Evidence map›Paper›PMID 38777903›Full record

ArticleEuropean radiology2024

Dual-Energy CT muscle fat fraction as a new imaging biomarker of body composition and survival predictor in critically ill patients.

Jennifer Erley, Kevin Roedl, Ann-Kathrin Ozga, Geraldine de Heer, Niklas Schubert, Julia Breckow, Christoph Burdelski, Enver Tahir, Stefan Kluge, Tobias B Huber and 3 more

Abstract read
In one paragraph

Article in European radiology, 2024. 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
–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

7 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

13 authors.

Jennifer ErleyDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Kevin RoedlDepartment of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Ann-Kathrin OzgaInstitute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Geraldine de HeerDepartment of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Niklas SchubertDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Julia BreckowDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Christoph BurdelskiDepartment of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Enver TahirDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Stefan KlugeDepartment of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Tobias B HuberIII. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Jin YamamuraDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Gerhard AdamDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Isabel MolwitzDepartment of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. i.molwitz@uke.de.ORCID http://orcid.org/0000-0003-4542-0140

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze changes in the muscular fat fraction (FF) during immobilization at the intensive care unit (ICU) using dual-energy CT (DECT) and evaluate the predictive value of the DECT FF as a new imaging biomarker for morbidity and survival.

methodsImmobilized ICU patients (n = 81, 43.2% female, 60.3 ± 12.7 years) were included, who received two dual-source DECT scans (CT1, CT2) within a minimum interval of 10 days between 11/2019 and 09/2022. The DECT FF was quantified for the posterior paraspinal muscle by two radiologists using material decomposition. The skeletal muscle index (SMI), muscle radiodensity attenuation (MRA), subcutaneous-/ visceral adipose tissue area (SAT, VAT), and waist circumference (WC) were assessed. Reasons for ICU admission, clinical scoring systems, therapeutic regimes, and in-hospital mortality were noted. Linear mixed models, Cox regression, and intraclass correlation coefficients were employed.

resultsBetween CT1 and CT2 (median 21 days), the DECT FF increased (from 20.9% ± 12.0 to 27.0% ± 12.0, p = 0.001). The SMI decreased (35.7 cm

conclusionThe DECT FF appears to be suitable for detecting increasing myosteatosis. It seems to have predictive value as a new imaging biomarker for ICU patients. CLINICAL RELEVANCE STATEMENT: The dual-energy CT muscular fat fraction appears to be a robust imaging biomarker to detect and monitor myosteatosis. It has potential for prognosticating, risk stratifying, and thereby guiding therapeutic nutritional regimes and physiotherapy in critically ill patients. KEY POINTS: The dual-energy CT muscular fat fraction detects increasing myosteatosis caused by immobilization. Change in dual-energy CT muscular fat fraction was a predictor of  in-hospital morbidity and mortality. Dual-energy CT muscular fat fraction had a predictive value superior to established CT body composition parameters.

Indexed as

Body CompositionCritical IllnessTomography, X-Ray ComputedAdipose TissueAgedBiomarkersFemaleHumansIntensive Care UnitsMaleMiddle AgedMuscle, SkeletalPredictive Value of TestsRetrospective StudiesBiomarkersBody compositionComputed tomographyCritical illnessMyosteatosisSpectral-CT

Identifiers

PMID38777903
PMCPMC11519288

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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.