Evidence map›Paper›PMID 39472315›Full record

ArticleCardiovascular and interventional radiology2025

Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement.

Tarig Elhakim, Arian Mansur, Jordan Kondo, Omar Moustafa Fathy Omar, Khalid Ahmed, Azadeh Tabari, Allison Brea, Gabriel Ndakwah, Shams Iqbal, Andrew S Allegretti and 4 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cardiovascular and interventional radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

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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

14 authors.

Tarig ElhakimPerelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. Tarigelhakim@gmail.com.ORCID http://orcid.org/0000-0002-5038-9232
Arian MansurHarvard Medical School, Boston, MA, USA.
Jordan KondoBrigham and Womens, Boston, MA, USA.
Omar Moustafa Fathy OmarUniversity of Connecticut School of Medicine, Farmington, CT, USA.
Khalid AhmedUniversity of Minnesota School of Medicine, Minneapolis, MN, USA.
Azadeh TabariMassachusetts General Hospital, Boston, MA, USA.
Allison BreaTufts University School of Medicine, Boston, MA, USA.
Gabriel NdakwahMassachusetts Institute of Technology, Boston, USA.
Shams IqbalMassachusetts General Hospital, Boston, MA, USA.
Andrew S AllegrettiMassachusetts General Hospital, Boston, MA, USA.
Florian J FintelmannMassachusetts General Hospital, Boston, MA, USA.
Eric Wehrenberg-KleeMassachusetts General Hospital, Boston, MA, USA.
Christopher BridgeMassachusetts General Hospital, Boston, MA, USA.
Dania DayeMassachusetts General Hospital, Boston, MA, USA.

Funding

Personalizing Therapies for Acute Kidney Injury in CirrhosisK23DK128567 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Andrew S Allegretti · 2022 to 2026
$982k
NIDDK NIH HHS K23 DK128567
6 · The paper itself

Abstract

purposeTo determine the association of machine learning-derived CT body composition and 90-day mortality after transjugular intrahepatic portosystemic shunt (TIPS) and to assess its predictive performance as a complement to Model for End-Stage Liver Disease (MELD) score for mortality risk prediction. MATERIALS AND

methodsThis retrospective multi-center cohort study included patients who underwent TIPS from 1995 to 2018 and had a contrast-enhanced CT abdomen within 9 months prior to TIPS and at least 90 days of post-procedural clinical follow-up. A machine learning algorithm extracted CT body composition metrics at L3 vertebral level including skeletal muscle area (SMA), skeletal muscle index (SMI), skeletal muscle density (SMD), subcutaneous fat area (SFA), subcutaneous fat index (SFI), visceral fat area (VFA), visceral fat index (VFI), and visceral-to-subcutaneous fat ratio (VSR). Independent t-tests, logistic regression models, and ROC curve analysis were utilized to assess the association of those metrics in predicting 90-day mortality.

resultsA total of 122 patients (58 ± 11.8, 68% male) were included. Patients who died within 90 days of TIPS had significantly higher MELD (18.9 vs. 11.9, p < 0.001) and lower SMA (123 vs. 144.5, p = 0.002), SMI (43.7 vs. 50.5, p = 0.03), SFA (122.4 vs. 190.8, p = 0.009), SFI (44.2 vs. 66.7, p = 0.04), VFA (105.5 vs. 171.2, p = 0.003), and VFI (35.7 vs. 57.5, p = 0.02) compared to those who survived past 90 days. There were no significant associations between 90-day mortality and BMI (26 vs. 27.1, p = 0.63), SMD (30.1 vs. 31.7, p = 0.44), or VSR (0.97 vs. 1.03, p = 0.66). Multivariable logistic regression showed that SMA (OR = 0.97, p < 0.01), SMI (OR = 0.94, p = 0.03), SFA (OR = 0.99, p = 0.01), and VFA (OR = 0.99, p = 0.02) remained significant predictors of 90-day mortality when adjusted for MELD score. ROC curve analysis demonstrated that including SMA, SFA, and VFA improves the predictive power of MELD score in predicting 90-day mortality after TIPS (AUC, 0.84; 95% CI: 0.77, 0.91; p = 0.02).

conclusionCT body composition is positively predictive of 90-day mortality after TIPS and improves the predictive performance of MELD score. LEVEL OF EVIDENCE: Level 3, Retrospective multi-center cohort study.

Indexed as

Body CompositionEnd Stage Liver DiseaseMachine LearningPortasystemic Shunt, Transjugular IntrahepaticTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentArtificial intelligenceCT body compositionMachine learningTIPS prognostication

Identifiers

PMID39472315
PMCPMC11790367

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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.