Evidence map›Paper›PMID 37864738›Full record

ArticleDigestive diseases and sciences2023

Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

Saroja Bangaru, Ram Sundaresh, Anna Lee, Nicole Prause, Frank Hao, Tien S Dong, Monica Tincopa, George Cholankeril, Nicole E Rich, Jenna Kawamoto and 5 more

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

Article in Digestive diseases and sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

15 authors at 6 institutions in 1 country.

Saroja BangaruVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Ram SundareshDavid Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
Anna LeeDavid Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
Nicole PrauseVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Frank HaoDepartment of Radiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Tien S DongVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Monica TincopaLiver Center, University of California, San Diego, San Diego, CA, 92093, USA.
George CholankerilSection of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.
Nicole E RichUT Southwestern Medical Center, Division of Digestive and Liver Diseases and Harold C. Simmons Comprehensive Cancer Center, Dallas, TX, 75390, USA.
Jenna KawamotoGreater Los Angeles Veterans Affairs Healthcare System, Gastroenterology, Hepatology and Parenteral Nutrition, Los Angeles, CA, 90075, USA.
Debika BhattacharyaDivision of Infectious Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Steven B HanVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Arpan A PatelVatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Magda ShaheenCollege of Medicine, Charles R Drew University, Los Angeles, CA, USA.
Jihane N BenhammouGreater Los Angeles Veterans Affairs Healthcare System, Gastroenterology, Hepatology and Parenteral Nutrition, Los Angeles, CA, 90075, USA. jbenhammmou@mednet.ucla.edu.ORCID 0000-0003-2442-5145
University of California, Los Angeles · USBaylor College of Medicine · USCharles R. Drew University of Medicine and Science · USHarold C. Simmons Comprehensive Cancer CenterUniversity of California San Diego · USVA Greater Los Angeles Healthcare System · US

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
NCATS NIH HHS UL1 TR001881
6 · The paper itself

Abstract

BACKGROUND AND

aimsNonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.

methodsWe identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards.

resultsThe cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%).

conclusionOur HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.

Indexed as

Elasticity Imaging TechniquesLiver Diseases, AlcoholicNon-alcoholic Fatty Liver DiseaseVeteransBiopsyElectronic Health RecordsHumansLiverLiver CirrhosisProspective StudiesROC CurveElastographyModelNonalcoholic fatty liver diseasePrediction

Identifiers

PMID37864738
PMCPMC10635943
OpenAlexW4387845443

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.