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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
An Introduction to Statistical Learning: with Applications in R
James, G. M., Witten, D., Hastie, T. J., & Tibshirani, R. J. (2018) · doi:10.25334/q4ht55 · Cited by 2,913
Bilimoria, K., Liu, Y., Paruch, J., Zhou, L., Kmiecik, T. E., Ko, C. Y., & Cohen, M. E. (2013) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2013.07.385 · Cited by 1,854
POSSUM: A scoring system for surgical audit
Copeland, G. P., Jones, D., & Walters, M. (1991) · British journal of surgery · doi:10.1002/bjs.1800780327 · Cited by 1,716
The Department of Veterans Affairs' NSQIP
Khuri, S. F., Daley, J., Henderson, W. G., Hur, K., Demakis, J. G., Aust, J. B., Chong, V., Fabri, P. J., Gibbs, J. O., Grover, F. L., et al. (1998) · Annals of Surgery · doi:10.1097/00000658-199810000-00006 · Cited by 1,543
Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations
Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1702071 · Cited by 1,171
Complications, Failure to Rescue, and Mortality With Major Inpatient Surgery in Medicare Patients
Ghaferi, A. A., Birkmeyer, J. D., & Dimick, J. B. (2009) · Annals of Surgery · doi:10.1097/sla.0b013e3181bef697 · Cited by 809
Hall, B. L., Hamilton, B. H., Richards, K., Bilimoria, K. Y., Cohen, M. E., & Ko, C. Y. (2009) · Annals of Surgery · doi:10.1097/sla.0b013e3181b4148f · Cited by 754
The National Surgical Quality Improvement Program in Non-Veterans Administration Hospitals
Fink, A. S., Campbell, D. A., Mentzer, R. M., Henderson, W. G., Daley, J., Bannister, J., Hur, K., & Khuri, S. F. (2002) · Annals of Surgery · doi:10.1097/00000658-200209000-00011 · Cited by 545
Gawande, A. A., Kwaan, M. R., Regenbogen, S. E., Lipsitz, S. R., & Zinner, M. J. (2006) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2006.11.011 · Cited by 504
Evaluating Discrimination of Risk Prediction Models
Pencina, M. J., & D’Agostino, R. B. (2015) · JAMA · doi:10.1001/jama.2015.11082 · Cited by 388
Bertsimas, D., Dunn, J., Velmahos, G. C., & Kaafarani, H. M. A. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002956 · Cited by 321
Prehabilitation: preparing patients for surgery
Wynter-Blyth, V., & Moorthy, K. (2017) · BMJ · doi:10.1136/bmj.j3702 · Cited by 238
Postoperative complications and implications on patient-centered outcomes
Tevis, S. E., & Kennedy, G. D. (2013) · Journal of Surgical Research · doi:10.1016/j.jss.2013.01.032 · Cited by 232
Hospital and Payer Costs Associated With Surgical Complications
Healy, M. A., Mullard, A. J., Campbell, D. A., & Dimick, J. B. (2016) · JAMA Surgery · doi:10.1001/jamasurg.2016.0773 · Cited by 221
Raval, M. V., & Pawlik, T. M. (2018) · JAMA Surgery · doi:10.1001/jamasurg.2018.0486 · Cited by 196
Self-citationImpact of complications on long-term survival after resection of colorectal liver metastases
Mavros, M. N., Jong, M. C. D., Dogeas, E., Hyder, O. N., & Pawlik, T. M. (2013) · British journal of surgery · doi:10.1002/bjs.9060 · Cited by 142
Self-citationA Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery
Zhao, B., Waterman, R. S., Urman, R. D., & Gabriel, R. A. (2019) · Journal of Medical Systems · doi:10.1007/s10916-018-1151-y · Cited by 98
Changes in Prognosis After the First Postoperative Complication
Silber, J. H., Rosenbaum, P. R., Trudeau, M. E., Chen, W., Zhang, X., Kelz, R. R., Mosher, R. E., & Even‐Shoshan, O. (2005) · Medical Care · doi:10.1097/00005650-200502000-00005 · Cited by 94
Copeland, E. M. (2010) · Yearbook of Surgery · doi:10.1016/s0090-3671(09)79588-6 · Cited by 87
Impact of complications on long‐term survival after resection of intrahepatic cholangiocarcinoma
Spolverato, G., Yakoob, M. Y., Kim, Y., Alexandrescu, S. T., Marques, H. P., Lamelas, J., Aldrighetti, L. A., Gamblin, T. C., Maithel, S. K., Pulitanò, C., et al. (2015) · Cancer · doi:10.1002/cncr.29419 · Cited by 86
Self-citationDorcaratto, D., Mazzinari, G., Fernández‐Moreno, M., Forner, E. M., Garcés‐Albir, M., Ortega, J., & Sabater, L. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000003254 · Cited by 85
Gleeson, E., Shaikh, M. F., Shewokis, P. A., Clarke, J. R., Meyers, W. C., Pitt, H. A., & Bowne, W. B. (2016) · Surgery · doi:10.1016/j.surg.2016.06.040 · Cited by 77
Prediction of burn healing time using artificial neural networks and reflectance spectrometer
Yeong, E., Hsiao, T., Chiang, H. K., & Lin, C. (2005) · Burns · doi:10.1016/j.burns.2004.12.003 · Cited by 76
Variation in Medicare Expenditures for Treating Perioperative Complications
Pradarelli, J. C., Healy, M. A., Osborne, N. H., Ghaferi, A. A., Dimick, J. B., & Nathan, H. (2016) · JAMA Surgery · doi:10.1001/jamasurg.2016.3340 · Cited by 76
Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
Hu, Y., Ku, T.-H., Jan, R., Wang, K., Tseng, Y., & Yang, S.-F. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-131 · Cited by 75
Data Science: Big Data, Machine Learning, and Artificial Intelligence
Carlos, R. C., Kahn, C. E., & Halabi, S. S. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.01.029 · Cited by 70
Multiple complications and short length of stay are associated with postoperative readmissions
Kohlnhofer, B. M., Tevis, S. E., Weber, S. M., & Kennedy, G. D. (2014) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2013.10.022 · Cited by 68
A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms
Canchi, T., Srinivasan, D. K., Ng, E. Y. K., & Narayanan, S. (2015) · BioMed Research International · doi:10.1155/2015/861627 · Cited by 62
Minnella, E. M., Liberman, A. S., Charlebois, P., Stein, B., Scheede‐Bergdahl, C., Awasthi, R., Gillis, C., Bousquet-Dion, G., Ramanakuma, A. V., Pecorelli, N., et al. (2019) · Acta Oncologica · doi:10.1080/0284186x.2018.1557343 · Cited by 62
Modeling time‐to‐event (survival) data using classification tree analysis
Linden, A., & Yarnold, P. R. (2017) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.12779 · Cited by 53
Cost of Major Complications After Liver Resection in the United States
Idrees, J. J., Johnston, F. M., Canner, J. K., Dillhoff, M. E., Schmidt, C. R., Haut, E. R., & Pawlik, T. M. (2017) · Annals of Surgery · doi:10.1097/sla.0000000000002627 · Cited by 52
Self-citationMerath, K., Bagante, F., Beal, E. W., Lopez‐Aguiar, A. G., Poultsides, G. A., Makris, E. A., Rocha, F. G., Kanji, Z. S., Weber, S. M., Fisher, A. V., et al. (2018) · Journal of Surgical Oncology · doi:10.1002/jso.24985 · Cited by 44
Self-citationBeal, E. W., Lyon, E., Kearney, J., Wei, L., Ethun, C. G., Black, S. M., Dillhoff, M. E., Salem, A., Weber, S. M., Tran, T. B., et al. (2017) · HPB · doi:10.1016/j.hpb.2017.08.009 · Cited by 32
Self-citationVariation in Medicare Payments for Colorectal Cancer Surgery
Abdelsattar, Z. M., Birkmeyer, J. D., & Wong, S. L. (2015) · Journal of Oncology Practice · doi:10.1200/jop.2015.004036 · Cited by 31
Parikh, S. A., Gómez, R., Thirugnanasambandam, M., Chauhan, S. S., Oliveira, V. D., Muluk, S. C., Eskandari, M. K., & Finol, E. A. (2018) · Annals of Biomedical Engineering · doi:10.1007/s10439-018-02116-w · Cited by 28
Merath, K., Chen, Q., Bagante, F., Sun, S. H., Akgül, Ö., Idrees, J. J., Dillhoff, M. E., Schmidt, C. R., Cloyd, J. M., & Pawlik, T. M. (2018) · HPB · doi:10.1016/j.hpb.2018.08.005 · Cited by 28
Self-citationMerath, K., Chen, Q., Bagante, F., Akgül, Ö., Idrees, J. J., Dillhoff, M. E., Cloyd, J. M., & Pawlik, T. M. (2018) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-018-3829-3 · Cited by 21
Self-citationEjaz, A. M., Kim, Y., Spolverato, G., Taylor, R., Hundt, J., & Pawlik, T. M. (2015) · HPB · doi:10.1111/hpb.12452 · Cited by 14
Self-citationBeal, E. W., Saunders, N., Kearney, J. F., Lyon, E., Wei, L., Squires, M. H., Jin, L. X., Worhunsky, D. J., Votanopoulos, K. I., Ejaz, A. M., et al. (2018) · The American Surgeon · doi:10.1177/000313481808400318 · Cited by 14
Self-citationHarvin, J. A., Green, C. E., Pedroza, C., Tyson, J. E., Moore, L. J., Wade, C. E., Holcomb, J. B., & Kao, L. S. (2019) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2018.12.025 · Cited by 7
Dorcaratto, D., Mazzinari, G., Garcés‐Albir, M., Muñoz, E., Fernández, M., Sabater-Orti, L., & Ortega, J. (2018) · HPB · doi:10.1016/j.hpb.2018.06.117 · Cited by 2
Cited by
Sorted by citations
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
Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221101488 · Cited by 156
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
