A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study

Abstract

Background Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities.

Methods A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting model (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in the GBM.

Results 1378 patients (mean age 43.3 years; 61.2% female) were included in the GBM and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM had accuracy of 71.5% (SD 2.5%) (AUC 0.79 [SD 0.06]) and performed better than the 2019 American Society for Gastrointestinal Endoscopy (ASGE) guidelines (accuracy 62.4% [SD 2.6%]; AUC 0.63 [SD 0.03]) and European Society of Gastrointestinal Endoscopy (ESGE) guidelines (accuracy 62.8% [SD 2.6%]; AUC 0.67 [SD 0.02]). The GBM correctly categorized 22% of patients directed to unnecessary ERCP by ASGE guidelines, and appropriately recommended as the next management step 48% of ERCPs incorrectly rejected by ESGE guidelines.

Conclusions A machine learning-based tool was created, providing real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directed ERCP use than existing ASGE and ESGE guidelines, and has the potential to reduce morbidity associated with ERCP or missed choledocholithiasis.

Publication History

Received: 05 March 2023

Accepted after revision: 12 September 2023

Accepted Manuscript online:
12 September 2023

Article published online:
29 November 2023

• References

• 1 Everhart JE, Ruhl CE. Burden of digestive diseases in the United States part I: overall and upper gastrointestinal diseases. Gastroenterology 2009; 136: 376-386
• 2 Thistle JL, Cleary PA, Lachin JM. et al. The natural history of cholelithiasis: the National Cooperative Gallstone Study. Ann Intern Med 1984; 101: 171-175
• 3 Friedman GD, Raviola CA, Fireman B. Prognosis of gallstones with mild or no symptoms: 25 years of follow-up in a health maintenance organization. J Clin Epidemiol 1989; 42: 127-136
• 4 Manes G, Paspatis G, Aabakken L. et al. Endoscopic management of common bile duct stones: European Society of Gastrointestinal Endoscopy (ESGE) guideline. Endoscopy 2019; 51: 472-491
• 5 Kochar B, Akshintala VS, Afghani E. et al. Incidence, severity, and mortality of post-ERCP pancreatitis: a systematic review by using randomized, controlled trials. Gastrointest Endosc 2015; 81: 143-149.e149
• 6 Maple JT, Ben-Menachem T. ASGE Standards of Practice Committee. et al. The role of endoscopy in the evaluation of suspected choledocholithiasis. Gastrointest Endosc 2010; 71: 1-9
• 7 Suarez AL, LaBarre NT, Cotton PB. et al. An assessment of existing risk stratification guidelines for the evaluation of patients with suspected choledocholithiasis. Surg Endosc 2016; 30: 4613-4618
• 8 Adams MA, Hosmer AE, Wamsteker EJ. et al. Predicting the likelihood of a persistent bile duct stone in patients with suspected choledocholithiasis: accuracy of existing guidelines and the impact of laboratory trends. Gastrointest Endosc 2015; 82: 88-93
• 9 Buxbaum JL, Abbas Fehmi SM. ASGE Standards of Practice Committee. et al. ASGE guideline on the role of endoscopy in the evaluation and management of choledocholithiasis. Gastrointest Endosc 2019; 89: 1075-1105.e1015
• 10 Deo RC. Machine learning in medicine. Circulation 2015; 132: 1920-1930
• 11 Yu CY, Roth N, Jani N. et al. Dynamic liver test patterns do not predict bile duct stones. Surg Endosc 2019; 33: 3300-3313
• 12 Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2001; 29: 1189-1232
• 13 Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot 2013; 7: 21
• 14 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837-845
• 15 Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol 2008; 77: 802-813
• 16 Ahmed M, Kanotra R, Savani GT. et al. Utilization trends in inpatient endoscopic retrograde cholangiopancreatography (ERCP): a cross-sectional US experience. Endosc Int Open 2017; 5: E261-E271
• 17 Kang J, Paik KH, Lee JC. et al. The efficacy of clinical predictors for patients with intermediate risk of choledocholithiasis. Digestion 2016; 94: 100-105
• 18 He H, Tan C, Wu J. et al. Accuracy of ASGE high-risk criteria in evaluation of patients with suspected common bile duct stones. Gastrointest Endosc 2017; 86: 525-532
• 19 Jagtap N, Hs Y, Tandan M. et al. Clinical utility of ESGE and ASGE guidelines for prediction of suspected choledocholithiasis in patients undergoing cholecystectomy. Endoscopy 2020; 52: 569-573
• 20 Tranter SE, Thompson MH. Spontaneous passage of bile duct stones: frequency of occurrence and relation to clinical presentation. Ann R Coll Surg Engl 2003; 85: 174-177
• 21 Khoury T, Adileh M, Imam A. et al. Parameters suggesting spontaneous passage of stones from common bile duct: a retrospective study. Can J Gastroenterol Hepatol 2019; 2019: 5382708
• 22 Jovanovic P, Salkic NN, Zerem E. Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis. Gastrointest Endosc 2014; 80: 260-268
• 23 Golub R, Cantu Jr R, Tan M. The prediction of common bile duct stones using a neural network. J Am Coll Surg 1998; 187: 584-590
• 24 Adadi A, Adadi S, Berrada M. Gastroenterology meets machine learning: status quo and quo vadis. Adv Bioinformatics 2019; 2019: 1870975
• 25 Beyer G, Kasprowicz F, Hannemann A. et al. Definition of age-dependent reference values for the diameter of the common bile duct and pancreatic duct on MRCP: a population-based, cross-sectional cohort study. Gut 2023; 72: 1738-1744