Literature DB >> 27697303

Existing equations to estimate lean body mass are not accurate in the critically ill: Results of a multicenter observational study.

Lesley L Moisey1, Marina Mourtzakis2, Rosemary A Kozar3, Charlene Compher4, Daren K Heyland5.   

Abstract

BACKGROUND & AIMS: Lean body mass (LBM), quantified using computed tomography (CT), is a significant predictor of clinical outcomes in the critically ill. While CT analysis is precise and accurate in measuring body composition, it may not be practical or readily accessible to all patients in the intensive care unit (ICU). Here, we assessed the agreement between LBM measured by CT and four previously developed equations that predict LBM using variables (i.e. age, sex, weight, height) commonly recorded in the ICU.
METHODS: LBM was calculated in 327 critically ill adults using CT scans, taken at ICU admission, and 4 predictive equations (E1-4) that were derived from non-critically adults since there are no ICU-specific equations. Agreement was assessed using paired t-tests, Pearson's correlation coefficients and Bland-Altman plots.
RESULTS: Median LBM calculated by CT was 45 kg (IQR 37-53 kg) and was significantly different (p < 0.001) from E1 (52.5 kg; IQR: 42-61 kg), E2 (55 kg; IQR 45-64 kg), E3 (55 kg; IQR 44-64 kg), and E4 (54 kg; IQR 49-61 kg). Pearson correlation coefficients suggested moderate correlation (r = 0.739, 0.756, 0.732, and 0.680, p < 0.001, respectively). Each of the equations overestimated LBM (error ranged from 7.5 to 9.9 kg), compared with LBM calculated by CT, suggesting insufficient agreement.
CONCLUSIONS: Our data indicates a large bias is present between the calculation of LBM by CT imaging and the predictive equations that have been compared here. This underscores the need for future research toward the development of ICU-specific equations that reliably estimate LBM in a practical and cost-effective manner.
Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Entities:  

Keywords:  Critical illness; Lean body mass; Predictive equation; Skeletal muscle

Mesh:

Year:  2016        PMID: 27697303     DOI: 10.1016/j.clnu.2016.09.013

Source DB:  PubMed          Journal:  Clin Nutr        ISSN: 0261-5614            Impact factor:   7.324


  5 in total

1.  Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Authors:  Michael T Paris; Puneeta Tandon; Daren K Heyland; Helena Furberg; Tahira Premji; Gavin Low; Marina Mourtzakis
Journal:  Clin Nutr       Date:  2020-01-22       Impact factor: 7.324

2.  Phase I study of AR-42 and decitabine in acute myeloid leukemia.

Authors:  Sophia G Liva; Christopher C Coss; Jiang Wang; William Blum; Rebecca Klisovic; Bhavana Bhatnagar; Katherine Walsh; Susan Geyer; Qiuhong Zhao; Ramiro Garzon; Guido Marcucci; Mitch A Phelps; Alison R Walker
Journal:  Leuk Lymphoma       Date:  2020-02-08

Review 3.  Measuring and monitoring lean body mass in critical illness.

Authors:  Wilhelmus G P M Looijaard; Jeroen Molinger; Peter J M Weijs
Journal:  Curr Opin Crit Care       Date:  2018-08       Impact factor: 3.687

4.  Population Pharmacokinetic Analysis from First-in-Human Data for HDAC Inhibitor, REC-2282 (AR-42), in Patients with Solid Tumors and Hematologic Malignancies: A Case Study for Evaluating Flat vs. Body Size Normalized Dosing.

Authors:  Sophia Liva; Min Chen; Amir Mortazavi; Alison Walker; Jiang Wang; Kristin Dittmar; Craig Hofmeister; Christopher C Coss; Mitch A Phelps
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2021-10-07       Impact factor: 2.441

Review 5.  Drug dosing in the critically ill obese patient-a focus on sedation, analgesia, and delirium.

Authors:  Brian L Erstad; Jeffrey F Barletta
Journal:  Crit Care       Date:  2020-06-08       Impact factor: 9.097

  5 in total

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