{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:09:45Z","timestamp":1791565785398,"version":"4.3.4"},"reference-count":57,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T00:00:00Z","timestamp":1736294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203145Z\/16\/Z"],"award-info":[{"award-number":["203145Z\/16\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["NS\/A000050\/1"],"award-info":[{"award-number":["NS\/A000050\/1"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009360","name":"King's College London","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["associationofanaesthetists-publications.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Anaesthesia"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Understanding 1\u2010year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri\u2010operative care. Few models exist that predict 1\u2010year mortality accurately. This study aimed to develop a predictive model for 1\u2010year mortality in patients undergoing complex non\u2010cardiac surgery using a novel machine\u2010learning technique called multi\u2010objective symbolic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A single\u2010institution database of patients undergoing major elective surgery with previous cardiopulmonary exercise testing was divided into three datasets: pre\u2010operative clinical data; cardiorespiratory and physiological data; and combined. A multi\u2010objective symbolic regression model was developed and compared against existing models. Model performance was evaluated using the F1 score. Shapley additive explanations analysis was used to identify the major contributors to model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 2145 patients in the database, 1190 were included, with 952 in the training dataset and 238 in the test dataset. Median (IQR [range]) age was 71 (61\u201379 [45\u201389]) years and 825 (69%) were male. The multi\u2010objective symbolic regression model demonstrated robust consistency with an F1 score of 0.712. Shapley\u00a0additive explanations analysis indicated that ventilatory equivalents for carbon dioxide, oxygen at peak\u00a0exercise and BMI influenced model performance most significantly, surpassing surgery type and named comorbidities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study confirms the feasibility of developing a multi\u2010objective symbolic regression\u2010based model for predicting 1\u2010year postoperative mortality in a mixed non\u2010cardiac surgical population. The model's strong performance underscores the critical role of physiological data, particularly cardiorespiratory fitness, in surgical risk assessment and emphasises the importance of pre\u2010operative optimisation to identify and manage high\u2010risk patients. The multi\u2010objective symbolic regression model demonstrated high sensitivity and a good F1 score, highlighting its potential as an effective tool for peri\u2010operative risk prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/anae.16538","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T20:19:11Z","timestamp":1736367551000},"page":"551-560","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mortality prediction after major surgery in a mixed population through machine learning: a multi\u2010objective symbolic regression approach"],"prefix":"10.1111","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2080-5274","authenticated-orcid":false,"given":"Pietro","family":"Arina","sequence":"first","affiliation":[{"name":"Bloomsbury Institute of Intensive Care Medicine University College London  London UK"},{"name":"Human Physiology and Performance 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