[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119937-en":3,"doc-seo-119937-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119937,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Prediction of Complications and Prognostication in Perioperative Medicine - A Systematic Review and PROBAST Assessment of Machine Learning Tools","Perioperative medicine faces a growing need to accurately identify surgical patients at risk of complications as postoperative mortality and morbidity remain substantial. Over the past decade, predictive tools using artificial intelligence and machine learning have emerged, offering opportunities for improved risk prediction and personalized care, but raising concerns about bias, interpretability, and reproducibility. This systematic review evaluates prognostic AI/ML models using PROBAST and assesses readiness levels, summarizing evidence quality and gaps in external validation.","PerioPerative Medicine  \nPrediction of Complications and Prognostication in Perioperative Medicine:  \nA Systematic Review and PROBAST Assessment of Machine Learning Tools  \nPietro Arina, M. D. , Maciej R. Kaczorek, B. E. , Daniel A. Hofmaenner, M. D. , Walter Pisciotta, M. D. , Patricia Refinetti, M. D. , Mervyn Singer, F. R. C. P. , Evangelos B. Mazomenos, Ph. D. , John Whittle, M. D. Anesthesiology 2024; 140:85–101  \neditor’S PerSPective  \nWhat We already Know about This Topic  \n• Artificial intelligence and machine learning may offer a novel approach to better predict perioperative outcomes.  \nWhat This article Tells us That Is New  \n• This systematic review and meta-analysis identified 103 studies  \nthat employed artificial intelligence or machine learning to predict perioperative outcomes, but the overall quality was only modest with only 13% being externally validated. The authors conclude that the artificial intelligence and machine learning may hold great promise but are not ready for prime time.  \nPerioperative  \nthat focuses  \nmedicine is a multidisciplinary specialty on meeting the complex medical needs  \nof patients at risk of complications from surgery. With the number of surgical operations worldwide expected to rise  \nCollege London ( ucl) / England  \nThis article is featured in “This Month in Anesthesiology,” page A1 . Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are available in both the HTML and PDF versions of this article. Links to the digital files are provided in the HTML text of this article on the Journal’s Web site ([www.anesthesiology.org](www.anesthesiology.org)) .  \nM.S. , E. B. M. , and J.W. contributed equally to this work.  \nSubmitted for publication June 22, 2023. Accepted for publication September 11, 2023. Published online first on November 9, 2023.  \nPietro Arina, M. D.: Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom.  \nMaciej R. Kaczorek, B. E.: Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.  \nDaniel A. Hofmaenner, M. D.: Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland  \nWalter Pisciotta, M. D.: Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.  \nCopyright © 2023 The Author(s) . Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Anesthesiologists. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Anesthesiology 2024; 140:85–101. DOI: 10. 1097/ALN.0000000000004764  \nThe article processing charge was funded by University College London.  \nuser on 0 1 May 2024  \nAnesthesiology , V 140 • NO 1 JaNuary 2024 85  \nPerioPerative Medicine  \nto 500 million by the end of the 21st century,1,2 there is a growing need to accurately identify patients at risk and to manage potential complications. The incidence of postoperative mortality ranges from 1.7 to 5.7%3–6 and accounts for 7.7% of the global burden of death.9 Postoperative morbidity represents a major issue, with 16% of patients developing serious complications.5,7,8 This can affect both quality and length of life, placing a significant burden on individuals, families, and the healthcare system.9–14  \nOver the last 10 yr, there has been an emergence of novel predictive tools for perioperative outcomes driven by artificial intelligence and machine learning techniques. These tools offer exciting opportuni","cbCaipY1Wvl8alc4","https://ap.wps.com/l/cbCaipY1Wvl8alc4","pdf",870014,1,17,"English","en",105,"# Editor’s Perspective\n## What We already Know about This Topic\n## What This article Tells us That Is New\n# Background and Rationale\n# Approach and Evaluation Framework\n## Risk of Bias Assessment (PROBAST)\n## Readiness-Level Classification\n# Scope and Knowledge Gaps","[{\"question\":\"Why is accurate prediction of perioperative outcomes important?\",\"answer\":\"Postoperative mortality and serious complications remain common, affecting quality of life and creating a substantial burden on patients, families, and healthcare systems.\"},{\"question\":\"What promise do machine learning tools offer in perioperative medicine?\",\"answer\":\"Machine learning can learn from large complex datasets to improve risk prediction and potentially support more personalized treatment planning.\"},{\"question\":\"How does the study evaluate bias and readiness of AI/ML prediction tools?\",\"answer\":\"Bias is assessed using PROBAST, and the review applies an additional classification to determine the readiness level of the reported machine learning algorithms.\"}]","Prediction of Complications and Prognostication in Perioperative Medicine - 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