Evidence map›Paper›PMID 38017436›Full record

ArticleClinical proteomics2023

Validation of the novel GLAS algorithm as an aid in the detection of liver fibrosis and cirrhosis based on GP73, LG2m, age, and sex.

Philip M Hemken, Xuzhen Qin, Lori J Sokoll, Laurel Jackson, Fan Feng, Peng Li, Susan H Gawel, Bailin Tu, Zhihong Lin, James Hartnett and 13 more

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

Article in Clinical proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 16% 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, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  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

23 authors at 6 institutions in 3 countries.

Philip M Hemken *Diagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA. philip.hemken@abbott.com.
Xuzhen Qin *Department of Laboratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China.
Lori J SokollDivision of Clinical Chemistry, Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA.
Laurel JacksonDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Fan FengDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Peng LiDepartment of Laboratory Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College Hospital, Beijing, China.
Susan H GawelDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Bailin TuDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Zhihong LinDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
James HartnettDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
David HawksworthDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Bryan C TiemanDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA.
Toru YoshimuraResearch & Development, Abbott Japan, Matsudo, Japan.
Hideki KinukawaResearch & Development, Abbott Japan, Matsudo, Japan.
Shaohua NingDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Enfu LiuDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Fanju MengDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Fei ChenDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Juru MiaoDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Xuan MiDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Xin TongDiagnostics Discovery Research & Development, Abbott Diagnostics, Shanghai, China.
Daniel W ChanDivision of Clinical Chemistry, Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA.
Gerard J DavisDiagnostics Discovery Research & Development, Abbott Diagnostics, 100 Abbott Park Road AP20, Abbott Park, IL, 60064, USA. gerard.davis@abbott.com.
Abbott Fund · USAbbott (China) · CNAbbott (Japan) · JPJohns Hopkins University · USChinese Academy of Medical Sciences & Peking Union Medical College · CNPeking Union Medical College Hospital · CN

Funding

National High Level Hospital Clinical Research Funding 2022-PUMCH-A-059, 2022-PUMCH-B-124
6 · The paper itself

Abstract

backgroundDiagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. In this pilot study, we tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with liver disease and healthy individuals in two independent cohorts.

methodsTo develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model.

resultsGP73 and LG2m concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90-0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90-0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls.

conclusionsGP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for detection of liver disease in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.

Indexed as

DiagnosticHepatitisImmunoassayLiver DiseasePredictive modelRisk assessment

Identifiers

PMID38017436
PMCPMC10683319
OpenAlexW4389093033

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.