Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery

Abstract

Decision making in Hepatobiliary and Pancreatic Surgery is challenging, not least because of the significant complications that may occur following surgery and the complexity of interventions to treat them. Machine Learning (ML) relates to the use of computer derived algorithms and systems to enhance knowledge in order to facilitate decision making and could be of great benefit to surgical patients. ML could be employed pre- or peri-operatively to shape treatment choices prospectively, or could be utilised in the post-hoc analysis of complications in order to inform future practice. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. Accuracy, validity and integrity of data are of fundamental importance if predictive models generated by ML are to be successfully integrated into surgical practice. The choice of appropriate ML models and the interface between ML, traditional statistical methodologies and human expertise will also impact the potential to incorporate data science techniques into daily clinical practice.

Keywords

Machine Learning / artificial intelligence / hepatic surgery / pancreatic surgery

References

[1] Versteijne E,Suker M.Neoadjuvant chemoradiotherapy versus upfront surgery for resectable and borderline resectable pancreatic cancer: long-term results of the dutch randomized PREOPANC trial.J Clin Oncol2022;40:1220-30

[2] van der Gaag NA,van Eijck CH.Preoperative biliary drainage for cancer of the head of the pancreas.N Engl J Med2010;362:129-37

[3] Hackert T,Fritz S.Enucleation in pancreatic surgery: indications, technique, and outcome compared to standard pancreatic resections.Langenbecks Arch Surg2011;396:1197-203

[4] Graaf W, van Lienden KP, van den Esschert JW, Bennink RJ, van Gulik TM. Increase in future remnant liver function after preoperative portal vein embolization.Br J Surg2011;98:825-34

[5] Guglielmi A,Conci S,Iacono C.How much remnant is enough in liver resection?.Dig Surg2012;29:6-17

[6] Cercek A,Tan BR.Assessment of hepatic arterial infusion of floxuridine in combination with systemic gemcitabine and oxaliplatin in patients with unresectable intrahepatic cholangiocarcinoma: a phase 2 clinical trial.JAMA Oncol2020;6:60-7 PMCID:PMC6824231

[7] Xu Q,Ye X.Comparison of hepatic resection and radiofrequency ablation for small hepatocellular carcinoma: a meta-analysis of 16,103 patients.Sci Rep2014;4:7252 PMCID:PMC4246212

[8] Konstantinou I,Papamichael D.Outcomes following potentially curative therapies for older patients with metastatic colorectal cancer.Eur J Surg Oncol2021;47:591-6

[9] Pencina MJ,Steyerberg EW.Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.Stat Med2011;30:11-21 PMCID:PMC3341973

[10] Bakhtiarvand N,Mahnam M.A novel reliability-based regression model to analyze and forecast the severity of COVID-19 patients.BMC Med Inform Decis Mak2022;22:123 PMCID:PMC9069125

[11] Klein G,Rall EL. A data-frame theory of sensemaking. In Hoffman RR, editor. Expertise out of context. Psychology Press; 2007. pp. 118-160.

[12] Klein GA. A recognition-primed decision (RPD) model of rapid decision making. In Klein GA, Orasanu J, Calderwood R, Zsambok CE, editors. Decision making in action: models and methods. Ablex Publishing; 1993. pp. 138-47. Available from: https://psycnet.apa.org/record/1993-97634-006 [Last accessed on 11 Jan 2023]

[13] Klein G,Macgregor D.Critical decision method for eliciting knowledge.IEEE Trans Syst Man Cybern1989;19:462-72

[14] Smits FJ,Besselink MG.Algorithm-based care versus usual care for the early recognition and management of complications after pancreatic resection in the Netherlands: an open-label, nationwide, stepped-wedge cluster-randomised trial.Lancet2022;399:1867-75

[15] Subbe CP,Rutherford P.Validation of a modified Early Warning Score in medical admissions.QJM-INT J MED2001;94:521-6

[16] Gardner-Thorpe J,Wrightson J,Keeling N.The value of modified early warning score (MEWS) in surgical in-patients: a prospective observational study.Ann R Coll Surg Engl2006;88:571-5 PMCID:PMC1963767

[17] Nishijima I,Maedomari S.Use of a modified early warning score system to reduce the rate of in-hospital cardiac arrest.J Intensive Care2016;4:12 PMCID:PMC4748572

[18] Beckett DJ,Oswald S.Reducing cardiac arrests in the acute admissions unit: a quality improvement journey.BMJ Qual Saf2013;22:1025-31 PMCID:PMC3888590

[19] Kwon JM,Lee Y,Park J.An algorithm based on deep learning for predicting in-hospital cardiac arrest.J Am Heart Assoc2018;7 PMCID:PMC6064911

[20] Varley PR,Tsung A.Factors influencing failure to rescue after pancreaticoduodenectomy: a National Surgical Quality Improvement Project Perspective.J Surg Res2017;214:131-9

[21] O'Reilly D,Bijoor P.Early experience with a hepatobiliary and pancreatic quality improvement program.BMJ Qual Improv Rep2014;2:u201158.w721 PMCID:PMC4663834

[22] Koch M,Padbury R.Bile leakage after hepatobiliary and pancreatic surgery: a definition and grading of severity by the International Study Group of Liver Surgery.Surgery2011;149:680-8

[23] Rahbari NN,Padbury R.Posthepatectomy liver failure: a definition and grading by the International Study Group of Liver Surgery (ISGLS).Surgery2011;149:713-24

[24] Rahbari NN,Padbury R.Post-hepatectomy haemorrhage: a definition and grading by the International Study Group of Liver Surgery (ISGLS).HPB2011;13:528-35 PMCID:PMC3163274

[25] Bassi C,Dervenis C.The 2016 update of the International Study Group (ISGPS) definition and grading of postoperative pancreatic fistula: 11 years after.Surgery2017;161:584-91

[26] Wente MN,Dervenis C.Delayed gastric emptying (DGE) after pancreatic surgery: a suggested definition by the International Study Group of Pancreatic Surgery (ISGPS).Surgery2007;142:761-8

[27] Wente MN,Bassi C.Postpancreatectomy hemorrhage (PPH): an International Study Group of Pancreatic Surgery (ISGPS) definition.Surgery2007;142:20-5

[28] Slankamenac K,Barkun J,Clavien PA.The comprehensive complication index: a novel continuous scale to measure surgical morbidity.Ann Surg2013;258:1-7

[29] van der Werf LR,Buis CI.Implementation and first results of a mandatory, nationwide audit on liver surgery.HPB2019;21:1400-10

[30] Suurmeijer JA,Bonsing BA.Outcome of pancreatic surgery during the first six years of a mandatory audit within the Dutch pancreatic cancer group.Ann Surg2022;Online ahead of print:

[31] Madani A,Altieri MS.Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy.Ann Surg2022;276:363-9 PMCID:PMC8186165

[32] Callery MP,Kent TS,Vollmer CM Jr.A prospectively validated clinical risk score accurately predicts pancreatic fistula after pancreatoduodenectomy.J Am Coll Surg2013;216:1-14

[33] Mungroop TH,van Klaveren D.Alternative fistula risk score for pancreatoduodenectomy (a-FRS): design and international external validation.Ann Surg2019;269:937-43

[34] Roberts KJ,Marudanayagam R.Scoring system to predict pancreatic fistula after pancreaticoduodenectomy: a UK multicenter study.Ann Surg2015;261:1191-7

[35] Shi Y,Qi Y.Computed tomography-adjusted fistula risk score for predicting clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy: training and external validation of model upgrade.EBioMedicine2020;62:103096 PMCID:PMC7648191

[36] Tang B,Ma Y.A modified alternative fistula risk score (a-FRS) obtained from the computed tomography enhancement pattern of the pancreatic parenchyma predicts pancreatic fistula after pancreatoduodenectomy.HPB2021;23:1759-66

[37] Hayashi H,Fujiwara Y.Comparison of three fistula risk scores after pancreatoduodenectomy: A single-institution retrospective study.Asian J Surg2021;44:143-6

[38] Kambakamba P,Herrera PE.The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: a proof-of-principle study.Surgery2020;167:448-54

[39] Capretti G,De Palma C.A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy.Updates Surg2022;74:235-43

[40] Gichoya JW,Bhimireddy AR.AI recognition of patient race in medical imaging: a modelling study.Lancet Digit Health2022;4:e406-14 PMCID:PMC9650160

[41] Mu W,Gao F.Prediction of clinically relevant pancreatico-enteric anastomotic fistulas after pancreatoduodenectomy using deep learning of preoperative computed tomography.Theranostics2020;10:9779-88 PMCID:PMC7449906

[42] Chen S,Wang D,Wong L.The hepatitis B epidemic in China should receive more attention.Lancet2018;391:1572

[43] Sidey-Gibbons JAM.Machine learning in medicine: a practical introduction.BMC Med Res Methodol2019;19:64

[44] Han IW,Ryu Y.Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence.World J Gastroenterol2020;26:4453-64 PMCID:PMC7438201

[45] Cos H,Williams G.Predicting outcomes in patients undergoing pancreatectomy using wearable technology and machine learning: prospective cohort study.J Med Internet Res2021;23:e23595 PMCID:PMC8074869

[46] Shi HY,Wang JJ,Lee HH.Artificial neural network model for predicting 5-year mortality after surgery for hepatocellular carcinoma: a nationwide study.J Gastrointest Surg2012;16:2126-31

[47] Qiao G,Huang A,Lau WY.Artificial neural networking model for the prediction of post-hepatectomy survival of patients with early hepatocellular carcinoma.J Gastroenterol Hepatol2014;29:2014-20

[48] Wang F,Khullar D.Should health care demand interpretable artificial intelligence or accept “black box” medicine?.Ann Intern Med2020;172:59-60

[49] Huang Y,Zeng Y,Ma H.Development and validation of a machine learning prognostic model for hepatocellular carcinoma recurrence after surgical resection.Front Oncol2020;10:593741 PMCID:PMC7882739

[50] Mai R,Bai T.Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.Surgery2020;168:643-52

[51] Shapey IM,de Liguori Carino N.Data driven decision-making for older patients with hepatocellular carcinoma.Eur J Surg Oncol2021;47:576-82

[52] Collins GS.Reporting of artificial intelligence prediction models.Lancet2019;393:1577-9

[53] Shen Z,Wang W.Machine learning algorithms as early diagnostic tools for pancreatic fistula following pancreaticoduodenectomy and guide drain removal: A retrospective cohort study.Int J Surg2022;102:106638

[54] Pfitzner B,Brabender R.Perioperative risk assessment in pancreatic surgery using machine learning. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021.pp. 2211-4.