Acute PancreatitisMINERVANCT06124989
Machine learnINg for the rElapse Risk eValuation in Acute Biliary Pancreatitis.
An observational study of Acute Pancreatitis and Pancreatitis Due to Gallstones, run by University of Cagliari at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Pancreatitis, Pancreatitis Due to Gallstones, Pancreatitis Biliary
- What is being tested?
- MINERVA Machine Learning model
- How many people
- 430 participants
- Where
- 1 siteItaly
- Study dates
- 1 Jan 2024to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Italy1 site
University of Cagliari, Emergency Surgery Department
Cagliari, CA, 09120
Mauro Podda, MD, mauro.podda@unica.it
Gianluca Pellino, MD, PhD
About this study
Official title: 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)
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:
Read more
- 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);
- Propose a Machine Learning predictive model using a Deep Learning architecture applied to easily collectable data;
- Validate the MINERVA score on an extensive, multicentric, prospective cohort;
- 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 model will be developed on a retrospective cohort of patients (MANCTRA-1, already registered in ClinicalTrials.gov) and will be validated on a novel prospective multicentric cohort. After validation, 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.
Read the detailed description
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:
- 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;
- Propose a Machine Learning predictive model using a Deep Learning architecture applied to data easy to collect from patients;
- Validate the MINERVA score on an extensive, multicentric, prospective cohort;
- 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.
The MINERVA score model will be developed on a retrospective cohort of patients (MANCTRA-1, already registered in ClinicalTrials.gov) and will be validated on a novel prospective multicentric cohort.
Retrospective cohort The model development and initial training will be performed on a retrospective cohort of patients (n=692) collected during a preliminary multicentric study, the MANCTRA-1 study (approved by the Ethics Committee of the University of Cagliari Hospital, MANCTRA-1 - NCT04747990, Prot. PG/2021/7108) conducted by the PI (Dr. Mauro Podda) and the University of Cagliari local responsible of the MINERVA project.
Prospective cohort A total of 430 patients will be recruited in the prospective cohort of the MINERVA study.
Methods The MINERVA score will be grounded on a Machine Learning model that will be developed and trained on a retrospective cohort and validated on a prospective cohort of patients.
All model variables will be processed with kernel Principal Component Analysis (kPCA).
The Convex Hull of the scatterplot of the main components will be computed and the smallest rectangle will be extracted. The rectangle will be transformed into a 2d image with a fixed resolution using feature averaging and normalization.
The model will be developed at the University of Naples Federico II by Dr. Daniela Pacella with the Machine Learning expertise and supervision.
To prevent overfitting, the dataset will be split into training set, test set and validation set. Additionally, k-fold cross-validation will be used. The performance of the MINERVA model will be evaluated using the most adopted measures of accuracy, such as precision, recall and AUC (Area Under the ROC Curve). Additionally, its performance will be compared with that achieved using traditional machine learning methods (SVM, ANN). Missing data will be handled with imputation methods.
Variables Age (Years) Sex (Male:Female) Previous episodes of biliary pancreatitis (Yes; No) Admitting speciality (HepatoPancreatoBiliary surgery, General surgery, Internal medicine, Gastroenterology) Body mass index -BMI- (Kg/m2) Clinical history of diabetes (No diabetes; Yes with organ dysfunction; Yes without organ dysfunction) Clinical history of chronic pulmonary disease (Yes; No) Clinical history of hypertension (Yes; No) Clinical history of atrial fibrillation (Yes; No) Clinical history of ischemic heart disease (Yes; No) Clinical history of chronic kidney disease (No; Yes under medications; Yes in permanent renal replacement therapy or in preparation for it) Clinical history of of diseases of the hematopoietic system (Yes; No) Patient on immunosuppressive medications on hospital admission (Yes; No) White Blood Cells -WBC- (cells/mm3) Neutrophils (cells/mm3) Platelets (Plt/mm3) INR (International Normalized Ratio) C-reactive protein -CRP- (mg/L) Aspartate aminotransferase -AST- (U/L) Alanine aminotransferase -ALT- (U/L) Total bilirubin (mg/dL) Conjugated bilirubin (mg/dL) Gamma-glutamil-transpeptidase -GGT- (U/L) Serum amylase (U/L) Serum lipase (U/L) Lactate DeHydrogenase -LDH- (U/L) Choledocholithiasis (Yes; Yes with common bile duct obstruction; No) Cholangitis (Yes; No) ERCP with sphincterotomy (Yes within 24h from hospital admission; Yes within 24-48h from hospital admission; Yes within 48-72h from hospital admission; No) Acute biliary pancreatitis relapse at 30-day, 60-day, 90-day, 1 year.
- Conditions
- Acute Pancreatitis, Pancreatitis Due to Gallstones, Pancreatitis Biliary, Pancreatitis Relapsing
- Being tested
- MINERVA Machine Learning model
- Keywords
- Machine Learning, Prediction Model
- Medical subject headings
- Pancreatitis, Pancreatitis, Chronic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MINERVA Machine Learning modelDiagnostic 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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 30-day, 60-day, 90-day, 1-year
Secondary outcomes (1)
Accuracy of the MINERVA model.
Accuracy, sensibility, and specificity (AUC, area under the ROC curve) of the MINERVA Machine Learning model compared with other traditional machine learning models previously adopted in literature (such as ANN and SVM) and with statistical models (such as multiple regression).
Measured: 30-day, 60-day, 90-day, 1-year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Nov 2023
- Study start1 Jan 2024 (estimated)
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (24)Papers the sponsor has linked to this study.
- Other trials for Acute PancreatitisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 430 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Cagliari
- Sponsor type
- University, hospital or other
- Collaborators
- Federico II University, University of Campania Luigi Vanvitelli
- Responsible party
- Principal investigator, Mauro Podda, University of Cagliari
- Investigators
- Mauro Podda, MD, Study chair, University of Cagliari, Department of Surgical Science
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06124989
- Sponsor's protocol ID
- MINERVA_1
- Acronym
- MINERVA
- Status last verified
- Nov 2023
- First submitted
- 1 Nov 2023
- Last updated
- 9 Nov 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Publications
Ahmed Ali U, Issa Y, Hagenaars JC, Bakker OJ, van Goor H, Nieuwenhuijs VB, Bollen TL, van Ramshorst B, Witteman BJ, Brink MA, Schaapherder AF, Dejong CH, Spanier BW, Heisterkamp J, van der Harst E, van Eijck CH, Besselink MG, Gooszen HG, van Santvoort HC, Boermeester MA; Dutch Pancreatitis Study Group. Risk of Recurrent Pancreatitis and Progression to Chronic Pancreatitis After a First Episode of Acute Pancreatitis. Clin Gastroenterol Hepatol. 2016 May;14(5):738-46. doi: 10.1016/j.cgh.2015.12.040. Epub 2016 Jan 6.
PubMed 26772149Bagepally BS, Haridoss M, Sasidharan A, Jagadeesh KV, Oswal NK. Systematic review and meta-analysis of gallstone disease treatment outcomes in early cholecystectomy versus conservative management/delayed cholecystectomy. BMJ Open Gastroenterol. 2021 Jul;8(1):e000675. doi: 10.1136/bmjgast-2021-000675.
PubMed 34261757Chen Y, Chen TW, Wu CQ, Lin Q, Hu R, Xie CL, Zuo HD, Wu JL, Mu QW, Fu QS, Yang GQ, Zhang XM. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol. 2019 Aug;29(8):4408-4417. doi: 10.1007/s00330-018-5824-1. Epub 2018 Nov 9.
PubMed 30413966Cho JH, Jeong YH, Kim KH, Kim TN. Risk factors of recurrent pancreatitis after first acute pancreatitis attack: a retrospective cohort study. Scand J Gastroenterol. 2020 Jan;55(1):90-94. doi: 10.1080/00365521.2019.1699598. Epub 2019 Dec 10.
PubMed 31822144da Costa DW, Bouwense SA, Schepers NJ, Besselink MG, van Santvoort HC, van Brunschot S, Bakker OJ, Bollen TL, Dejong CH, van Goor H, Boermeester MA, Bruno MJ, van Eijck CH, Timmer R, Weusten BL, Consten EC, Brink MA, Spanier BWM, Bilgen EJS, Nieuwenhuijs VB, Hofker HS, Rosman C, Voorburg AM, Bosscha K, van Duijvendijk P, Gerritsen JJ, Heisterkamp J, de Hingh IH, Witteman BJ, Kruyt PM, Scheepers JJ, Molenaar IQ, Schaapherder AF, Manusama ER, van der Waaij LA, van Unen J, Dijkgraaf MG, van Ramshorst B, Gooszen HG, Boerma D; Dutch Pancreatitis Study Group. Same-admission versus interval cholecystectomy for mild gallstone pancreatitis (PONCHO): a multicentre randomised controlled trial. Lancet. 2015 Sep 26;386(10000):1261-1268. doi: 10.1016/S0140-6736(15)00274-3.
PubMed 26460661Ding N, Guo C, Li C, Zhou Y, Chai X. An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III. Biomed Res Int. 2021 Jan 28;2021:6638919. doi: 10.1155/2021/6638919. eCollection 2021.
PubMed 33575333Gurusamy KS, Nagendran M, Davidson BR. Early versus delayed laparoscopic cholecystectomy for acute gallstone pancreatitis. Cochrane Database Syst Rev. 2013 Sep 2;2013(9):CD010326. doi: 10.1002/14651858.CD010326.pub2.
PubMed 23996398Hong WD, Chen XR, Jin SQ, Huang QK, Zhu QH, Pan JY. Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis. Clinics (Sao Paulo). 2013 Jan;68(1):27-31. doi: 10.6061/clinics/2013(01)rc01. No abstract available.
PubMed 23420153Mashayekhi R, Parekh VS, Faghih M, Singh VK, Jacobs MA, Zaheer A. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol. 2020 Feb;123:108778. doi: 10.1016/j.ejrad.2019.108778. Epub 2019 Dec 11.
PubMed 31846864Hu X, Yang B, Li J, Bai X, Li S, Liu H, Zhang H, Zeng F. Individualized Prediction of Acute Pancreatitis Recurrence Using a Nomogram. Pancreas. 2021 Jul 1;50(6):873-878. doi: 10.1097/MPA.0000000000001839.
PubMed 34347724Loozen CS, Oor JE, van Ramshorst B, van Santvoort HC, Boerma D. Conservative treatment of acute cholecystitis: a systematic review and pooled analysis. Surg Endosc. 2017 Feb;31(2):504-515. doi: 10.1007/s00464-016-5011-x. Epub 2016 Jun 17.
PubMed 27317033Mador BD, Panton ON, Hameed SM. Early versus delayed cholecystectomy following endoscopic sphincterotomy for mild biliary pancreatitis. Surg Endosc. 2014 Dec;28(12):3337-42. doi: 10.1007/s00464-014-3621-8. Epub 2014 Jun 25.
PubMed 24962855Nebiker CA, Frey DM, Hamel CT, Oertli D, Kettelhack C. Early versus delayed cholecystectomy in patients with biliary acute pancreatitis. Surgery. 2009 Mar;145(3):260-4. doi: 10.1016/j.surg.2008.10.012. Epub 2009 Feb 1.
PubMed 19231577Riquelme F, Marinkovic B, Salazar M, Martinez W, Catan F, Uribe-Echevarria S, Puelma F, Munoz J, Canals A, Astudillo C, Uribe M. Early laparoscopic cholecystectomy reduces hospital stay in mild gallstone pancreatitis. A randomized controlled trial. HPB (Oxford). 2020 Jan;22(1):26-33. doi: 10.1016/j.hpb.2019.05.013. Epub 2019 Jun 22.
PubMed 31235428Sankaran SJ, Xiao AY, Wu LM, Windsor JA, Forsmark CE, Petrov MS. Frequency of progression from acute to chronic pancreatitis and risk factors: a meta-analysis. Gastroenterology. 2015 Nov;149(6):1490-1500.e1. doi: 10.1053/j.gastro.2015.07.066. Epub 2015 Aug 20.
PubMed 26299411
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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)
- ClinicalTrials.gov ID
- NCT06124989
- Acronym / sponsor ID
- MINERVA / MINERVA_1
- Conditions
- Acute Pancreatitis, Pancreatitis Due to Gallstones, Pancreatitis Biliary, Pancreatitis Relapsing
- Keywords
- Machine Learning, Prediction Model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 430 participants (estimated)
- Lead sponsor
- University of CagliariUniversity, hospital or other
- Collaborators
- Federico II University, University of Campania Luigi Vanvitelli
- Study start
- 1 Jan 2024 (estimated)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 9 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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);
- Propose a Machine Learning predictive model using a Deep Learning architecture applied to easily collectable data;
- Validate the MINERVA score on an extensive, multicentric, prospective cohort;
- 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 model will be developed on a retrospective cohort of patients (MANCTRA-1, already registered in ClinicalTrials.gov) and will be validated on a novel prospective multicentric cohort. After validation, 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.
Detailed description
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:
- 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;
- Propose a Machine Learning predictive model using a Deep Learning architecture applied to data easy to collect from patients;
- Validate the MINERVA score on an extensive, multicentric, prospective cohort;
- 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.
The MINERVA score model will be developed on a retrospective cohort of patients (MANCTRA-1, already registered in ClinicalTrials.gov) and will be validated on a novel prospective multicentric cohort.
Retrospective cohort The model development and initial training will be performed on a retrospective cohort of patients (n=692) collected during a preliminary multicentric study, the MANCTRA-1 study (approved by the Ethics Committee of the University of Cagliari Hospital, MANCTRA-1 - NCT04747990, Prot. PG/2021/7108) conducted by the PI (Dr. Mauro Podda) and the University of Cagliari local responsible of the MINERVA project.
Prospective cohort A total of 430 patients will be recruited in the prospective cohort of the MINERVA study.
Methods The MINERVA score will be grounded on a Machine Learning model that will be developed and trained on a retrospective cohort and validated on a prospective cohort of patients.
All model variables will be processed with kernel Principal Component Analysis (kPCA).
The Convex Hull of the scatterplot of the main components will be computed and the smallest rectangle will be extracted. The rectangle will be transformed into a 2d image with a fixed resolution using feature averaging and normalization.
The model will be developed at the University of Naples Federico II by Dr. Daniela Pacella with the Machine Learning expertise and supervision.
To prevent overfitting, the dataset will be split into training set, test set and validation set. Additionally, k-fold cross-validation will be used. The performance of the MINERVA model will be evaluated using the most adopted measures of accuracy, such as precision, recall and AUC (Area Under the ROC Curve). Additionally, its performance will be compared with that achieved using traditional machine learning methods (SVM, ANN). Missing data will be handled with imputation methods.
Variables Age (Years) Sex (Male:Female) Previous episodes of biliary pancreatitis (Yes; No) Admitting speciality (HepatoPancreatoBiliary surgery, General surgery, Internal medicine, Gastroenterology) Body mass index -BMI- (Kg/m2) Clinical history of diabetes (No diabetes; Yes with organ dysfunction; Yes without organ dysfunction) Clinical history of chronic pulmonary disease (Yes; No) Clinical history of hypertension (Yes; No) Clinical history of atrial fibrillation (Yes; No) Clinical history of ischemic heart disease (Yes; No) Clinical history of chronic kidney disease (No; Yes under medications; Yes in permanent renal replacement therapy or in preparation for it) Clinical history of of diseases of the hematopoietic system (Yes; No) Patient on immunosuppressive medications on hospital admission (Yes; No) White Blood Cells -WBC- (cells/mm3) Neutrophils (cells/mm3) Platelets (Plt/mm3) INR (International Normalized Ratio) C-reactive protein -CRP- (mg/L) Aspartate aminotransferase -AST- (U/L) Alanine aminotransferase -ALT- (U/L) Total bilirubin (mg/dL) Conjugated bilirubin (mg/dL) Gamma-glutamil-transpeptidase -GGT- (U/L) Serum amylase (U/L) Serum lipase (U/L) Lactate DeHydrogenase -LDH- (U/L) Choledocholithiasis (Yes; Yes with common bile duct obstruction; No) Cholangitis (Yes; No) ERCP with sphincterotomy (Yes within 24h from hospital admission; Yes within 24-48h from hospital admission; Yes within 48-72h from hospital admission; No) Acute biliary pancreatitis relapse at 30-day, 60-day, 90-day, 1 year.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (6 listed)
- Acute pancreatitis of etiology other than gallstones;
- Moderately-severe pancreatitis;
- Severe pancreatitis;
- Presence of pancreatic necrosis;
- Pregnant patients;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MINERVA Machine Learning modelThe 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. | Diagnostic test | – |
Primary outcome
Number of patients with recurrence of biliary acute pancreatitis.
Time frame: 30-day, 60-day, 90-day, 1-year
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.
1 secondary outcome
Accuracy of the MINERVA model.
Time frame: 30-day, 60-day, 90-day, 1-year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2023.
- Background: PubMed 26772149Ahmed Ali U, Issa Y, Hagenaars JC, Bakker OJ, van Goor H, Nieuwenhuijs VB, Bollen TL, van Ramshorst B, Witteman BJ, Brink MA, Schaapherder AF, Dejong CH, Spanier BW, Heisterkamp J, van der Harst E, van Eijck CH, Besselink MG, Gooszen HG, van Santvoort HC, Boermeester MA; Dutch Pancreatitis Study Group. Risk of Recurrent Pancreatitis and Progression to Chronic Pancreatitis After a First Episode of Acute Pancreatitis. Clin Gastroenterol Hepatol. 2016 May;14(5):738-46. doi: 10.1016/j.cgh.2015.12.040. Epub 2016 Jan 6.
- Background: PubMed 34261757Bagepally BS, Haridoss M, Sasidharan A, Jagadeesh KV, Oswal NK. Systematic review and meta-analysis of gallstone disease treatment outcomes in early cholecystectomy versus conservative management/delayed cholecystectomy. BMJ Open Gastroenterol. 2021 Jul;8(1):e000675. doi: 10.1136/bmjgast-2021-000675.
- Background: PubMed 30413966Chen Y, Chen TW, Wu CQ, Lin Q, Hu R, Xie CL, Zuo HD, Wu JL, Mu QW, Fu QS, Yang GQ, Zhang XM. Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol. 2019 Aug;29(8):4408-4417. doi: 10.1007/s00330-018-5824-1. Epub 2018 Nov 9.
- Background: PubMed 31822144Cho JH, Jeong YH, Kim KH, Kim TN. Risk factors of recurrent pancreatitis after first acute pancreatitis attack: a retrospective cohort study. Scand J Gastroenterol. 2020 Jan;55(1):90-94. doi: 10.1080/00365521.2019.1699598. Epub 2019 Dec 10.
- Background: PubMed 26460661da Costa DW, Bouwense SA, Schepers NJ, Besselink MG, van Santvoort HC, van Brunschot S, Bakker OJ, Bollen TL, Dejong CH, van Goor H, Boermeester MA, Bruno MJ, van Eijck CH, Timmer R, Weusten BL, Consten EC, Brink MA, Spanier BWM, Bilgen EJS, Nieuwenhuijs VB, Hofker HS, Rosman C, Voorburg AM, Bosscha K, van Duijvendijk P, Gerritsen JJ, Heisterkamp J, de Hingh IH, Witteman BJ, Kruyt PM, Scheepers JJ, Molenaar IQ, Schaapherder AF, Manusama ER, van der Waaij LA, van Unen J, Dijkgraaf MG, van Ramshorst B, Gooszen HG, Boerma D; Dutch Pancreatitis Study Group. Same-admission versus interval cholecystectomy for mild gallstone pancreatitis (PONCHO): a multicentre randomised controlled trial. Lancet. 2015 Sep 26;386(10000):1261-1268. doi: 10.1016/S0140-6736(15)00274-3.
- Background: PubMed 33575333Ding N, Guo C, Li C, Zhou Y, Chai X. An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III. Biomed Res Int. 2021 Jan 28;2021:6638919. doi: 10.1155/2021/6638919. eCollection 2021.
- Background: PubMed 23996398Gurusamy KS, Nagendran M, Davidson BR. Early versus delayed laparoscopic cholecystectomy for acute gallstone pancreatitis. Cochrane Database Syst Rev. 2013 Sep 2;2013(9):CD010326. doi: 10.1002/14651858.CD010326.pub2.
- Background: PubMed 23420153Hong WD, Chen XR, Jin SQ, Huang QK, Zhu QH, Pan JY. Use of an artificial neural network to predict persistent organ failure in patients with acute pancreatitis. Clinics (Sao Paulo). 2013 Jan;68(1):27-31. doi: 10.6061/clinics/2013(01)rc01. No abstract available.
- Background: PubMed 31846864Mashayekhi R, Parekh VS, Faghih M, Singh VK, Jacobs MA, Zaheer A. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol. 2020 Feb;123:108778. doi: 10.1016/j.ejrad.2019.108778. Epub 2019 Dec 11.
- Background: PubMed 34347724Hu X, Yang B, Li J, Bai X, Li S, Liu H, Zhang H, Zeng F. Individualized Prediction of Acute Pancreatitis Recurrence Using a Nomogram. Pancreas. 2021 Jul 1;50(6):873-878. doi: 10.1097/MPA.0000000000001839.
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- Background: PubMed 24962855Mador BD, Panton ON, Hameed SM. Early versus delayed cholecystectomy following endoscopic sphincterotomy for mild biliary pancreatitis. Surg Endosc. 2014 Dec;28(12):3337-42. doi: 10.1007/s00464-014-3621-8. Epub 2014 Jun 25.
- Individual participant dataSharing planned: Yes
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Mauro Podda, MD study chair
University of Cagliari, Department of Surgical Science
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Pancreatitis and Pancreatitis Due to Gallstones, run by University of Cagliari at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Mauro Podda, MD (07051096571, mauro.podda@unica.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of patients with recurrence of biliary acute pancreatitis. (time frame: 30-day, 60-day, 90-day, 1-year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06124989, which also lists 24 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Nov 2023.