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[JAMA Surg发表论文]:CT自动分析人体成分作为腹部手术衰弱的生物标志物
2024年06月08日 时讯速递, 进展交流 [JAMA Surg发表论文]:CT自动分析人体成分作为腹部手术衰弱的生物标志物已关闭评论

Original Investigation 

April 10, 2024

Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery

Ijeamaka Anyene Fumagalli, Sidney T. Le, Peter D. Peng, et al

JAMA Surg. Published online April 10, 2024. doi:10.1001/jamasurg.2024.0628

Key Points

Question  Can preoperative computed tomography (CT) scans provide patient frailty assessments that are associated with outcomes of abdominal surgery?

Findings  In this cohort study of 48 444 patients, scores reflecting the quantity and quality of skeletal muscle were strongly correlated with patient frailty and associated with 30-day readmission and postoperative mortality. Body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes.

Meaning  The findings suggest that assessment of muscle quantity and quality via CT can provide an objective measure of patient frailty that may identify patients at high risk of mortality or readmission.

Abstract

Importance  Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients.

Objective  To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery.

Design, Setting, and Participants  This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. 

Exposure  Body composition derived from automated analysis of multislice abdominal CT scans.

Main Outcomes and Measures  The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program.

Results  The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = −0.42; 95% CI, −0.43 to −0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS.

Conclusions and Relevance  In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

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