High Prevalence of Sarcopenia in Older Trauma Patients: A Pilot Study

Pectoralis Muscle Index as Predictor of Outcomes in Patients With Severe Blunt Chest Wall Injury

INTRODUCTION

Sarcopenia has been shown to portend worse outcomes in injured patients; however, little is known about the impact of thoracic muscle wasting on outcomes of patients with chest wall injury. We hypothesized that reduced pectoralis muscle mass is associated with poor outcomes in patients with severe blunt chest wall injury.

METHODS

All patients admitted to the intensive care unit between 2014 and 2019 with blunt chest wall injury requiring mechanical ventilation were retrospectively identified. Blunt chest wall injury was defined as the presence of one or more rib fractures as a result of blunt injury mechanism. Exclusion criteria included lack of admission computed tomography imaging, penetrating trauma, <18 y of age, and primary neurologic injury. Thoracic musculature was assessed by measuring pectoralis muscle cross-sectional area (cm2) that was obtained at the fourth thoracic vertebral level using Slice-O-Matic software. The area was then divided by the patient height in meters2 to calculate pectoralis muscle index (PMI) (cm2/m2). Patients were divided into two groups, 1) the lowest gender-specific quartile of PMI and 2) second-fourth gender-specific PMI quartiles for comparative analysis.

RESULTS

One hundred fifty-three patients met the inclusion criteria with a median (interquartile range) age 48 y (34-60), body mass index of 30.1 kg/m2 (24.9-34.6), and rib score of 3.0 (2.0-4.0). Seventy-five percent of patients (116/153) were male. Fourteen patients (8%) had prior history of chronic lung disease. Median (IQR) intensive care unit length-of-stay and duration of mechanical ventilation (MV) was 18.0 d (13.0-25.0) and 15.0 d (10.0-21.0), respectively. Seventy-three patients (48%) underwent tracheostomy and nine patients (6%) expired during hospitalization. On multivariate linear regression, reduced pectoralis muscle mass was associated with increased MV duration when adjusting for rib score and injury severity score (β 5.98, 95% confidence interval 1.28-10.68, P = 0.013).

CONCLUSIONS

Reduced pectoralis muscle mass is associated with increased duration of MV in patients with severe blunt chest wall injury. Knowledge of this can help guide future research and risk stratification of critically ill chest wall injury patients.

The Impact of Body Mass Composition on Outcome in Multiple Traumatized Patients—Results from the Fourth Thoracic and Third Lumbar Vertebrae: A Single-Center Retrospective Observational Study

Background: Body mass composition (BC) was shown to correlate with outcome in patients after surgery and minor trauma. As BC is assessed using computed tomography (CT) and routinely applied in multiple trauma (MT), this study will help to analyze whether BC variables also correlate with outcome in trauma patients. Materials and Methods: Inclusion criteria were MT (Injury Severity Score (ISS) > 15) and whole-body CT (WBCT) scan on admission. Muscle and fat tissue were assessed at the level of the fourth thoracic vertebra (T4) and the third lumbar vertebra (L3) using Slice-O-matic software, version 5.0 (Tomovision, Montreal, QC, Canada). Univariate and multivariate regression models were used with regard to outcome parameters such as duration of ventilation, hospital stay, local (i.e., pneumonia, wound infection) and systemic (i.e., MODS, SIRS) complications, and mortality. Results: 297 patients were included. BC correlated with both the development and severity of complications. Skeletal muscle index (SMI) and subcutaneous adipose tissue index (SATI) at both T4 and L3 correlated positively with the occurrence of systemic infections. Local infections positively correlated with SMI at T4. Low muscle mass and high visceral adipose tissue (VAT) predicted the severity of systemic and local complications. Muscle tissue markers at both T4 and L3 predicted the severity of complications in roughly the same way. Moreover, higher muscle mass at the L3 level was significantly associated with higher overall survival, while SATI at the T4 level correlated positively with hospital stay, length of stay in the ICU, and duration of ventilation. Conclusions: A lower muscle mass and a high adipose tissue index are associated with a poor outcome in MT. For the first time, it was shown that BC at the fourth thoracic vertebra is associated with comparable results to those found at the third lumbar level.

Predicting mortality in elderly trauma patients: a review of the current literature

PURPOSE OF REVIEW

Advances in medical care allow patients to live longer, translating into a larger geriatric patient population. Adverse outcomes increase with older age, regardless of injury severity. Age, comorbidities, and physiologic deterioration have been associated with the increased mortality seen in geriatric trauma patients. As such, outcome prediction models are critical to guide clinical decision making and goals of care discussions for this population. The purpose of this review was to evaluate the various outcome prediction models for geriatric trauma patients.

RECENT FINDINGS

There are several prediction models used for predicting mortality in elderly trauma patients. The Geriatric Trauma Outcome Score (GTOS) is a validated and accurate predictor of mortality in geriatric trauma patients and performs equally if not better to traditional scores such as the Trauma and Injury Severity Score. However, studies recommend medical comorbidities be included in outcome prediction models for geriatric patients to further improve performance.

SUMMARY

The ideal outcome prediction model for geriatric trauma patients has not been identified. The GTOS demonstrates accurate predictive ability in elderly trauma patients. The addition of medical comorbidities as a variable in outcome prediction tools may result in superior performance; however, additional research is warranted.

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

New Diagnostic and Therapeutic Approaches for the Care of the Severely Injured Patient

Severe trauma remains a leading cause of death, especially in the younger population [...].