*Result*: Augmenting decision making in acute care surgery: A systematic review of machine learning-driven risk prediction models.
Title:
Augmenting decision making in acute care surgery: A systematic review of machine learning-driven risk prediction models.
Authors:
Lee AH; From the Division of General Surgery, Department of Surgery (A.H.L., M.K.C.), University of British Columbia, Vancouver, British Columbia, Canada; Division of General Surgery, Department of Surgery (A.H.L., K.S., A.N., J.D.F., L.M.K., S.M.H.), and Department of Management Science and Engineering (D.N.), Stanford University, Stanford, California., Chan MK, Narayanan D, Staudenmayer K, Nassar A, Forrester JD, Knowlton LM, Hameed SM
Source:
The journal of trauma and acute care surgery [J Trauma Acute Care Surg] 2026 Feb 01; Vol. 100 (2), pp. 332-338. Date of Electronic Publication: 2025 Oct 22.
Publication Type:
Journal Article; Systematic Review
Language:
English
Journal Info:
Publisher: Lippincott, Williams & Wilkins Country of Publication: United States NLM ID: 101570622 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2163-0763 (Electronic) Linking ISSN: 21630755 NLM ISO Abbreviation: J Trauma Acute Care Surg Subsets: MEDLINE
Imprint Name(s):
Original Publication: Hagerstown, MD : Lippincott, Williams & Wilkins
MeSH Terms:
References:
Ball CG, Hameed SM, Brenneman FD. Acute care surgery: a new strategy for the general surgery patients left behind. Can J Surg . 2010;53(2):84.
Use of National Burden to Define Operative Emergency General Surgery | Surgery | JAMA Surgery | JAMA Network. July 9, 2024. Available at: https://jamanetwork.com/journals/jamasurgery/article-abstract/2516780 . Accessed July 9, 2024.
Havens JM, Peetz AB, Do WS, et al. The excess morbidity and mortality of emergency general surgery. J Trauma Acute Care Surg . 2015;78(2):306.
Havens JM, Columbus AB, Seshadri AJ, et al. Risk stratification tools in emergency general surgery. Trauma Surg Acute Care Open . 2018;3(1):e000160.
Vernooij JEM, Koning NJ, Geurts JW, et al. Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review. Anaesthesia . 2023;78(5):607–619.
Hyder JA, Reznor G, Wakeam E, Nguyen LL, Lipsitz SR, Havens JM. Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP. Ann Surg . 2016;264(6):959.
Varghese C, Harrison EM, O’Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med . 2024;30(5):1257–1268.
El-Menyar A, Naduvilekandy M, Asim M, Rizoli S, Al-Thani H. Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission. Comput Biol Med . 2024;179:108880.
Chiew CJ, Liu N, Wong TH, Sim YE, Abdullah HR. Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Ann Surg . 2020;272(6):1133–1139.
Finlayson SG, Beam AL, van Smeden M. Machine learning and statistics in clinical research articles—moving past the false dichotomy. JAMA Pediatr . 2023;177(5):448–450.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ . 2021;372:n71.
What Is Logistic Regression? IBM. August 16, 2021. Available at: https://www.ibm.com/think/topics/logistic-regression . Accessed April 2, 2025.
Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med . 2019;170(1):W1.
Eickhoff RM, Bulla A, Eickhoff SB, et al. Machine learning prediction model for postoperative outcome after perforated appendicitis. Langenbecks Arch Surg . 2022;407(2):789–795.
Harmantepe AT. A method for predicting mortality in acute mesenteric ischemia: machine learning. Turk J Trauma Emerg Surg . 2024;487–492. doi:10.14744/tjtes.2024.48074. (PMID: 10.14744/tjtes.2024.48074)
Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator. Ann Surg . 2018;268(4):574–583.
Gao J, Merchant AM. A machine learning approach in predicting mortality following emergency general surgery. Am Surg . 2021;87(9):1379–1385.
Chen B, Sheng W, Wu Z, et al. Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study. Int J Surg . 2024;110(6):3527–3535.
Chai C, Peng SZ, Zhang R, Li CW, Zhao Y. Advancing emergency department triage prediction with machine learning to optimize triage for abdominal pain surgery patients. Surg Innov . 2024;31(6):583–597.
Parreco J, Hidalgo A, Kozol R, Namias N, Rattan R. Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. Am Surg . 2018;84(7):1190–1194.
Gao J, Chen G, O’Rourke AP, et al. Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models. J Am Med Inform Assoc . 2024;31(6):1291–1302.
Kulshrestha S, Dligach D, Joyce C, et al. Comparison and interpretability of machine learning models to predict severity of chest injury. JAMIA Open . 2021;4(1):ooab015.
Katz S, Suijker J, Hardt C, et al. Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections. Int J Med Inform . 2022;167:104878.
Hodgman E, Cripps M, Mina M, et al. External validation of a smartphone app model to predict the need for massive transfusion using five different definitions. J Trauma Acute Care Surg . 2018;84(2):397–402.
Maurer LR, Bertsimas D, Bouardi HT, et al. Trauma outcome predictor: an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients. J Trauma Acute Care Surg . 2021;91(1):93–99.
Nederpelt CJ, Mokhtari AK, Alser O, et al. Development of a field artificial intelligence triage tool: confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg . 2021;90(6):1054–1060.
Schetinin V, Jakaite L, Krzanowski W. Bayesian averaging over decision tree models for trauma severity scoring. Artif Intell Med . 2018;84:139–145.
Fransvea P, Fransvea G, Liuzzi P, Sganga G, Mannini A, Costa G. Study and validation of an explainable machine learning–based mortality prediction following emergency surgery in the elderly: a prospective observational study. Int J Surg . 2022;107:106954.
Ribeiro Junior MAF, Smaniotto R, Gebran A, et al. O uso do aplicativo POTTER (Predictive Optimal Trees in Emergency Surgery Risk) para prever mortalidade e complicações em pacientes submetidos à cirurgia de emergência. Rev Colégio Bras Cir . 2023;50:e20233624.
El Hechi MW, Maurer LR, Levine J, et al. Validation of the artificial intelligence-based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) calculator in emergency general surgery and emergency laparotomy patients. J Am Coll Surg . 2021;232(6):912–919e1.
El Hechi M, Gebran A, Bouardi HT, et al. Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older. Surgery . 2022;171(6):1687–1694.
Maurer LR, Chetlur P, Zhuo D, et al. Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator in patients 65 years and older. Ann Surg . 2023;277(1):e8–e15.
El Moheb M, Gebran A, Maurer LR, et al. Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery. J Trauma Acute Care Surg . 2023;95(4):565.
Goh E, Gallo R, Hom J, et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open . 2024;7(10):e2440969.
Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Cherubini A. Experimental evidence of effective human–AI collaboration in medical decision-making. Sci Rep . 2022;12(1):14952.
Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med . 2021;181(8):1065–1070.
Ghaferi AA, Wells EE. Improving postoperative rescue through a multifaceted approach. Surg Clin North Am . 2021;101(1):71–80.
Wells CI, Bhat S, Xu W, et al. Variation in the definition of ‘failure to rescue’ from postoperative complications: a systematic review and recommendations for outcome reporting. Surgery . 2024;175(4):1103–1110.
Rivera SC, Liu X, Hughes SE, et al. Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies. Lancet Digit Health . 2023;5(3):e168–e173.
Pierson E, Cutler DM, Leskovec J, Mullainathan S, Obermeyer Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat Med . 2021;27(1):136–140.
Choi J, Vendrow EB, Moor M, Spain DA. Development and validation of a model to quantify injury severity in real time. JAMA Netw Open . 2023;6(10):e2336196.
Callahan A, McElfresh D, Banda JM, et al. Standing on FURM ground: a framework for evaluating fair, useful, and reliable AI models in health care systems. NEJM Catal . 2024;5(10):CAT.24.0131.
Use of National Burden to Define Operative Emergency General Surgery | Surgery | JAMA Surgery | JAMA Network. July 9, 2024. Available at: https://jamanetwork.com/journals/jamasurgery/article-abstract/2516780 . Accessed July 9, 2024.
Havens JM, Peetz AB, Do WS, et al. The excess morbidity and mortality of emergency general surgery. J Trauma Acute Care Surg . 2015;78(2):306.
Havens JM, Columbus AB, Seshadri AJ, et al. Risk stratification tools in emergency general surgery. Trauma Surg Acute Care Open . 2018;3(1):e000160.
Vernooij JEM, Koning NJ, Geurts JW, et al. Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review. Anaesthesia . 2023;78(5):607–619.
Hyder JA, Reznor G, Wakeam E, Nguyen LL, Lipsitz SR, Havens JM. Risk prediction accuracy differs for emergency versus elective cases in the ACS-NSQIP. Ann Surg . 2016;264(6):959.
Varghese C, Harrison EM, O’Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med . 2024;30(5):1257–1268.
El-Menyar A, Naduvilekandy M, Asim M, Rizoli S, Al-Thani H. Machine learning models predict triage levels, massive transfusion protocol activation, and mortality in trauma utilizing patients hemodynamics on admission. Comput Biol Med . 2024;179:108880.
Chiew CJ, Liu N, Wong TH, Sim YE, Abdullah HR. Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission. Ann Surg . 2020;272(6):1133–1139.
Finlayson SG, Beam AL, van Smeden M. Machine learning and statistics in clinical research articles—moving past the false dichotomy. JAMA Pediatr . 2023;177(5):448–450.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ . 2021;372:n71.
What Is Logistic Regression? IBM. August 16, 2021. Available at: https://www.ibm.com/think/topics/logistic-regression . Accessed April 2, 2025.
Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med . 2019;170(1):W1.
Eickhoff RM, Bulla A, Eickhoff SB, et al. Machine learning prediction model for postoperative outcome after perforated appendicitis. Langenbecks Arch Surg . 2022;407(2):789–795.
Harmantepe AT. A method for predicting mortality in acute mesenteric ischemia: machine learning. Turk J Trauma Emerg Surg . 2024;487–492. doi:10.14744/tjtes.2024.48074. (PMID: 10.14744/tjtes.2024.48074)
Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator. Ann Surg . 2018;268(4):574–583.
Gao J, Merchant AM. A machine learning approach in predicting mortality following emergency general surgery. Am Surg . 2021;87(9):1379–1385.
Chen B, Sheng W, Wu Z, et al. Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study. Int J Surg . 2024;110(6):3527–3535.
Chai C, Peng SZ, Zhang R, Li CW, Zhao Y. Advancing emergency department triage prediction with machine learning to optimize triage for abdominal pain surgery patients. Surg Innov . 2024;31(6):583–597.
Parreco J, Hidalgo A, Kozol R, Namias N, Rattan R. Predicting mortality in the surgical intensive care unit using artificial intelligence and natural language processing of physician documentation. Am Surg . 2018;84(7):1190–1194.
Gao J, Chen G, O’Rourke AP, et al. Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models. J Am Med Inform Assoc . 2024;31(6):1291–1302.
Kulshrestha S, Dligach D, Joyce C, et al. Comparison and interpretability of machine learning models to predict severity of chest injury. JAMIA Open . 2021;4(1):ooab015.
Katz S, Suijker J, Hardt C, et al. Decision support system and outcome prediction in a cohort of patients with necrotizing soft-tissue infections. Int J Med Inform . 2022;167:104878.
Hodgman E, Cripps M, Mina M, et al. External validation of a smartphone app model to predict the need for massive transfusion using five different definitions. J Trauma Acute Care Surg . 2018;84(2):397–402.
Maurer LR, Bertsimas D, Bouardi HT, et al. Trauma outcome predictor: an artificial intelligence interactive smartphone tool to predict outcomes in trauma patients. J Trauma Acute Care Surg . 2021;91(1):93–99.
Nederpelt CJ, Mokhtari AK, Alser O, et al. Development of a field artificial intelligence triage tool: confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds. J Trauma Acute Care Surg . 2021;90(6):1054–1060.
Schetinin V, Jakaite L, Krzanowski W. Bayesian averaging over decision tree models for trauma severity scoring. Artif Intell Med . 2018;84:139–145.
Fransvea P, Fransvea G, Liuzzi P, Sganga G, Mannini A, Costa G. Study and validation of an explainable machine learning–based mortality prediction following emergency surgery in the elderly: a prospective observational study. Int J Surg . 2022;107:106954.
Ribeiro Junior MAF, Smaniotto R, Gebran A, et al. O uso do aplicativo POTTER (Predictive Optimal Trees in Emergency Surgery Risk) para prever mortalidade e complicações em pacientes submetidos à cirurgia de emergência. Rev Colégio Bras Cir . 2023;50:e20233624.
El Hechi MW, Maurer LR, Levine J, et al. Validation of the artificial intelligence-based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) calculator in emergency general surgery and emergency laparotomy patients. J Am Coll Surg . 2021;232(6):912–919e1.
El Hechi M, Gebran A, Bouardi HT, et al. Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older. Surgery . 2022;171(6):1687–1694.
Maurer LR, Chetlur P, Zhuo D, et al. Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) calculator in patients 65 years and older. Ann Surg . 2023;277(1):e8–e15.
El Moheb M, Gebran A, Maurer LR, et al. Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery. J Trauma Acute Care Surg . 2023;95(4):565.
Goh E, Gallo R, Hom J, et al. Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Netw Open . 2024;7(10):e2440969.
Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Cherubini A. Experimental evidence of effective human–AI collaboration in medical decision-making. Sci Rep . 2022;12(1):14952.
Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med . 2021;181(8):1065–1070.
Ghaferi AA, Wells EE. Improving postoperative rescue through a multifaceted approach. Surg Clin North Am . 2021;101(1):71–80.
Wells CI, Bhat S, Xu W, et al. Variation in the definition of ‘failure to rescue’ from postoperative complications: a systematic review and recommendations for outcome reporting. Surgery . 2024;175(4):1103–1110.
Rivera SC, Liu X, Hughes SE, et al. Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies. Lancet Digit Health . 2023;5(3):e168–e173.
Pierson E, Cutler DM, Leskovec J, Mullainathan S, Obermeyer Z. An algorithmic approach to reducing unexplained pain disparities in underserved populations. Nat Med . 2021;27(1):136–140.
Choi J, Vendrow EB, Moor M, Spain DA. Development and validation of a model to quantify injury severity in real time. JAMA Netw Open . 2023;6(10):e2336196.
Callahan A, McElfresh D, Banda JM, et al. Standing on FURM ground: a framework for evaluating fair, useful, and reliable AI models in health care systems. NEJM Catal . 2024;5(10):CAT.24.0131.
Contributed Indexing:
Keywords: Machine learning; acute care surgery; artificial intelligence; prediction; risk
Entry Date(s):
Date Created: 20251106 Date Completed: 20260127 Latest Revision: 20260309
Update Code:
20260310
DOI:
10.1097/TA.0000000000004805
PMID:
41196221
Database:
MEDLINE
*Further Information*
*Background: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning (ML) may improve risk prediction and decision making in this setting, its development, validation, and implementation in ACS remain understudied. We therefore evaluated the techniques, predictor features, and outcomes used in ML-driven risk prediction models in ACS and generated recommendations to inform future research and support clinically meaningful implementation.
Methods: A systematic review of ML-driven predictive models in ACS (emergency general surgery, surgical critical care, trauma) was conducted. Models were analyzed by predictor features, outcomes, algorithms, and performance. The best-performing models for the most commonly predicted outcome were identified.
Results: Of 52 studies, 57.7% focused on trauma populations. Most models used registry data (76.8%), fewer used electronic health records (28.8%), and only five studies performed external validation after model development. Common algorithms included logistic regression (44.2%), random forest (34.6%), and decision trees (26.9%). Mortality (59.6%), complications (30.8%), and triage/severity (15.4%) were the most frequent outcomes; patient-centered/reported outcomes were absent. Features commonly included demographics, physiologic scores, and vital signs, while imaging and intraoperative data were underused. Natural language processing was used in four studies. Model performance was typically assessed using area under the receiver operating characteristic curve (88.5%), with support vector machines demonstrating the highest performance. Machine learning models generally outperformed conventional risk scores among 11 comparative studies.
Conclusion: Machine learning-driven predictive models in ACS show promising performance but are constrained by limited methodological rigor, real-world validation, and substantial heterogeneity in features, outcomes, and algorithms, challenging systematic adoption and oversight. A grounded understanding of ACS decision making workflows and their postimplementation impact may ensure clinically relevant, seamless, and safe integration of ML-based risk prediction.
Level of Evidence: Systematic Review Without Meta-analysis; Level IV.
(Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)*