Evidence map›Paper›PMID 35186364›Full record

ArticleLiver research (Beijing, China)2021

Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis.

Camellia Dalai, John Azizian, Harry Trieu, Anand Rajan, Formosa Chen, Tien Dong, Simon Beaven, James H Tabibian

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Liver research (Beijing, China), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Article
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  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Camellia DalaiUCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
John AzizianUCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
Harry TrieuKeck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Anand RajanUCLA-Olive View Internal Medicine Residency Program, Department of Medicine, Olive View-UCLA Medical Center, Sylmar, CA, USA.
Formosa ChenDepartment of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Tien DongTamar and Vatche Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Simon BeavenTamar and Vatche Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
James H TabibianTamar and Vatche Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
University of California, Los Angeles · USOlive View-UCLA Medical Center · USUniversity of Southern California · US

Funding

Mayo Clinic Center for Translational Science ActivitiesUL1TR000135 · NCATS · MAYO CLINIC ROCHESTER · PI KHOSLA, SUNDEEP · 2012 to 2015
$41.2M
NCATS NIH HHS UL1 TR000135
6 · The paper itself

Abstract

BACKGROUND AND

aimsNoninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs).

methodsClinical data of consecutive patients undergoing first-ever ERCP for suspected choledocholithiasis from 2015-2019 were abstracted from a prospectively-maintained database. Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis. MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound (US) imaging only and separately using all available noninvasive imaging (US/CT/magnetic resonance cholangiopancreatography). The diagnostic performance of American Society for Gastrointestinal Endoscopy (ASGE) "high-likelihood" criteria was compared to MLMs.

resultsWe identified 270 patients (mean age 46 years, 62.2% female, 73.7% Hispanic/Latino, 59% with noninvasive imaging positive for choledocholithiasis) with native papilla who underwent ERCP for suspected choledocholithiasis, of whom 230 (85.2%) were found to have ERCP-confirmed choledocholithiasis. Logistic regression identified choledocholithiasis on noninvasive imaging (odds ratio (OR) = 3.045,

conclusionsMLMs using age, sex, race, presence of diabetes, fever, body mass index (BMI), total bilirubin, maximum CBD diameter, and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.

Indexed as

Bile duct disordersCommon bile duct stonesEndoscopic retrograde cholangiopancreatography (ERCP)GallstonesMachine learning models (MLMs)Noninvasive imaging

Identifiers

PMID35186364
PMCPMC8855981
OpenAlexW3210984556

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.