{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T09:36:42Z","timestamp":1792661802957,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Major spine surgery carries an inherent risk of hemodynamic instability due to prone positioning, significant blood loss, and the strict necessity to maintain adequate spinal cord perfusion. Hemodynamic management mainly relies on a reactive approach, treating hypotension only after it occurs, which increases the risk of postoperative complications such as acute kidney injury and ischemic events. This narrative review evaluates the clinical impact, current evidence, and future perspectives of integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into perioperative hemodynamic care. Methods: A comprehensive literature search was conducted through PubMed, EMBASE, and the Cochrane Library, spanning from inception to January 2026. The search strategy employed combinations of Medical Subject Headings terms and keywords related to \u201cArtificial Intelligence,\u201d \u201cMachine Learning,\u201d \u201cHypotension Prediction Index,\u201d \u201chemodynamic monitoring,\u201d and \u201cmajor spine surgery.\u201d Studies were selected based on their relevance to predictive hemodynamic algorithms, goal-directed fluid therapy (GDFT), and automated closed-loop systems within the perioperative setting of complex spinal interventions. Results: Five studies show that AI\/ML tools can improve hemodynamic management in spine surgery: an hypotension prediction index (HPI)-guided algorithm reduced intraoperative hypotension during prone spinal fusion; a machine learning model accurately predicted massive blood loss in metastatic spinal disease; an AutoML framework linked intraoperative hypertension to worse neurological recovery after spinal cord injury (SCI); a case report showed HPI-guided goal-directed therapy enabled safe, transfusion-free major spine surgery; and topological network analysis identified a narrow optimal mean arterial pressure (MAP) range for neurological recovery after SCI. Collectively, these preliminary findings suggest a potential role for AI\/ML in reducing hemodynamic instability and enabling more individualized perioperative management in spine surgery. Rather than converging on a single verdict, these five studies fall into three distinct evidentiary categories when appraised using a structured model-validation (V1\u2013V4) and clinical-translation (T0\u2013T4) framework applied within each category: a real-time monitoring technology (HPI) with a substantial extra-spinal evidence base but a limited spine-specific replication record; a single, externally validated but clinically unproven preoperative prediction model; and two retrospective, hypothesis-generating discovery frameworks that remain exploratory irrespective of surgical domain. Conclusions: The evidence identified does not support a single, unified statement about \u201cAI\/ML in spine surgery.\u201d Instead, it points to three distinct situations that warrant separate research priorities: consolidating spine-specific replication of an otherwise mature monitoring technology (HPI); externally confirming the clinical utility, rather than only the discriminative accuracy, of a single preoperative prediction model; and prospectively testing the retrospectively derived targets generated by discovery-oriented analytic frameworks. Considered together, these findings should inform hypothesis-driven research design rather than a single implementation-readiness judgment.<\/jats:p>","DOI":"10.3390\/jcm15176915","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:12:25Z","timestamp":1788783145000},"page":"6915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Predictive Paradigm in Perioperative Hemodynamic Management: The Role of Artificial Intelligence in Major Spine Surgery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1480-1573","authenticated-orcid":false,"given":"Gianluigi","family":"Cosenza","sequence":"first","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7263-0229","authenticated-orcid":false,"given":"Marco","family":"Fiore","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8175-7129","authenticated-orcid":false,"given":"Roberto","family":"Giurazza","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"},{"name":"Department of Critical Care, AORN Ospedali dei Colli, Via Leonardo Bianchi, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-3388","authenticated-orcid":false,"given":"Vincenzo","family":"Pota","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1362-7393","authenticated-orcid":false,"given":"Francesco","family":"Coppolino","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0873-3586","authenticated-orcid":false,"given":"Pasquale","family":"Sansone","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Caterina","family":"Pace","sequence":"additional","affiliation":[{"name":"Department of Women, Child and General and Specialized Surgery, University of Campania \u201cLuigi Vanvitelli\u201d, 80138 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s40140-023-00564-2","article-title":"The Effect of Intraoperative Hypotension on Postoperative Renal Function","volume":"13","author":"Kim","year":"2023","journal-title":"Curr. Anesthesiol. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1097\/ALN.0000000000003048","article-title":"Associations of Intraoperative Radial Arterial Systolic, Diastolic, Mean, and Pulse Pressures with Myocardial and Acute Kidney Injury after Noncardiac Surgery","volume":"132","author":"Ahuja","year":"2020","journal-title":"Anesthesiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109645","DOI":"10.4254\/wjh.v17.i11.109645","article-title":"Hypertransaminasemia in non-cirrhotic critically-ill patients","volume":"17","author":"Fiore","year":"2025","journal-title":"World J. Hepatol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.bja.2019.01.013","article-title":"Perioperative Quality Initiative consensus statement on intraoperative blood pressure, risk and outcomes for elective surgery","volume":"122","author":"Sessler","year":"2019","journal-title":"Br. J. Anaesth."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1097\/ALN.0000000000004744","article-title":"Perioperative Management for Complex Spine Fusion Surgery","volume":"140","author":"Carabini","year":"2024","journal-title":"Anesthesiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1093\/bja\/aem380","article-title":"Anaesthesia in the prone position","volume":"100","author":"Edgcombe","year":"2008","journal-title":"Br. J. Anaesth."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"292","DOI":"10.9738\/INTSURG-D-13-00256.1","article-title":"The Prone Position During Surgery and its Complications: A Systematic Review and Evidence-Based Guidelines","volume":"100","author":"Kwee","year":"2015","journal-title":"Int. Surg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.bjae.2020.04.006","article-title":"Anaesthesia, analgesia, and the surgical stress response","volume":"20","author":"Cusack","year":"2020","journal-title":"BJA Educ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1213\/ANE.0000000000005175","article-title":"Systemic Inflammatory Response Syndrome After Surgery: Mechanisms and Protection","volume":"131","author":"Margraf","year":"2020","journal-title":"Anesth. Analg."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kukralova, L., Dostalova, V., Cihlo, M., Kraus, J., and Dostal, P. (2022). The Impact of Individualized Hemodynamic Management on Intraoperative Fluid Balance and Hemodynamic Interventions during Spine Surgery in the Prone Position: A Prospective Randomized Trial. Medicina, 58.","DOI":"10.3390\/medicina58111683"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.bja.2024.03.040","article-title":"Cardiac index-guided therapy to maintain optimised postinduction cardiac index in high-risk patients having major open abdominal surgery: The multicentre randomised iPEGASUS trial","volume":"133","author":"Funcke","year":"2024","journal-title":"Br. J. Anaesth."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2356645","DOI":"10.1080\/07853890.2024.2356645","article-title":"Hemodynamic changes in the prone position according to fluid loading after anaesthesia induction in patients undergoing lumbar spine surgery: A randomized, assessor-blind, prospective study","volume":"56","author":"Lee","year":"2024","journal-title":"Ann. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vir\u00e1g, M., Rottler, M., Gede, N., Ocskay, K., Leiner, T., Tuba, M., \u00c1brah\u00e1m, S., Farkas, N., Hegyi, P., and Moln\u00e1r, Z. (2022). Goal-Directed Fluid Therapy Enhances Gastrointestinal Recovery after Laparoscopic Surgery: A Systematic Review and Meta-Analysis. J. Pers. Med., 12.","DOI":"10.3390\/jpm12050734"},{"key":"ref_14","first-page":"CD012767","article-title":"Perioperative restrictive versus goal-directed fluid therapy for adults undergoing major non-cardiac surgery","volume":"12","author":"Wrzosek","year":"2019","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111348","DOI":"10.1016\/j.jclinane.2023.111348","article-title":"Effect of using hypotension prediction index versus conventional goal-directed haemodynamic management to reduce intraoperative hypotension in non-cardiac surgery: A randomised controlled trial","volume":"93","author":"Yoshikawa","year":"2024","journal-title":"J. Clin. Anesth."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1097\/EJA.0000000000002211","article-title":"Intraoperative hypotension during robotic-assisted radical prostatectomy","volume":"42","author":"Cotoia","year":"2025","journal-title":"Eur. J. Anaesthesiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1213\/ANE.0000000000007074","article-title":"Intraoperative Hypotension in Patients Having Major Noncardiac Surgery Under General Anesthesia: A Systematic Review of Blood Pressure Optimization Strategies","volume":"141","author":"Lee","year":"2025","journal-title":"Anesth. Analg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1097\/ALN.0000000000005355","article-title":"Hemodynamic Management Guided by the Hypotension Prediction Index in Abdominal Surgery: A Multicenter Randomized Clinical Trial","volume":"142","author":"Aldecoa","year":"2025","journal-title":"Anesthesiology"},{"key":"ref_19","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":"ref_20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s41073-019-0064-8","article-title":"SANRA\u2014A scale for the quality assessment of narrative review articles","volume":"4","author":"Baethge","year":"2019","journal-title":"Res. Integr. Peer Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s10877-025-01303-0","article-title":"Effectiveness of hypotension prediction index software in reducing intraoperative hypotension in prolonged prone-position spine surgery: A single-center clinical trial","volume":"39","author":"Depaskouale","year":"2025","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1186\/s13256-022-03653-8","article-title":"The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah\u2019s Witness: A case report","volume":"16","author":"Denn","year":"2022","journal-title":"J. Med. Case Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.spinee.2023.09.001","article-title":"Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques","volume":"24","author":"Shi","year":"2024","journal-title":"Spine J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chou, A., Torres-Espin, A., Kyritsis, N., Huie, J.R., Khatry, S., Funk, J., Hay, J., Lofgreen, A., Shah, R., and McCann, C. (2022). Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265254"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e68015","DOI":"10.7554\/eLife.68015","article-title":"Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury","volume":"10","author":"Haefeli","year":"2021","journal-title":"eLife"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e49257","DOI":"10.1097\/MD.0000000000049257","article-title":"Hypotension prediction index in surgical patients: A systematic review and meta-analysis of randomized controlled trials","volume":"105","author":"Xie","year":"2026","journal-title":"Medicine"},{"key":"ref_27","first-page":"844649","article-title":"Effectiveness of the hypotension prediction index in non-cardiac surgeries: A systematic review, meta-analysis and trial sequential analysis","volume":"75","author":"Felippe","year":"2025","journal-title":"Braz. J. Anesthesiol. (Engl. Ed.)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shirmohamadi, E., Dolama, R.H., Mohammadzadeh, N., Ebrahimi, N., and Ghasemloo, N. (2025). Intraoperative hypotension prediction in cardiac and noncardiac procedures: Is HPI truly worthwhile? A systematic review and meta-analysis. BMC Anesthesiol., 25.","DOI":"10.1186\/s12871-025-03250-4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1016\/j.spinee.2025.03.018","article-title":"External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: A multi-institutional study using 880 patients","volume":"25","author":"Kuijten","year":"2025","journal-title":"Spine J."},{"key":"ref_30","first-page":"100082","article-title":"Transparency in the Reporting of Artificial Intelligence\u2013The TITAN Guideline","volume":"10","author":"Agha","year":"2025","journal-title":"Prem. J. Sci."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/17\/6915\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T12:42:00Z","timestamp":1788784920000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/17\/6915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":30,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jcm15176915"],"URL":"https:\/\/doi.org\/10.3390\/jcm15176915","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]}}}