The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.
Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery
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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“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]
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“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]
In the citing paper
“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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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
Dixon, D., Sattar, H., Moros, N., Kesireddy, S. R., Ahsan, H., Lakkimsetti, M., Fatima, M., Doshi, D., Sadhu, K., & Hassan, M. J. (2024) · Cureus · doi:10.7759/cureus.59954 · Cited by 249
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Dhiman, P., Ma, J., Andaur Navarro, C. L., Speich, B., Bullock, G., Damen, J. A. A., Hooft, L., Kirtley, S., Riley, R. D., Van Calster, B., et al. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01577-x · Cited by 140
Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine
Cè, M., Irmici, G., Foschini, C., Danesini, G. M., Falsitta, L. V., Serio, M. L., Fontana, A., Martinenghi, C. M. A., Oliva, G., & Cellina, M. I. (2023) · Current Oncology · doi:10.3390/curroncol30030203 · Cited by 141
Metadata
- 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)
- 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
- 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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