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Symbolic Regression Model To Predict Choledocholithiasis

Symbolic Regression Model To Predict Choledocholithiasis: Prospective Validation

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04410848
Acronym
(SymChole)
Enrollment
200
Registered
2020-06-01
Start date
2019-06-07
Completion date
2020-07-31
Last updated
2020-06-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Common Bile Duct Calculi

Brief summary

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.

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

OTHERTo determine the diagnostic of Choledocholithiasis with symbolic regression model

To determine the diagnostic of Choledocholithiasis with symbolic regression model

Sponsors

Hospital Universitario Dr. Jose E. Gonzalez
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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.

Design outcomes

Primary

MeasureTime frameDescription
To validate prospectively a symbolic regression model to predict choledocholithiasis72 horasTo validate a model to predict choledocholithiasis compared clinical predictors.

Countries

Mexico

Contacts

Primary ContactCarlos A. Herrera Figueroa, M.D
carale30@hotmail.com+52 8127319026
Backup ContactJosé A. González González, MD
jalbertogastro@gmail.com

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026