Prediction model for postoperative complications in gastrointestinal surgery based on preoperative and intraoperative factors using machine learning: a retrospective, single-center study

Katai H, Sasako M, Fukuda H, Nakamura K, Hiki N, Saka M, et al. Safety and feasibility of laparoscopy-assisted distal gastrectomy with suprapancreatic nodal dissection for clinical stage I gastric cancer: a multicenter phase II trial (JCOG 0703). Gastric Cancer. 2010;13:238–44.

PubMed  Google Scholar 

Kitano S, Inomata M, Mizusawa J, Katayama H, Watanabe M, Yamamoto S, et al. Survival outcomes following laparoscopic versus open D3 dissection for stage II or III colon cancer (JCOG0404): a phase 3, randomised controlled trial. Lancet Gastroenterol Hepatol. 2017;2:261–8.

PubMed  Google Scholar 

Aoki R, Maruyama S, Takii Y, Nogami H. Efficacy and safety of laparoscopic resection of colorectal cancer in non-elite cases. Surg Today. 2025;55:676–84.

PubMed  Google Scholar 

Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg. 1991;78:355–60.

CAS  PubMed  Google Scholar 

Whiteley MS, Pryherch DR, Higgins B, Weaver PC, Prout WG. An evaluation of the POSSUM surgical scoring system. Br J Surg. 1996;83:812–5.

CAS  PubMed  Google Scholar 

Tekkis PP, Prytherch DR, Kocher HM, Senapati A, Poloniecki JD, Stamatakis JD, et al. Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM). Br J Surg. 2004;91:1174–82.

CAS  PubMed  Google Scholar 

Gawande AA, Kwaan MR, Regenbogen SE, Lipsitz SA, Zinner MJ. An apgar score for surgery. J Am Coll Surg. 2007;204:201–8.

PubMed  Google Scholar 

Bilimoria KY, Liu Y, Paruch JL, Zhou L, Kmiecik TE, Ko CY, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217:833–42.

PubMed  PubMed Central  Google Scholar 

Beal EW, Saunders ND, Kearney JF, Lyon E, Wei L, Squires MH, et al. Accuracy of the ACS NSQIP online risk calculator depends on how you look at it: results from the United States Gastric Cancer Collaborative. Am Surg. 2018;84:358–64.

PubMed  Google Scholar 

Gleeson EM, Shaikh MF, Shewokis PA, Clarke JR, Meyers WC, Pitt HA, et al. Whipple-ABACUS, a simple, validated risk score for 30-day mortality after pancreaticoduodenectomy developed using the ACS-NSQIP database. Surgery. 2016;160:1279–87.

PubMed  Google Scholar 

Merath K, Hyer JM, Mehta R, Farooq A, Bagante F, Sahara K, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery. J Gastrointest Surg. 2020;24:1843–51.

PubMed  Google Scholar 

Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269:652–62.

PubMed  Google Scholar 

Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, et al. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth. 2019;123:688–95.

PubMed  PubMed Central  Google Scholar 

Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, et al. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw Open. 2021;4: e212240.

PubMed  PubMed Central  Google Scholar 

Kadomatsu Y, Emoto R, Kubo Y, Nakanishi K, Ueno H, Kato T, et al. Development of a machine learning-based risk model for postoperative complications of lung cancer surgery. Surg Today. 2024;54:1482–9.

PubMed  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.

Google Scholar 

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 785–794.

Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240:205–13.

PubMed  PubMed Central  Google Scholar 

Clavien PA, Barkun J, de Oliveira ML, Vauthey JN, Dindo D, Schulick RD, et al. The clavien-dindo classification of surgical complications: five-year experience. Ann Surg. 2009;250:187–96.

PubMed  Google Scholar 

Ho TK. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995;1:278–82.

Lawrence VA, Hilsenbeck SG, Mulrow CD, Dhanda R, Sapp J, Page CP. Incidence and hospital stay for cardiac and pulmonary complications after abdominal surgery. J Gen Intern Med. 1995;10:671–8.

CAS  PubMed  Google Scholar 

Smetana GW, Lawrence VA, Cornell JE, American College of Physicians. Preoperative pulmonary risk stratification for noncardiothoracic surgery: systematic review for the American College of Physicians. Ann Intern Med. 2006;144:581–95.

PubMed  Google Scholar 

Torrey L, Shavlik J. Transfer learning. In: Sonia E, Martin J, Magdalena R, Martinez M, Serrano A, editors. Handbook of Research on Machine Leaning Applications and Trends: Algorithms, Methods, and Techniques. IGI Global; 2010. p. 242–64.

Comments (0)

No login
gif