Artificial Intelligence Prediction for the Severity of Acute Pancreatitis

NCT04735055 | Completed

• 1 other identifier: MLKkrm986%
• Study Type: observational
• Target: 1,334
• Locations: 1 country
• Sites: 1

Brief Summary

The incidence of acute pancreatitis (AP) is increasing nowadays. The diagnosis of AP is defined according to Atlanta criteria with the presence of two of the following 3 findings; a) characteristic abdominal pain b) amylase and lipase values ≥3 times c) AP diagnosis in ultrasonography (USG), magnetic resonance imaging (MRI), or computerized tomography (CT) imaging. While 80% of the disease has a mild course, 20% is severe and requires intensive care treatment. Mortality varies between 10-25% in severe (severe) AP, while it is 1-3% in mild AP. Scoring systems with clinical, laboratory, and radiological findings are used to evaluate the severity of the disease. Advanced age (\>70yo), obesity (as body mass index (BMI, as kg/m2), cigarette and alcohol usage, blood urea nitrogen (BUN) ≥20 mg/dl, increased creatinine, C reactive protein level (CRP) \>120mg/dl, decreased or increased Hct levels, ≥8 Balthazar score on abdominal CT implies serious AP. According to the revised Atlanta criteria, three types of severity are present in AP. Mild (no organ failure and no local complications), moderate (local complications such as pseudocyst, abscess, necrosis, vascular thrombosis) and/or transient systemic complications (less than 48h) and severe (long-lasting systemic complications (\>48h); organ insufficiencies such as lung, heart, gastrointestinal and renal). Although Atlanta scoring is considered very popular today, it still seems to be in need of revision due to some deficiencies in the subjects of infected necrosis, non-pancreatic infection and non-pancreatic necrosis, and the dynamic nature of organ failure. Even though the presence of 30 severity scoring systems (the most accepted one is the APACHE 2 score among them), none of them can definitely predict which patient will have very severe disease and which patient will have a mild course has not been discovered yet. Today, artificial intelligence (machine learning) applications are used in many subjects in medicine (such as diagnosis, surgeries, drug development, personalized treatments, gene editing skills). Studies on machine learning in determining the violence in AP have started to appear in the literature. The purpose of this study is to investigate whether the artificial intelligence (AI) application has a role in determining the disease severity in AP.

Conditions

Acute Pancreatitis; Artificial Intelligence; Outcome, Fatal

Keywords

Acute pancreatitis, Artificial intelligence,

Outcome Measures

Primary Outcomes (1)

• Accurately estimation of the severity of the disease by machine learning method

  Severity is described as mild, moderate, and severe acute pancreatitis according to the revised Atlanta criteria.

  Within a week.

Secondary Outcomes (5)

• Invasive procedure requirement

  Within a week

• Intensive care unit requirement

  Within a week

• Survival status

  Within a week

• Length of hospital stay

  Within a month

• Number of AP attacks

  After a month of hospital admission as one attack or more than one attack

Study Arms (2)

Artificial intelligence (AI) machine learning group

90% machine learning part has also been divided into 2 parts as 70% for AI learning and 30% for testing the learning. 70% of the acute pancreatitis patients (approximately 840 pts) will form the model training group of the study. 30% of the acute pancreatitis patients (approximately 360 pts) will form the testing group of the study. Since cross-validation will also be applied to the model here, the data will also change within itself, and also the distribution will be optimized to increase the predictive power.

Validation group

10% of the acute pancreatitis patients (approximately 134) will form the validation group of the study. Since cross-validation will also be applied to the model here, the data will also change within itself, and also the distribution will be optimized to increase the predictive power.

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with acute pancreatitis diagnosis according to the Atlanta criteria

You may qualify if:

• \- Patients with acute pancreatitis diagnosis who admitted to ER within 24 hours after the beginning of abdominal pain

You may not qualify if:

• Patients who sign a treatment rejection form immediately after admission to the hospital and leave the hospital
• Patients with uncompleted data
• Psychiatric patients
• Patients with very poor general conditions

Sponsors & Collaborators

• Bezmialem Vakif University: lead
• Medipol University: collaborator

Study Sites (1)

Bezmialem Vakif University, Gastroenterology Clinic

Istanbul, 34093, Turkey (Türkiye)

Location

Related Publications (13)

• Banks PA, Bollen TL, Dervenis C, Gooszen HG, Johnson CD, Sarr MG, Tsiotos GG, Vege SS; Acute Pancreatitis Classification Working Group. Classification of acute pancreatitis--2012: revision of the Atlanta classification and definitions by international consensus. Gut. 2013 Jan;62(1):102-11. doi: 10.1136/gutjnl-2012-302779. Epub 2012 Oct 25.

  PMID: 23100216 BACKGROUND

• Fei Y, Gao K, Li WQ. Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis. Pancreatology. 2018 Dec;18(8):892-899. doi: 10.1016/j.pan.2018.09.007. Epub 2018 Sep 26.

  PMID: 30268673 BACKGROUND

• van den Heever M, Mittal A, Haydock M, Windsor J. The use of intelligent database systems in acute pancreatitis--a systematic review. Pancreatology. 2014 Jan-Feb;14(1):9-16. doi: 10.1016/j.pan.2013.11.010. Epub 2013 Dec 4.

  PMID: 24555973 BACKGROUND

• Yoldas O, Koc M, Karakose N, Kilic M, Tez M. Prediction of clinical outcomes using artificial neural networks for patients with acute biliary pancreatitis. Pancreas. 2008 Jan;36(1):90-2. doi: 10.1097/MPA.0b013e31812e964b. No abstract available.

  PMID: 18192888 BACKGROUND

• Pearce CB, Gunn SR, Ahmed A, Johnson CD. Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein. Pancreatology. 2006;6(1-2):123-31. doi: 10.1159/000090032. Epub 2005 Dec 1.

  PMID: 16327290 BACKGROUND

• Andersson B, Andersson R, Ohlsson M, Nilsson J. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology. 2011;11(3):328-35. doi: 10.1159/000327903. Epub 2011 Jul 9.

  PMID: 21757970 BACKGROUND

• Qiu Q, Nian YJ, Guo Y, Tang L, Lu N, Wen LZ, Wang B, Chen DF, Liu KJ. Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis. BMC Gastroenterol. 2019 Jul 4;19(1):118. doi: 10.1186/s12876-019-1016-y.

  PMID: 31272385 BACKGROUND

• Greedy function approximation: A gradient boostingmachine.

  BACKGROUND

• Clustering, A. (2009). Clustering Categorical Data Using Hierarchies. Engineering and Technology, 1(2), 334-339.

  BACKGROUND

• Silahtaroğlu, G. (2009). An Attribute-Centre Based Decision Tree Classification Algorithm. Engineering and Technology, 302-306.

  BACKGROUND

• Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3). https://doi.org/10.1007/s10462-020-09896-5.

  BACKGROUND

• Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3). https://doi.org/10.1007/s10462-020-09896-5

  BACKGROUND

• Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., … Wiswedel, B. (2009). KNIME - the Konstanz information miner. ACM SIGKDD Explorations Newsletter. https://doi.org/10.1145/1656274.1656280

  BACKGROUND

MeSH Terms

Conditions

Pancreatitis

Condition Hierarchy (Ancestors)

Pancreatic Diseases; Digestive System Diseases

Study Officials

• Gökhan Silahtaroğlu, Prof.

  Medipol University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal investigator

Study Record Dates

• First Submitted: September 30, 2020
• First Posted: February 2, 2021
• Study Start: September 3, 2020
• Primary Completion: September 23, 2020
• Study Completion: September 30, 2020
• Last Updated: May 5, 2021

Record last verified: 2021-04

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)