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Published December 2019 | public
Journal Article

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data


Background: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. Methods: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. Results: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955). Conclusions: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

Additional Information

© 2019 British Journal of Anaesthesia. Published by Elsevier Ltd. Accepted 29 July 2019, Available online 15 October 2019. Funding: National Science Foundation (grant number 1705197) to BLH, NR, and EH. National Institute of Mental Health (award number K99MH116115) to LOL. National Institute of Health (grant numbers R00GM111744, R35GM125055), National Science Foundation (grant number III-1705121), an Alfred P. Sloan Research Fellowship, and Okawa Foundation to SS. National Institute of Neurological Disorders and Stroke of the National Institute of Health (award number T32NS048004) and a UCLA QCB Collaboratory Postdoctoral Fellowship directed by Matteo Pellegrini to RB. Bial Foundation to UM. National Science Foundation Graduate Research Fellowship Program (grant number DGE-1650604) to BJ. Authors' contributions Drafted the manuscript: BLH, RB. Performed the experiments: BLH, RB, NR, CL, MC, RJ, BJ, PB. Analysed results: BLH, RB. Drafted the manuscript: RB. Clinical evaluation of results: EG. Set up infrastructure, defined clinical features: EG, IH. Revised the manuscript: LOL, UM. Proposed the problem: AM. Supervised the statistical analysis: SS, EH. Supervised the acquisition of clinical data: IH. Declarations of interest. MC is co-owner of US patent serial no. 61/432,081 for a closed-loop fluid administration system based on the dynamic predictors of fluid responsiveness which has been licensed to Edwards Lifesciences. MC is a consultant for Edwards Lifesciences (Irvine, CA, USA), Medtronic (Boulder, CO, USA), Masimo Corp. (Irvine, CA, USA). MC has received research support from Edwards Lifesciences through his Department and NIH R01 GM117622—Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instability and NIH R01 NR013912—Predicting Patient Instability Noninvasively for Nursing Care—Two (PPINNC-2). IH is the founder and President of Clarity Healthcare Analytics Inc. a company that assists hospitals with extracting and using data from their electronic medical records. IH also receives research funding from Merck Pharmaceuticals. EG is founder and Secretary of Clarity Healthcare Analytics Inc. a company that assists hospitals with extracting and using data from their electronic medical records. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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