• Ran Liu, Tom A D Stone, Praachi Raje, Rory V Mather, Laura A Santa Cruz Mercado, Kishore Bharadwaj, Jasmine Johnson, Masaya Higuchi, Ryan D Nipp, Hiroko Kunitake, and Patrick L Purdon.
• Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
• Br J Anaesth. 2024 Mar 1; 132 (3): 607615607-615.

BackgroundPreoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.MethodsWe conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.ResultsOur method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.ConclusionsFLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

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