Cited by 133Cardiac, Anesthesia and Surgical OutcomesArtificial Intelligence in HealthcareMachine Learning in Healthcare

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Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery

Merath, Katiuscha, Hyer, J. Madison, Mehta, Rittal, Farooq, Ayesha, Bagante, Fabio, Sahara, Kota, Tsilimigras, Diamantis I., Beal, Eliza, Paredes, Anghela Z., Wu, Lu, Ejaz, Aslam, Pawlik, Timothy M.·2020·Journal of Gastrointestinal Surgery

DOI 10.1007/s11605-019-04338-2

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Abstract

Background Surgical resection is the only potentially curative treatment for patients with colorectal, liver, and pancreatic cancers. Although these procedures are performed with low mortality, rates of complications remain relatively high following hepatopancreatic and colorectal surgery.Methods The American College of Surgeons (ACS) National Surgical Quality Improvement Program was utilized to identify patients undergoing liver, pancreatic and colorectal surgery from 2014 to 2016. Decision tree models were utilized to predict the occurrence of any complication, as well as specific complications. To assess the variability of the performance of the classification trees, bootstrapping was performed on 50% of the sample.Results Algorithms were derived from a total of 15,657 patients who met inclusion criteria. The algorithm had a good predictive ability for the occurrence of any complication, with a C-statistic of 0.74, outperforming the ASA (C-statistic 0.58) and ACS-Surgical Risk Calculator (C-statistic 0.71). The algorithm was able to predict with high accuracy thirteen out of the seventeen complications analyzed. The best performance was in the prediction of stroke (C-statistic 0.98), followed by wound dehiscence, cardiac arrest, and progressive renal failure (all C-statistic 0.96). The algorithm had a good predictive ability for superficial SSI (C-statistic 0.76), organ space SSI (C-statistic 0.76), sepsis (C-statistic 0.79), and bleeding requiring transfusion (C-statistic 0.79).Conclusion Machine learning was used to develop an algorithm that accurately predicted patient risk of developing complications following liver, pancreatic, or colorectal surgery. The algorithm had very good predictive ability to predict specific complications and demonstrated superiority over other established methods.

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Authors: Merath, K., Hyer, J. M., Mehta, R., Farooq, A., Bagante, F., Sahara, K., Tsilimigras, D. I., Beal, E., Paredes, A. Z., Wu, L., Ejaz, A., & Pawlik, T. M.
Affiliations: The Ohio State University Wexner Medical Center (US)
Corresponding author: Pawlik, Timothy M.
ORCID: Merath, Katiuscha
Hyer, J. Madison
Mehta, Rittal
Farooq, Ayesha
Bagante, Fabio
Sahara, Kota
Tsilimigras, Diamantis I.
Beal, Eliza
Paredes, Anghela Z.
Wu, Lu
Ejaz, Aslam
Pawlik, Timothy M.
Venue: Journal of Gastrointestinal Surgery
Publisher: Elsevier BV
Published: 2020
Volume: 24
Issue: 8
Pages: 1843-1851
ISSN: 1091-255X (ISSN-L)
DOI: 10.1007/s11605-019-04338-2
OpenAlex: W2966316153
Cited by: 131
References: 46
Citations by year: 2026: 13 · 2025: 24 · 2024: 22 · 2023: 19 · 2022: 27 · 2021: 19 · 2020: 7
FWCI: 9.58
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Gastrointestinal Surgery (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords: postoperative complications, surgical risk prediction, decision tree, hepatectomy, pancreatic surgery, colorectal surgery, surgical site infection, postoperative stroke, postoperative bleeding, sepsis
MeSH terms: Machine Learning, Colorectal Surgery, Humans, Liver, Postoperative Complications, Risk Factors, Risk Assessment

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Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery

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