{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:45:40Z","timestamp":1790621140333,"version":"4.1.0"},"reference-count":44,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Abdominal CT\u2010based assessments of skeletal muscle may provide important prognostic information for all\u2010cause mortality in aging adults. We aimed to evaluate whether AI\u2010segmented muscle area and muscle density predict long\u2010term survival in a large, retrospective adult population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study included 151\u2009141 adult patients who underwent an abdominal CT examination for any indication between 2000 and 2021. A validated automated AI\u2010based algorithm measured L3\u2010level muscle area (cm\n                      <jats:sup>2<\/jats:sup>\n                      ) and muscle density in Hounsfield units (HU). Demographic and clinical data (age, sex, BMI and date of death) were extracted from electronic health records. Survival analyses included Kaplan\u2013Meier curves and multivariable hazard ratio (HR) models to determine the relationship between these muscle metrics and mortality.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the 138\u2009535 adults (66\u2009468 men and 72\u2009067 women) included, 28\u2009489 died over the 20\u2010year period post\u2010CT, yielding an overall 20\u2010year survival rate of 0.620 (95% CI: 0.611\u20130.629). Of these, 9343 deaths occurred within the first year [survival rate: 0.933 (95% CI: 0.932\u20130.934)]. Mean and median follow\u2010up time was 6.4 and 4.9\u2009years, respectively. Lower muscle density significantly predicted higher mortality when each patient's measurement was expressed by its percentile within the age and sex\u2010matched 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automated CT\u2010based measurements of muscle density are superior to muscle area in predicting all\u2010cause mortality in a large, heterogeneous adult population. Incorporating AI\u2010driven muscle density assessments into routine clinical practice could substantially improve patient risk stratification and management, of particular relevance for aging and sarcopenic patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.70268","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T10:28:42Z","timestamp":1775125722000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automated CT\u2010Based Muscle Density Predicts Mortality Regardless of Muscle Area"],"prefix":"10.1002","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3418-0965","authenticated-orcid":false,"given":"Adam\u00a0J.","family":"Kuchnia","sequence":"first","affiliation":[{"name":"Department of Nutritional Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glen\u00a0M.","family":"Blake","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London St Thomas' Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew\u00a0H.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Wisconsin School of Medicine and Public Health  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9647-2579","authenticated-orcid":false,"given":"Jevin","family":"Lortie","sequence":"additional","affiliation":[{"name":"Department of Nutritional Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Fenske","sequence":"additional","affiliation":[{"name":"Department of Nutritional Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John\u00a0W.","family":"Garrett","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Wisconsin School of Medicine and Public Health  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Perry\u00a0J.","family":"Pickhardt","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Wisconsin School of Medicine and Public Health  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/2049-3258-72-45"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.24.32216"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2012.00260"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"G. 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