Naslov
Machine learning approach in the STARK international study: gradient boosting outperforms logistic regression to predict post- ERCP pancreatitis

Autori
Archibugi, Livia ; Ciarfaglia, G ; Cárdenas-Jaén, Karina ; Poropat, Goran ; Korpela, Taija ; Maisonneuve, Patrick ; Aparicio Tormo, JR ; Casella y Casellas, JA ; Arcidiacono, Paolo Giorgio ; Mariani, A ; Štimac, Davor ; Hauser, Goran ; Udd, M ; Kylänpää, L ; Rainio, M ; Di Giulio, E ; Vanella, G ; Löhr, Matthias ; Valente, Roberto ; Arnelo, U ; Fagerstrom, N ; de Pretis, Nicolo ; Gabbrielli, A ; Brozzi, L ; Capurso, Gabriele ; de-Madaria, Enrique

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
United European Gastroenterology Journal / - , 2020, 790-790

Skup
28th United European Gastroenterology Week

Mjesto i datum
Beč, Austrija; online, 03.10.2020. - 05.10.2020

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Machine learning, pancreatitis, ERCP

Sažetak
Introduction: Post-Endoscopic Retrograde Cholangiopancreatography (ERCP) pancreatitis (PEP) is ERCP most frequent complication. Predicting PEP risk can be determinant in reducing its incidence and managing patients appropriately, however, studies conducted so far identified single risk factors with standard statistical approaches, with limited accuracy. Aims & Methods: The aim was to build and evaluate performances of machine learning models to predict PEP probability and identify relevant features. The “STARK project” is an international, multicenter, prospective cohort study focused on PEP-associated factors. Data were randomly split in training (80%) and test set (20%). Models used to predict PEP probability were: gradient boosting (GB) and logistic regression (LR). On the training set, a 10-split random cross-validation (CV) was applied to optimize parameters to obtain the best mean Area Under the Receiver Operating Characteristics Curve (AUC of ROC). Afterwards, the model was re-trained on the whole training set with the best parameters and then applied on the test set. Results: 1, 150 patients were included. 70 (6.1%) patients developed PEP. GB model AUC in CV was 0.7±0.076 (95% CI 0.64-0.76), LR model 0.585±0.068 (95% CI 0.53-0.63) (p-value=0.012). AUC in test for GB model was 0.671. Most relevant variables for PEP prediction were: total bilirubin, age, body mass index, procedure time, alcohol units/day, previous sphincterotomy, biliary cannulation attempts and use of Ringer’s solution. Conclusion: This is the first study applying machine learning techniques for PEP prediction, with GB significantly outperforming LR model. Relevant variables were mostly pre-procedural except for procedure time, biliary cannulation attempts and Ringer’s solution use.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti

POVEZANOST RADA