A Novel Machine Learning Model for the Prediction of Relapse of Acute Biliary Pancreatitis (Machine learnINg for the rElapse Risk eValuation in Acute Biliary Pancreatitis - MINERVA)

Acute pancreatitis is the most common pancreatic disease, with a global incidence of 34 cases per 100,000 individuals. This disease counts more than 1.5 million new patients per year worldwide, with a mortality that approaches 1%. Mild biliary acute pancreatitis patients, when admitted to the hospital, can be treated with index, early cholecystectomy (within 3 to 7 days from the acute episode) or conservatively. While conservative treatment can be resolutive, up to 35% of these patients have a relapse within 30 days, and require emergency surgery in a significantly worse overall patient condition, reducing the chances of success. Other than that, relapse dramatically increases the chances of chronic pancreatitis, pancreatic cancer, postoperative complications and overall mortality. Relapse episodes have also an economic impact on healthcare facilities, as a second and longer hospital admission per patient increases the overall medical cost per patient by at least 100%. So far, however, there are no standardised methods to predict relapse of biliary acute pancreatitis in patients who did not undergo early cholecystectomy after the first episode of mild biliary acute pancreatitis.

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 mild acute biliary pancreatitis.

The MINERVA project aims to reach the following objectives and results:

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 after the first episode of mild biliary acute pancreatitis;
2. Propose a Machine Learning predictive model using a Deep Learning architecture applied to data easy to collect from patients;
3. Validate the MINERVA score on an extensive, multicentric, prospective cohort;
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.

The MINERVA score will provide the clinicians with a validated and standardized assessment of relapse risk that takes into account the personal history, demographic data and laboratory characteristics of each patient. The MINERVA score will be free and easy to compute instantly for all medical staff; it will be accessible at any time on the MINERVA website and web app, and will provide an immediate and reliable result that can be a clear indication for the best treatment pathway for the clinician and for the patient.