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

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 +4 more·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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Crossref · 10.1007/s11605-019-04338-2

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  • Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated.

    “Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16, 20–25).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [21]

  • ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.

    “These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [21]

  • Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.

    “Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [21]

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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
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
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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