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An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data
Open accessPublished version of a preprint
Abstract
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.
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“Another advantage is the promise that the use of ML in medicine will facilitate an understanding of what features drive outcomes (26).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [26]
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XGBoost: A Scalable Tree Boosting System
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SMOTE synthetic minority over-sampling technique
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XGBoost: A Scalable Tree Boosting System
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Validation of a combined comorbidity index
Charlson, M., Szatrowski, T. P., Peterson, J., & Gold, J. (1994) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(94)90129-5 · Cited by 6,587
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Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
“Another advantage is the promise that the use of ML in medicine will facilitate an understanding of what features drive outcomes (26).”
BackgroundMachine Learning and Surgical Outcomes Prediction: A Systematic Review
Elfanagely, O., Toyoda, Y., Othman, S., Mellia, J. A., Basta, M. N., Liu, T., Kording, K. P., Ungar, L., & Fischer, J. P. (2021) · Journal of Surgical Research · doi:10.1016/j.jss.2021.02.045 · Cited by 126
Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication
Chung, P., Fong, C. T., Walters, A. M., Aghaeepour, N., Yetişgen, M., & O’Reilly-Shah, V. N. (2024) · JAMA Surgery · doi:10.1001/jamasurg.2024.1621 · Cited by 134
Klug, M., Barash, Y., Bechler, S., Resheff, Y. S., Tron, T., Ironi, A., Soffer, S., Zimlichman, E., & Klang, E. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05512-7 · Cited by 120
Bishara, A. M., Chiu, C., Whitlock, E. L., Douglas, V. C., Lee, S., Butte, A. J., Leung, J. M., & Donovan, A. L. (2022) · BMC Anesthesiology · doi:10.1186/s12871-021-01543-y · Cited by 118
“…prediction models have been developed to predict delirium in hospitalized patients [17], postoperative delirium in focused patient populations [18], non-delirium-related intraoperative complications [19, 20], and postoperative mortality [21], in addition to applications in many other contexts [22].”
Background
Metadata
- Authors
- Hill, B. L., Brown, R., Gabel, E., Rakocz, N., Lee, C., Cannesson, M., Baldi, P., Olde Loohuis, L., Johnson, R., Jew, B., Maoz, U., Mahajan, A., Sankararaman, S., Hofer, I., & Halperin, E.
- Affiliations
- University of California, Los Angeles (US)
- University of California, Irvine (US)
- California Institute of Technology (US)
- University of Pittsburgh (US)
- Corresponding author
- Hofer, Ira
- ORCID
- Venue
- British Journal of Anaesthesia
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 123
- Issue
- 6
- Pages
- 877-886
- ISSN
- 0007-0912 (ISSN-L)
- PMC
- PMC6883494
- OpenAlex
- W2980769368
- Cited by
- 103
- References
- 39
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 19 · 2023: 13 · 2022: 21 · 2021: 12 · 2020: 7 · 2019: 1
- FWCI
- 7.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- British Journal of Anaesthesia (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion, cc-by-nc-nd)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Chapman University Digital Commons (Chapman University) (submittedVersion)
- Funders
- National Institute of Mental Health (K99MH116115)
- University of California, Los Angeles (DGE- 1650604)
- National Institute of Neurological Disorders and Stroke (T32NS048004)
- National Institutes of Health (III-1705121, R01 NR013912, R01 GM117622, R00GM111744, R35GM125055)
- Alfred P. Sloan Foundation
- National Science Foundation (III-1705121, 1705197)
- Masimo
- Edwards Lifesciences
- Okawa Foundation for Information and Telecommunications
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- postoperative mortality prediction, preoperative risk assessment, electronic health record data, random forest, general anesthesia, surgical patients, ASA physical status, Charlson Comorbidity Index
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Aged, 80 and over, California, Female, Health Status, Humans, Male, Middle Aged, Postoperative Complications, Risk Factors, Comorbidity, Hospital Mortality, Risk Assessment, Young Adult, Preoperative Period, Electronic Health Records
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