Stark Study: Machine Learning Approach to Predict Post-Ercp Pancreatitis in an International Multicenter Prospective Cohort Study

Ciarfaglia, G.; Cárdenas-Jaén, Karina; Poropat, Goran; Korpela, Taija; Madelenat, P.; Aparicio, JR; Casellas, JA; Arcidiacono, P.G.; Mariani, Alberto; Štimac, Davor; Hauser, Goran; Udd, Marianne; Kylänpää, Leena; Rainio, Mia; Giulio, E. Di; Vanella, Giuseppe; Löhr, Matthias; Valente, Roberto; Arnelo, Urban; Pretis, ND; Gabbrielli, Armando; Brozzi, Lorenzo; de–Madaria, Enrique; Capurso, Gabriele

doi:10.1055/s-0040-1704234

Cited by 1 publication

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“…A recent conference paper presented the first data of an international, multicenter, prospective cohort study that applied ML techniques in the development of two different models for the prediction of PEP, respectively, based on gradient boosting and logistic regression. 65 Preliminary results of this study reported that relevant variables included in the analysis were mostly pre-procedural factors, such as total bilirubin level, body mass index, age, units of alcohol drunk per day, and previous ERCP with sphincterotomy. 65 Interestingly, the gradient boosting-based model showed a significantly better performance when compared to the logistic regression-based one, 65 raising awareness that the application of ML for risk stratification would lead to the development of more reliable and accurate models for the prediction of PEP.…”

Section: Cutting-edge Tools For Predicting Pep: When Machine Learning...mentioning

confidence: 94%

Prevention of post-ERCP pancreatitis: current strategies and novel perspectives

Andreis

Mascagni

Schepis

et al. 2023

Therap Adv Gastroenterol

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Endoscopic retrograde cholangiopancreatography (ERCP) is an advanced endoscopic procedure that might lead to severe adverse events. Post-ERCP pancreatitis (PEP) is the most common post-procedural complication, which is related to significant mortality and increasing healthcare costs. Up to now, the prevalent approach to prevent PEP consisted of employing pharmacological and technical expedients that have been shown to improve post-ERCP outcomes, such as the administration of rectal nonsteroidal anti-inflammatory drugs, aggressive intravenous hydration, and the placement of a pancreatic stent. However, it has been reported that PEP originates from a more complex interaction of procedural and patient-related factors. Appropriate ERCP training has a pivotal role in PEP prevention strategy, and it is not a chance that a low PEP rate is universally considered one of the most relevant indicators of proficiency in ERCP. Scant data on the acquisition of skills during the ERCP training are currently available, although some efforts have been recently done to shorten the learning curve by way of simulation-based training and demonstrate competency by meeting technical requirements as well as adopting skill evaluation scales. Besides, the identification of adequate indications for ERCP and accurate pre-procedural risk stratification of patients might help to reduce PEP occurrence regardless of the endoscopist’s technical abilities, and generally preserve safety in ERCP. This review aims at delineating current preventive strategies and highlighting novel perspectives for a safer ERCP focusing on the prevention of PEP.

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Section: Cutting-edge Tools For Predicting Pep: When Machine Learning...mentioning

confidence: 94%