Evidence map›Paper›PMID 41925083›Full record

ArticleJournal of cachexia, sarcopenia and muscle2026

Automated CT-Based Muscle Density Predicts Mortality Regardless of Muscle Area.

Adam J Kuchnia, Glen M Blake, Matthew H Lee, Jevin Lortie, Rachel Fenske, John W Garrett, Perry J Pickhardt

Abstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2026. 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
–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

1 citing paper in PubMed.

  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

7 authors.

Adam J KuchniaDepartment of Nutritional Sciences, University of Wisconsin, Madison, Wisconsin, USA.ORCID https://orcid.org/0000-0002-3418-0965
Glen M BlakeSchool of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
Matthew H LeeDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Jevin LortieDepartment of Nutritional Sciences, University of Wisconsin, Madison, Wisconsin, USA.ORCID https://orcid.org/0000-0001-9647-2579
Rachel FenskeDepartment of Nutritional Sciences, University of Wisconsin, Madison, Wisconsin, USA.
John W GarrettDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Perry J PickhardtDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAbdominal CT-based assessments of skeletal muscle may provide important prognostic information for all-cause mortality in aging adults. We aimed to evaluate whether AI-segmented muscle area and muscle density predict long-term survival in a large, retrospective adult population.

methodsThis retrospective study included 151 141 adult patients who underwent an abdominal CT examination for any indication between 2000 and 2021. A validated automated AI-based algorithm measured L3-level muscle area (cm

resultsAmong the 138 535 adults (66 468 men and 72 067 women) included, 28 489 died over the 20-year period post-CT, yielding an overall 20-year survival rate of 0.620 (95% CI: 0.611-0.629). Of these, 9343 deaths occurred within the first year [survival rate: 0.933 (95% CI: 0.932-0.934)]. Mean and median follow-up time was 6.4 and 4.9 years, respectively. Lower muscle density significantly predicted higher mortality when each patient's measurement was expressed by its percentile within the age and sex-matched population (HR up to 3.5 in women and 4.0 in men), with decreasing mortality throughout the higher percentiles. Lower muscle area showed a more modest effect on mortality in both sexes when expressed in percentiles. Individuals with high muscle density demonstrated the most favourable survival and those with low density demonstrated the worst survival. Low muscle density significantly predicted mortality across all age groups and both sexes. Conversely, muscle area predicted mortality in all age groups in men, albeit to a lesser degree and did not predict mortality in any age group among women.

conclusionsAutomated CT-based measurements of muscle density are superior to muscle area in predicting all-cause mortality in a large, heterogeneous adult population. Incorporating AI-driven muscle density assessments into routine clinical practice could substantially improve patient risk stratification and management, of particular relevance for aging and sarcopenic patients.

Indexed as

Muscle, SkeletalTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesSarcopeniabody compositioncomputed tomography (CT)mortality predictionmuscle densitymuscle qualitymyosteatosisopportunistic imaging

Identifiers

PMID41925083
PMCPMC13045369

What Socratic holds

Textmetadata
LicenceCC BY
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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.