Symbolic Regression Model To Predict Choledocholithiasis

Overview

Choledocholithiasis refers to the presence of gallstones within the common bile duct. It is proposed to look for markers that help in the diagnosis and in differentiating between retained and migrated gallstones. The selection of patients is a very important aspect, due to the economic aspects and possible complications. Taking advantage of the development of technology, the improvement in computer systems, the use of artificial intelligence and a symbolic regression model that works to predict the presence of choledocholithiasis and provide evidence that clarifies the treatment of patients with this pathology, especially in this group where there is a bigger controversy.

Full Title of Study: “Symbolic Regression Model To Predict Choledocholithiasis: Prospective Validation”

Study Type

  • Study Type: Observational [Patient Registry]
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: May 30, 2020

Detailed Description

Choledocholithiasis refers to the presence of gallstones within the common bile duct. It is proposed to look for markers that help in the diagnosis and in differentiating between retained and migrated gallstones. The selection of patients to perform endoscopic retrograde cholangiopancreatography (ERCP) is a very important aspect, due to the economic aspects and possible complications. By making a proper patient selection for additional studies or procedure, then the costs, complications and days of stay would be reduced. Avoiding the unnecessary use of ERCP would avoid its complications. Taking advantage of the development of technology, the improvement in computer systems, the use of artificial intelligence and a Symbolic Regression Model that works to predict the presence of choledocholithiasis and provide evidence that clarifies the treatment of patients with this pathology, especially in this group where there is a bigger controversy.

Having the historical database of the University Hospital (HU), regarding clinical, laboratory and image variables of patients with suspected choledocholithiasis, using a symbolic regression method, several randomly formed equations are generated. Each equation deducts its coefficient of linear correlation (Pearson's correlation).

For the following study we admitted to the emergency department of adults at the University Hospital all patients with clinical suspicion of choledocholithiasis, who meet the inclusion criterion. The study which is realized is a normal one based on the method of clinical predictors, obtaining laboratory studies, image studies, and patient management will be carried out based on the method of clinical predictors. The calculation is made with the equation obtained, and the patient is monitored until discharge. The calculation obtained from the equation will not be taken into account for the decisions in the management of the patient. The variables studied as white blood cells, total bilirubin values, direct bilirubin, indirect bilirubin, Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST), alkaline phosphatase (AP), gamma-glutamyl transpeptidase (GGT) at admission will be taken from the clinical record. Transabdominal ultrasonography will be performed upon admission by the diagnostic radiology department of the HU and the size of the bile duct in mm, presence of gallbladder gallstones and bile duct stones will be taken from the report. ERCP, magnetic resonance cholangiopancreatography (MRCP) or intraoperative cholangiography will be performed and one will be taken as a confirmation of choledocolithiasis, and its absence would rule it out.

Interventions

  • Other: To determine the diagnostic of Choledocholithiasis with symbolic regression model
    • To determine the diagnostic of Choledocholithiasis with symbolic regression model

Arms, Groups and Cohorts

  • All patient with suspicion of choledocholithiasis
    • Patient with the suspect of common biliary duct stone for pain type colic in the right upper quadrant abdomen, the elevation of bilirubin, alkaline phosphatase, pancreatitis, dilated common bile duct and cholangitis. According to the criteria to assign the risk of choledocholithiasis. We are going to validate a scale based on intelligence artificial compared to the clinical predictor.

Clinical Trial Outcome Measures

Primary Measures

  • To validate prospectively a symbolic regression model to predict choledocholithiasis
    • Time Frame: 72 horas
    • To validate a model to predict choledocholithiasis compared clinical predictors.

Participating in This Clinical Trial

Inclusion Criteria

  • Patients above 18 with clinical suspicion of choledocholithiasis by biliary-type pain, laboratory testing that reveals a cholestatic pattern of liver test abnormalities, biliary pancreatitis or dilated common bile duct

Exclusion Criteria

  • Patient with a history of cholecystectomy
  • History of previous ERCP or surgery involving bile duct
  • Patient who could not be followed
  • Patient with other pathology that causes alteration of liver function test.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Hospital Universitario Dr. Jose E. Gonzalez
  • Provider of Information About this Clinical Study
    • Principal Investigator: Dr. med. Hector Eloy Tamez Perez, Clinical Professor – Hospital Universitario Dr. Jose E. Gonzalez
  • Overall Official(s)
    • José A. González González, MD, Study Director, José Eleuterio González University Hospital
  • Overall Contact(s)
    • Carlos A. Herrera Figueroa, M.D, +52 8127319026, carale30@hotmail.com

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