Preprint Article Version 1 This version is not peer-reviewed

Machine Learning Approaches for the Prediction of Postoperative Major Complications in Patients Undergoing Surgery for Bowel Obstruction

Alessandro D. Mazzotta
,
Elisa Burti
,
Francesco Andrea Causio
* ,
Alex Orlandi
,
Silvia Martinelli
,
Mattia Longaroni
,
Tiziana Pinciroli
,
Tarek Debs
,
Gianluca Costa
,
Michelangelo Miccini
,
Paolo Aurello
,
Niccolò Petrucciani
Version 1 : Received: 23 July 2024 / Approved: 23 July 2024 / Online: 24 July 2024 (12:38:09 CEST)

How to cite: Mazzotta, A. D.; Burti, E.; Causio, F. A.; Orlandi, A.; Martinelli, S.; Longaroni, M.; Pinciroli, T.; Debs, T.; Costa, G.; Miccini, M.; Aurello, P.; Petrucciani, N. Machine Learning Approaches for the Prediction of Postoperative Major Complications in Patients Undergoing Surgery for Bowel Obstruction. Preprints 2024, 2024071889. https://doi.org/10.20944/preprints202407.1889.v1 Mazzotta, A. D.; Burti, E.; Causio, F. A.; Orlandi, A.; Martinelli, S.; Longaroni, M.; Pinciroli, T.; Debs, T.; Costa, G.; Miccini, M.; Aurello, P.; Petrucciani, N. Machine Learning Approaches for the Prediction of Postoperative Major Complications in Patients Undergoing Surgery for Bowel Obstruction. Preprints 2024, 2024071889. https://doi.org/10.20944/preprints202407.1889.v1

Abstract

Background/Aim: Performing emergency surgery for bowel obstruction continues to place a significant strain on the healthcare system. Conventional assessment methods for outcomes in bowel obstruction cases often concentrate on isolated factors, and the evaluation of results for individuals with bowel obstruction remains poorly studied. This study aimed to examine the risk factors associated with major postoperative complications. Methods: We retrospectively analyzed 99 patients undergoing surgery from 2015 to 2022. We divided the patients into two groups: 1) benign related obstruction (n=68), 2) cancer related obstruction (n=31). We used logistic regression, KNN and XGBOOST. We calculated the receiver operating characteristic curve, and accuracy of the model. Results: Colon obstructions were more frequent in the cancer group (p=0.005). Operative time, intestinal resection and stoma were significantly more frequent in the cancer group. Major complications were 41% for the cancer group vs. 20 in the benign group (p=0.03). Uni-and multivariate analysis shows that significant risk factors for major complication were cancer related obstruction and CRP. The best model was KNN with an accuracy of 0.82. Conclusion. Colonic obstruction is associated with a tumor-related blockage. Malignant cancer and an increase in C-reactive protein (CRP) are significant risk factors for patients who have undergone emergency surgery due to major complications. KNN could improve the process of counseling and the perioperative management of patients with intestinal obstruction in emergency settings.

Keywords

Bowel obstruction; Emergency surgery; Machine learning; Postoperative complications; Artificial intelligence; Risk prediction; C-reactive protein; Malignant obstruction; K-nearest neighbors algorithm

Subject

Medicine and Pharmacology, Emergency Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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