Evidence mapPaperPMID 32504466Full record

Trial reportThe journals of gerontology. Series A, Biological sciences and medical sciences2021

Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial.

Leon Lenchik, Ryan Barnard, Robert D Boutin, Stephen B Kritchevsky, Haiying Chen, Josh Tan, Peggy M Cawthon, Ashley A Weaver, Fang-Chi Hsu

Open access · bronzeAbstract readClinical TrialMulticenter Study
In one paragraph

Trial report in The journals of gerontology. Series A, Biological sciences and medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 5% 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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Pooled it
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  7. Opportunistic Screening on Chest CT, From theAJR. American journal of roentgenology · 2026
    Review
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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

9 authors at 3 institutions in 1 country.

Leon LenchikDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Ryan BarnardDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Robert D BoutinDepartment of Radiology, Stanford University Medical Center, California.
Stephen B KritchevskyDepartment of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Haiying ChenDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Josh TanDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Peggy M CawthonCalifornia Pacific Medical Center Research Institute, San Francisco.
Ashley A WeaverDepartment of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Fang-Chi HsuDepartment of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Wake Forest University · USCalifornia Pacific Medical Center · USStanford Medicine · US

Funding

Wake Forest Claude D. Pepper OAIC - RenewalP30AG021332 · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2002 to 2025
$7.7M
Effect of protein supplementation during weight loss on older adult bone healthK25AG058804 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Ashley Weaver · 2022 to 2023
$267k
NCATS NIH HHS UL1 TR001420NIA NIH HHS K25 AG058804NIA NIH HHS P30 AG021332
6 · The paper itself

Abstract

backgroundMuscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults.

methodThe relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment.

resultsAfter a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance.

conclusionHigher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.

Indexed as

AgedAgingCohort StudiesFemaleHumansLungMachine LearningMaleMiddle AgedMuscle, SkeletalProportional Hazards ModelsRetrospective StudiesTomography, X-Ray ComputedComputed tomographyMachine learningMortalityMyosteatosisSarcopenia

Identifiers

PMID32504466
PMCPMC7812435
OpenAlexW3033703213

What Socratic holds

Textmetadata
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.