{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:39:09Z","timestamp":1792543149150,"version":"4.5.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Clinical Anesthesia"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.jclinane.2026.112353","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T21:02:52Z","timestamp":1792530172000},"page":"112353","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Deep learning algorithms for predicting intraoperative hypotension: A systematic review and meta-analysis"],"prefix":"10.1016","volume":"116","author":[{"given":"Junfeng","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengshi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jclinane.2026.112353_bb0005","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1097\/01.anes.0000270724.40897.8e","article-title":"Incidence of intraoperative hypotension as a function of the chosen definition: literature definitions applied to a retrospective cohort using automated data collection","volume":"107","author":"Bijker","year":"2007","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0010","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1097\/ALN.0b013e3182a10e26","article-title":"Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension","volume":"119","author":"Walsh","year":"2013","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0015","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1097\/ALN.0000000000001432","article-title":"Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: a retrospective cohort analysis","volume":"126","author":"Salmasi","year":"2017","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0020","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12630-023-02653-6","article-title":"Association between postinduction hypotension and postoperative mortality: a single-Centre retrospective cohort study","volume":"71","author":"Nakanishi","year":"2024","journal-title":"Can J Anaesth"},{"key":"10.1016\/j.jclinane.2026.112353_bb0025","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1097\/ALN.0000000000003557","article-title":"Hypotension prediction index for prevention of hypotension during moderate- to high-risk noncardiac surgery","volume":"133","author":"Maheshwari","year":"2020","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0030","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1213\/ANE.0000000000004121","article-title":"Ability of an arterial waveform analysis-derived hypotension prediction index to predict future hypotensive events in surgical patients","volume":"130","author":"Davies","year":"2020","journal-title":"Anesth Analg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0035","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1097\/ALN.0000000000002300","article-title":"Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis","volume":"129","author":"Hatib","year":"2018","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0040","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","article-title":"Explainable machine-learning predictions for the prevention of hypoxaemia during surgery","volume":"2","author":"Lundberg","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.jclinane.2026.112353_bb0045","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.jclinane.2026.112353_bb0050","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1186\/s12967-024-05481-4","article-title":"Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis","volume":"22","author":"Mohammadi","year":"2024","journal-title":"J Transl Med"},{"key":"10.1016\/j.jclinane.2026.112353_bb0055","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1001\/jama.2020.0592","article-title":"Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: the HYPE randomized clinical trial","volume":"323","author":"Wijnberge","year":"2020","journal-title":"JAMA"},{"key":"10.1016\/j.jclinane.2026.112353_bb0060","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.bja.2023.02.029","article-title":"Predicting intraoperative hypotension: from hope to hype and back to reality","volume":"131","author":"Michard","year":"2023","journal-title":"Br J Anaesth"},{"issue":"1","key":"10.1016\/j.jclinane.2026.112353_bb0065","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1213\/ANE.0000000000007216","article-title":"Intraoperative hypotension prediction: current methods, controversies, and research outlook","volume":"141","author":"Mukkamala","year":"2025","journal-title":"Anesth Analg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0070","doi-asserted-by":"crossref","first-page":"842","DOI":"10.23736\/S0375-9393.25.19197-9","article-title":"Forecasting the fall: the role of machine learning in predicting intraoperative hypotension, a scoping review","volume":"91","author":"Koh","year":"2025","journal-title":"Minerva Anestesiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0075","doi-asserted-by":"crossref","DOI":"10.2196\/31311","article-title":"Short-term event prediction in the operating room (STEP-OP) of five-minute intraoperative hypotension using hybrid deep learning: retrospective observational study and model development","volume":"9","author":"Choe","year":"2021","journal-title":"JMIR Med Inform"},{"key":"10.1016\/j.jclinane.2026.112353_bb0080","first-page":"1300","article-title":"Predicting intraoperative hypotension using deep learning with waveform data","volume":"135","author":"Jo","year":"2022","journal-title":"Anesth Analg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0085","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.bja.2025.03.024","article-title":"The effect of selection bias on the performance of a deep learning-based intraoperative hypotension prediction model using real-world samples from a publicly available database","volume":"135","author":"Yang","year":"2025","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.jclinane.2026.112353_bb0090","article-title":"Prediction of intraoperative hypotension using a multi-head attention model with non-invasive monitoring waveforms","volume":"24","author":"Jeong","year":"2024","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.jclinane.2026.112353_bb0095","first-page":"618","article-title":"Interpretable deep learning model for predicting intraoperative hypotension using arterial blood pressure waveforms","volume":"133","author":"Hwang","year":"2024","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.jclinane.2026.112353_bb0100","article-title":"Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.jclinane.2026.112353_bb0105","doi-asserted-by":"crossref","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","year":"2025","journal-title":"BMJ"},{"key":"10.1016\/j.jclinane.2026.112353_bb0110","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.jclinepi.2014.01.006","article-title":"Applying grading of recommendations assessment, development and evaluation (GRADE) to diagnostic tests was challenging but feasible","volume":"67","author":"Gopalakrishna","year":"2014","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0115","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0120","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1002\/sim.1186","article-title":"Quantifying heterogeneity in a meta-analysis","volume":"21","author":"Higgins","year":"2002","journal-title":"Stat Med"},{"key":"10.1016\/j.jclinane.2026.112353_bb0125","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","year":"2005","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0130","article-title":"Intraoperative hypotension prediction model based on systematic feature engineering and machine learning","volume":"22","author":"Lee","year":"2022","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.jclinane.2026.112353_bb0135","article-title":"Comparative analysis on machine learning and deep learning to predict post-induction hypotension","volume":"20","author":"Lee","year":"2020","journal-title":"Sensors (Basel)"},{"key":"10.1016\/j.jclinane.2026.112353_bb0140","first-page":"14374","article-title":"A composite multi-attention framework for intraoperative hypotension early warning","volume":"37","author":"Lu","year":"2023","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10.1016\/j.jclinane.2026.112353_bb0145","first-page":"853","article-title":"Predicting postinduction hypotension with deep learning using preoperative echocardiographic data","volume":"135","author":"Yoshimura","year":"2022","journal-title":"Anesth Analg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0150","article-title":"A hybrid deep learning model for predicting intraoperative hypotension during non-cardiac surgery","volume":"14","author":"Shim","year":"2025","journal-title":"J Clin Med"},{"issue":"4 Suppl","key":"10.1016\/j.jclinane.2026.112353_bb0155","first-page":"90","article-title":"Machine learning predicting hypotension in operating room: application of deep neural networks on continuous physiologic waveforms for early prediction of intraoperative hypotension","volume":"136","author":"Kim","year":"2023","journal-title":"Anesth Analg"},{"issue":"Suppl","key":"10.1016\/j.jclinane.2026.112353_bb0160","article-title":"Prediction of postinduction hypotension with deep learning","volume":"128","author":"Lee","year":"2019","journal-title":"Anesth Analg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0165","article-title":"Clinical performance of machine-learning algorithms to predict intraoperative hypotension: a meta-analysis","volume":"26","author":"Faraji","year":"2026","journal-title":"BMC Surg"},{"key":"10.1016\/j.jclinane.2026.112353_bb0170","doi-asserted-by":"crossref","DOI":"10.1186\/s12871-025-03250-4","article-title":"Intraoperative hypotension prediction in cardiac and noncardiac procedures: is HPI truly worthwhile? A systematic review and meta-analysis","volume":"25","author":"Shirmohamadi","year":"2025","journal-title":"BMC Anesthesiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0175","doi-asserted-by":"crossref","first-page":"169","DOI":"10.4097\/kja.25249","article-title":"Hypotension prediction index in the prediction of better outcomes: a systematic review and meta-analysis","volume":"79","author":"Liu","year":"2026","journal-title":"Korean J Anesthesiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0180","article-title":"Hypotension prediction index-guided hemodynamic management on postoperative organ outcome: a systematic review and meta-analysis of randomized clinical trials","volume":"20","author":"Wang","year":"2026","journal-title":"A A Pract"},{"key":"10.1016\/j.jclinane.2026.112353_bb0185","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1097\/ALN.0000000000004990","article-title":"Hypotension prediction index is equally effective in predicting intraoperative hypotension during noncardiac surgery compared to a mean arterial pressure threshold: a prospective observational study","volume":"141","author":"Mulder","year":"2024","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0190","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1097\/EJA.0000000000001939","article-title":"CON: the hypotension prediction index is not a validated predictor of hypotension","volume":"41","author":"Vistisen","year":"2024","journal-title":"Eur J Anaesthesiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0195","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1097\/ALN.0000000000004320","article-title":"Performance of the hypotension prediction index may be overestimated due to selection bias","volume":"137","author":"Enevoldsen","year":"2022","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0200","first-page":"1095","article-title":"Comparison of differences in cohort (forward) and case-control (backward) methodologic approaches for validation of the hypotension prediction index","volume":"140","author":"Davies","year":"2024","journal-title":"Anesthesiology"},{"key":"10.1016\/j.jclinane.2026.112353_bb0205","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10877-025-01357-0","article-title":"Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods","volume":"40","author":"Chaari","year":"2026","journal-title":"J Clin Monit Comput"},{"key":"10.1016\/j.jclinane.2026.112353_bb0210","doi-asserted-by":"crossref","DOI":"10.1186\/s12871-025-03336-z","article-title":"Hypotension prediction index smart alerts and trends to improve compliance with goal-directed haemodynamic therapy: a feasibility study","volume":"25","author":"Guerra-Londono","year":"2025","journal-title":"BMC Anesthesiol"},{"key":"10.1016\/j.jclinane.2026.112353_bb0215","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.bja.2025.06.016","article-title":"Predicting arterial pressure without prejudice: towards effective hypotension prediction models","volume":"135","author":"Vistisen","year":"2025","journal-title":"Br J Anaesth"}],"container-title":["Journal of Clinical Anesthesia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952818026002321?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952818026002321?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:10:28Z","timestamp":1792541428000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952818026002321"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":43,"alternative-id":["S0952818026002321"],"URL":"https:\/\/doi.org\/10.1016\/j.jclinane.2026.112353","relation":{},"ISSN":["0952-8180"],"issn-type":[{"value":"0952-8180","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning algorithms for predicting intraoperative hypotension: A systematic review and meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Journal of Clinical Anesthesia","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jclinane.2026.112353","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"112353"}}