random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman\u2013adjusted inference.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We included 219 studies with 7,343,170 participants and 101 modeling approaches. Primary analyses included 188 AUC estimates for AKI occurrence and 31 for post-AKI mortality. Pooled AUCs were 0.834 (95% CI 0.821\u20100.846) for AKI occurrence prediction and 0.830 (95% CI 0.807\u20100.851) for post-AKI mortality prediction. For AKI occurrence, nonlinear approaches, especially deep learning and tree-based or ensemble methods, generally showed higher pooled AUC point estimates than linear or generalized linear models in exploratory subgroup analyses. At the study level, PROBAST+AI rated 129 (58.9%) studies as having low risk, 84 (38.4%) studies as having high risk, and 6 (2.7%) studies as having unclear risk. External validation was uncommon: it was reported in 30 (16.0%) AKI occurrence records and 9 (29.0%) post-AKI mortality records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML models have shown high average discrimination for AKI occurrence and post-AKI mortality, supporting their potential value for AKI risk stratification and early warning. However, high levels of heterogeneity, limited external or prospective validation, and inconsistent reporting of model calibration and clinical utility mean that substantial barriers remain before routine clinical deployment. Pooled AUC estimates revealed that nonlinear models have considerable clinical translational potential. Further refinements to modeling frameworks are warranted to explore feasible strategies for real-world clinical implementation. Future studies should prioritize standardized definitions, robust validation, clinically meaningful thresholds, assessment of alert burden, and evidence that model-guided care improves kidney-protective management or patient outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/104269","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:42:03Z","timestamp":1787247723000},"page":"e104269-e104269","source":"Crossref","is-referenced-by-count":0,"title":["AI Models for Predicting Acute Kidney Injury (AKI) and Post-AKI Mortality: Systematic Review and Meta-Analysis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5508-2031","authenticated-orcid":false,"given":"Huiyu","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9389-1750","authenticated-orcid":false,"given":"Jingjing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4068-3139","authenticated-orcid":false,"given":"Gaoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2518-2465","authenticated-orcid":false,"given":"Yunhao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9518-8284","authenticated-orcid":false,"given":"Tianxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6648-7785","authenticated-orcid":false,"given":"Xin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7000-0789","authenticated-orcid":false,"given":"Qiangguo","family":"Ao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9521-2940","authenticated-orcid":false,"given":"Yazhou","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3190-8455","authenticated-orcid":false,"given":"Qiuyue","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"issue":"8","key":"R1","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1007\/s00134-015-3934-7","article-title":"Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study","volume":"41","author":"Hoste","journal-title":"Intensive Care Med"},{"issue":"8","key":"R2","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s00134-021-06454-7","article-title":"Acute kidney injury in the critically ill: an updated review on pathophysiology and management","volume":"47","author":"Pickkers","journal-title":"Intensive Care Med"},{"issue":"9843","key":"R3","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1016\/S0140-6736(11)61454-2","article-title":"Acute kidney injury","volume":"380","author":"Bellomo","journal-title":"Lancet"},{"issue":"7767","key":"R4","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Toma\u0161ev","journal-title":"Nature"},{"issue":"11","key":"R5","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1007\/s00134-016-4670-3","article-title":"Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: the PrevAKI randomized controlled trial","volume":"43","author":"Meersch","journal-title":"Intensive Care Med"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12010375","article-title":"Acute kidney injury: medical causes and pathogenesis","volume":"12","author":"Turgut","journal-title":"J Clin Med"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1050255","article-title":"Machine learning for acute kidney injury: changing the traditional disease prediction mode","volume":"10","author":"Yu","journal-title":"Front Med (Lausanne)"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.2196\/51255","article-title":"A machine learning-based prediction model for acute kidney injury in patients with community-acquired pneumonia: multicenter validation study","volume":"26","author":"Ma","journal-title":"J Med Internet Res"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","DOI":"10.1080\/0886022X.2023.2194436","article-title":"Development and validation of a prediction model for the early occurrence of acute kidney injury in patients with acute pancreatitis","volume":"45","author":"Wu","journal-title":"Ren Fail"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111034","article-title":"Development of real-time individualized risk prediction models for contrast associated acute kidney injury and 30-day dialysis after contrast enhanced computed tomography","volume":"167","author":"Chen","journal-title":"Eur J Radiol"},{"issue":"7","key":"R11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1000100","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration","volume":"6","author":"Liberati","journal-title":"PLoS Med"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"key":"R13","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-14-25","article-title":"The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method","volume":"14","author":"IntHout","journal-title":"BMC Med Res Methodol"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.d4002","article-title":"Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials","volume":"343","author":"Sterne","journal-title":"BMJ"},{"issue":"7568","key":"R15","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1136\/bmj.333.7568.597","article-title":"The case of the misleading funnel plot","volume":"333","author":"Lau","journal-title":"BMJ"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i6460","article-title":"A guide to systematic review and meta-analysis of prediction model performance","volume":"356","author":"Debray","journal-title":"BMJ"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i3140","article-title":"External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges","volume":"353","author":"Riley","journal-title":"BMJ"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the performance of prediction models: a framework for traditional and novel measures","volume":"21","author":"Steyerberg","journal-title":"Epidemiology"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the Achilles heel of predictive analytics","volume":"17","author":"Van Calster","journal-title":"BMC Med"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.q902","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"key":"R21","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m4786","article-title":"Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial","volume":"372","author":"Wilson","journal-title":"BMJ"},{"issue":"5","key":"R22","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.13359","article-title":"Characterization of risk prediction models for acute kidney injury: a systematic review and meta-analysis","volume":"6","author":"Feng","journal-title":"JAMA Netw Open"},{"issue":"6","key":"R23","doi-asserted-by":"publisher","DOI":"10.1093\/ckj\/sfae150","article-title":"Using artificial intelligence to predict mortality in AKI patients: a systematic review\/meta-analysis","volume":"17","author":"Raina","journal-title":"Clin Kidney J"},{"issue":"15","key":"R24","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14151594","article-title":"Machine learning models for predicting mortality in critically ill patients with sepsis-associated acute kidney injury: a systematic review","volume":"14","author":"Wu","journal-title":"Diagnostics (Basel)"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:42:04Z","timestamp":1787247724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e104269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":24,"URL":"https:\/\/doi.org\/10.2196\/104269","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]},"article-number":"v28i8e104269"}}