A, Measurement of skeletal muscle cross-sectional area in the axial plane at L3, which includes the rectus abdominis, internal and external obliques, transversus abdominis, quadratus lumborum, psoas major and minor, and erector spinae. B, Cross-sectional area of right and left erector spinae muscles (iliocostalis, longissimus, and spinalis). Measured cross-sectional areas for the corresponding panels are shaded in red.
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Kaplan SJ, Zhao KL, Koren M, Bentov I, Reed MJ, Pham TN. Thresholds and Mortality Associations of Paraspinous Muscle Sarcopenia in Older Trauma Patients. JAMA Surg. 2020;155(7):662–664. doi:10.1001/jamasurg.2020.0435
Sarcopenia is a good surrogate for physical frailty in older adults with injuries.1 The paraspinous muscle group in the mid-back is readily measurable at the lower thoracic vertebral level on abdominal and chest computed tomography (CT) scans, which are routinely obtained during trauma evaluation. Recent advances in automated CT image analysis creates an opportunity for paraspinous sarcopenia measurement as part of the trauma workup.2
Paraspinous measurement is straightforward and does not require specialized software, but to our knowledge, thresholds for determining sarcopenia have not been established. We hypothesize that paraspinous sarcopenia at the T12 level (1) correlates with previously established thresholds3 at the lumbar level allowing for T12 threshold definition and (2) is associated with poor long-term outcomes in older trauma patients.
This retrospective cohort study included trauma patients 65 years and older at a single level 1 trauma center from January 2011 to December 2014. Data analysis began in May 2019. Chest and/or abdominal CT imaging obtained as part of the clinical trauma workup was used to determine cross-sectional area of paraspinous and abdominal muscles at the T12 and L3 levels (Figure). Skeletal muscle index (SMI) was calculated as cross-sectional area divided by height squared (cm2/m2). Image analysis protocol development and validation was similar to that of our prior works.4,5
Established L3 SMI thresholds3 were correlated with paraspinous T12 SMI to identify optimal sex-specific sarcopenia thresholds. Sensitivity analysis was conducted and the performance among models was compared using area under the curve (AUC) and Brier score. Using the T12 thresholds identified through this procedure, sarcopenic and nonsarcopenic groups were compared via multivariable Cox regression adjusted for age, sex, updated Charlson Comorbidity Index, body mass index (calculated as weight in kilograms divided by height in meters squared), and in-hospital complications. Analyses were conducted using Stata/SE, version 14.2 (StataCorp), with an a priori α of .05. Patient inclusion/exclusion, data collection/sources, variable selection/definitions, and limitations are the same as described in similarly focused projects.4,5 Institutional review board approval was provided by the University of Washington with a waiver of consent partially because of the low risk, retrospective nature of the study.
Of the 450 patients who met inclusion criteria, 316 (70.2%) had comparable L3 imaging available for T12 threshold development. The median age for the entire cohort was 75 years (interquartile range, 69-83 years); 192 patients (42.7%) were female.
The correlation between T12 SMI and L3 SMI was strong (ρ = 0.63; P < .001). There was no difference between the T12- and L3-based models: Brier score of 0.055 vs 0.047 and AUC of 0.86 (95% CI, 0.79–0.93) vs 0.85 (95% CI, 0.77–0.92) (P = .79). The T12 SMI thresholds with the best model performance were 9.01 cm2/m2 for women and 10.66 cm2/m2 for men. Patients were dichotomized into sarcopenic and nonsarcopenic groups using these T12 thresholds, which did not diminish model accuracy compared with a continuous variable L3 normalized model (AUC, 0.86 [95% CI, 0.79–0.93] vs 0.85 [95% CI, 0.77–0.92], respectively; χ2 = 0.07; df = 1; P = .79)
Data from all 450 patients were used in the outcome analyses. Short-term outcomes did not differ between sarcopenic and nonsarcopenic groups (Table). One-year mortality was significantly higher in patients with sarcopenia (34 [16%] vs 7 [3%]; P < .001; hazard ratio, 3.3; 95% CI, 1.3–8.5; P = .01).
Objective and rapid assessment of frailty remains an important challenge for trauma clinicians. Clinical tools require the patient or available surrogates to answer history questions; grip strength and walking ability are often not feasible instruments in early trauma care.6 Conversely, many trauma patients undergo CT scanning as part of their initial evaluation. Thus, imaging provides an objective and reproducible assessment of sarcopenia.4,5 Computed tomography scans are safe, widely available, reasonable in cost, and provide expeditious results for detecting injuries and chronic conditions, such as physical frailty.
This study demonstrates that paraspinous sarcopenia at the T12 level is independently associated with 1-year mortality, with similar correlative statistics compared with more cumbersome methods established for the lumbar area.4 This is an important conclusion because (1) this method of measurement is technically less challenging and can be automated; (2) it can be derived from either chest or abdominal CT; and (3) it does not require the use of specialized software. We propose that paraspinous sarcopenia at the T12 level is a good, widely accessible, surrogate measurement of frailty that may help guide long-term prognostication in geriatric trauma patients following prospective confirmation of utility.
Corresponding Author: Stephen J. Kaplan, MD, MPH, Virginia Mason Medical Center, 1100 Ninth Ave, C6-GS, Seattle, WA 98101 (firstname.lastname@example.org).
Published Online: April 29, 2020. doi:10.1001/jamasurg.2020.0435
Author Contributions: Drs Kaplan and Pham had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Kaplan, Bentov, Reed, Pham.
Acquisition, analysis, or interpretation of data: Kaplan, Zhao, Koren.
Drafting of the manuscript: Kaplan, Zhao, Pham.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Kaplan.
Obtained funding: Kaplan, Reed.
Administrative, technical, or material support: Kaplan, Reed.
Supervision: Kaplan, Reed, Pham.
Conflict of Interest Disclosures: None reported.
Funding/Support: This work was supported in part by the National Institutes of Health National Institute on Aging Medical Student Training in Aging Research Program (T35AG026736) and the Patterson Surgery Research Endowment at Benaroya Research Institute/Virginia Mason.
Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: We thank the following individuals from the University of Washington for their uncompensated assistance: Mamatha Damodarasamy, MS, Kevin Penn, MD, and Sarah C. Katsandres, BS, for data collection and general project support and Joel A. Gross, MD, for development and supervision of the image analysis protocol.
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