Artificial Intelligence Application in Predicting Disease Severity in Acute Pancreatitis
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Bezmialem Vakif University
- Enrollment
- 1,334
- Locations
- 1
- Primary Endpoint
- Accurately estimation of the severity of the disease by machine learning method
Study Overview
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.
Detailed Description
In a retrospective way, 1550 patients who were followed up at the Gastroenterology Clinic of Bezmialem Foundation University between October 2010 and February 2020 period and who were diagnosed with AP according to Atlanta criteria were screened. After the removal of 216 patients with missing data, 1334 patients were included in the study for evaluation.
- Patient demographic information; [age (yo), gender (male/female), cigarette/alcohol usage (as yes or no)], clinical information; [height (centimeters), weight (kilograms), BMI (as kg/m2), presence of diabetes mellitus and hypertension (yes or no)], etiology of AP such as gallstones, alcohol, etc., and laboratory tests those taken within the first 24 hours of the admission; [CRP level (mg/dl, normally: 0-5), BUN level (mg/dl, normally; 9,8 - 20,1), creatinine level (mg/dl, normally; 0,57 - 1,11), number of leukocytes (normally 4.5 to 11.0 ×109/L) and hematocrit level (%, normally: 35,5-48%)], as well as Balthazar tomographic scoring [0: normal, 1: an increase in pancreatic size, 2: inflammatory changes in pancreatic tissue and peripancreatic fatty tissue, 3: irregularly bordered, single fluid collection, 4: irregularly bordered 2 or more fluid collections, 5 to 10 different degrees of necrosis)], will be recorded in the excel file.
- Revised Atlanta scoring will also be recorded within a week period of hospital admission as mild, moderate, and severe scores. Infected pancreatic necrosis and sepsis that developed during the course of acute pancreatitis will be accepted as severe acute pancreatitis due to the inadequacy of some issues in Atlanta scoring. The severity of the disease will be evaluated according to the Atlanta scores. And the results of the artificial intelligence study will be matched according to the results of Atlanta scoring.
- Complications are classified as 0; none, 2; local complications: pseudocyst, abscess, necrosis, thrombosis, and mesenteric panniculitis, 3; systemic complications: lung, kidney, gastrointestinal and cardiovascular complications, 4; mixed serious complications/co-morbidity situations, 5: infectious and septic complications.
- Additionally, invasive procedure requirements such as endoscopic ultrasonography (EUS), endoscopic retrograde cholangiopancreatography (ERCP) (as yes or no), length of hospital stay (less than 10 days or more than 11 days), intensive care unit requirement (present or not), number of future AP attacks (in duration after a month of hospital admission, as of one attack or more than one attack), and survival (death, alive) will also be recorded.
Machine Learning Algorithm is used: Gradient Boosted Ensemble Trees Trees. ("Greedy Function Approximation: A Gradient Boosting Machine" by Jerome H. Friedman (1999)). The dataset has been partitioned with a 90%-10% ratio. 10% is for validation and 90% is for AI machine learning. 90% machine learning part has also been divided into two parts as 70% for AI Learning and 30% for testing the learning. For this purpose, 5-fold stratified sampling has been used
Artificial Intelligence Methods of the Study
Features Used for AI Machine Learning:
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to 100 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients with acute pancreatitis diagnosis who admitted to ER within 24 hours after the beginning of abdominal pain
Exclusion Criteria
- •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
Outcomes
Primary Outcomes
Accurately estimation of the severity of the disease by machine learning method
Time Frame: Within a week.
Severity is described as mild, moderate, and severe acute pancreatitis according to the revised Atlanta criteria.
Secondary Outcomes
- 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)
Investigators
Ali Tüzün İnce
Principal investigator
Bezmialem Vakif University