Machine learnINg for the rElapse Risk eValuation in Acute Biliary Pancreatitis.

NCT06124989 | Unknown DMC

• 1 other identifier: MINERVA_1
• Study Type: observational
• Target: 430
• Locations: 1 country
• Sites: 1

Brief Summary

The MINERVA (Machine learnINg for the rElapse Risk eValuation in Acute biliary pancreatitis) project stems from the need in the clinical practice of taking an operational decision in patients that are admitted to the hospital with a diagnosis of acute biliary pancreatitis. In particular, the MINERVA prospective cohort study aims to develop a predictive score that allows to assess the risk of hospital readmission for patients diagnosed with mild biliary acute pancreatitis using Machine Learning and artificial intelligence. The objectives of the MINERVA study are to:

1. 1.Propose a novel methodology for the assessment of the risk of relapse in patients with mild biliary acute pancreatitis who did not undergo early cholecystectomy (within 3 to 7 days from hospital admission);
2. 2.Propose a Machine Learning predictive model using a Deep Learning architecture applied to easily collectable data;
3. 3.Validate the MINERVA score on an extensive, multicentric, prospective cohort;
4. 4.Allow national and international clinicians, medical staff, researchers and the general audience to freely and easily access the MINERVA score computation and use it in their daily clinical practice.

Conditions

Acute Pancreatitis; Pancreatitis Due to Gallstones; Pancreatitis Biliary; Pancreatitis Relapsing

Keywords

Machine Learning, Prediction Model

Outcome Measures

Primary Outcomes (1)

• Number of patients with recurrence of biliary acute pancreatitis.

  The number of patients with recurrence of biliary acute pancreatitis: prediction of risk relapse of acute biliary pancreatitis in patients after a first episode of mild biliary acute pancreatitis (according to the 2012 Revised Atlanta Classification) not submitted to early (within three to seven days from the acute episode) cholecystectomy. This outcome will be reached by the development and validation of a novel risk score.

  30-day, 60-day, 90-day, 1-year

Secondary Outcomes (1)

• Accuracy of the MINERVA model.

  30-day, 60-day, 90-day, 1-year

Interventions

MINERVA Machine Learning model DIAGNOSTIC_TEST

The MINERVA score for the prediction of the risk of relapse of acute pancreatitis in patients who did not undergo early cholecystectomy after the first episode of acute biliary pancreatitis will be grounded upon a Machine Learning model that takes into account patients' demographic, clinical, and laboratory variables that can be easily collected and recorded at index patient admission.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients (≥ 18 years old), of both sexes, admitted to the participating centers (surgical departments and/or gastroenterology departments and/or internal medicine departments) with a clinical diagnosis of mild biliary acute pancreatitis (according to the Revised Atlanta Classification), confirmed by at least an ultrasound scan, and not submitted to cholecystectomy or ERCP/ES (Endoscopic Retrograde CholangioPancreatography/Endoscopic Sphyncterotomy) during the same hospital admission.

You may qualify if:

• Adult patients (≥ 18 years old)
• Clinical diagnosis of mild biliary acute pancreatitis (according to the Revised Atlanta Classification)
• Not submitted to cholecystectomy or ERCP/ES (Endoscopic Retrograde CholangioPancreatography/Endoscopic Sphyncterotomy) during the same hospital admission

You may not qualify if:

• Acute pancreatitis of etiology other than gallstones;
• Moderately-severe pancreatitis;
• Severe pancreatitis;
• Presence of pancreatic necrosis;
• Pregnant patients;
• Patients not able to sign the informed consent to take part in the study.

Sponsors & Collaborators

• University of Cagliari: lead
• Università di Napoli Federico II: collaborator
• Università della Campania Luigi Vanvitelli: collaborator

Study Sites (1)

University of Cagliari, Emergency Surgery Department

Cagliari, CA, 09120, Italy

Location

Related Publications (24)

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• Podda M, Pisanu A, Pellino G, De Simone A, Selvaggi L, Murzi V, Locci E, Rottoli M, Calini G, Cardelli S, Catena F, Vallicelli C, Bova R, Vigutto G, D'Acapito F, Ercolani G, Solaini L, Biloslavo A, Germani P, Colutta C, Occhionorelli S, Lacavalla D, Sibilla MG, Olmi S, Uccelli M, Oldani A, Giordano A, Guagni T, Perini D, Pata F, Nardo B, Paglione D, Franco G, Donadon M, Di Martino M, Bruzzese D, Pacella D. Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol. World J Emerg Surg. 2025 Mar 3;20(1):17. doi: 10.1186/s13017-025-00594-7.

  PMID: 40033414 DERIVED

MeSH Terms

Conditions

Pancreatitis; Pancreatitis, Chronic

Condition Hierarchy (Ancestors)

Pancreatic Diseases; Digestive System Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Mauro Podda, MD

  University of Cagliari, Department of Surgical Science

  STUDY CHAIR

Central Study Contacts

Mauro Podda, MD

CONTACT

07051096571; mauro.podda@unica.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: November 1, 2023
• First Posted: November 9, 2023
• Study Start: January 1, 2024
• Primary Completion: December 31, 2024
• Study Completion: December 31, 2025
• Last Updated: November 9, 2023

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR

Locations

IT (1)