4.2 Article

Validation of Bedside Ultrasound of Muscle Layer Thickness of the Quadriceps in the Critically Ill Patient (VALIDUM Study): A Prospective Multicenter Study

Journal

JOURNAL OF PARENTERAL AND ENTERAL NUTRITION
Volume 41, Issue 2, Pages 171-180

Publisher

WILEY
DOI: 10.1177/0148607116637852

Keywords

critical illness; intensive care unit; ultrasound; muscle thickness; muscle atrophy; computed tomography

Funding

  1. Government of Ontario Ministry of Research and Innovation Early Researcher Award

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Background: In critically ill patients, muscle atrophy is associated with long-term disability and mortality. Bedside ultrasound may quantify muscle mass, but it has not been validated in the intensive care unit (ICU). Here, we compared ultrasound-based quadriceps muscle layer thickness (QMLT) with precise quantifications of computed tomography (CT)-based muscle cross-sectional area (CSA). Methods: Patients 18 years old with abdominal CT scans performed for clinical reasons were recruited from 9 ICUs for an ultrasound assessment of the quadriceps. CT scans of the third lumbar vertebra, performed <24 hours before or <72 hours after ICU admission, were analyzed for CSA. Low muscularity was defined as 170 cm(2) for men and 110 cm(2) for women. The ultrasound probe was maximally compressed against the skin and QMLT was measured on 2 sites of each quadriceps <72 hours of the CT scan. Results: Mean CT-derived muscle CSA was 109 +/- 25 cm(2) for women and 168 +/- 37 cm(2) for men, where 58% of patients exhibited low muscularity; only 2.7% patients were underweight according to body mass index. QMLT was positively correlated with CT CSA (r = 0.45, P < .001). Based on logistic regression to predict low muscularity, QMLT independently generated a concordance index (c) of 0.67 (P < .002), which increased to 0.77 (P < .001) when age, sex, body mass index, Charlson Comorbidity Index, and admission type (surgical vs medical) were added. Conclusions: Our results suggest that QMLT alone with our current protocol may not accurately identify patients with low muscle mass.

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