The importance of high total body water/fat free mass ratio and serial changes in body composition for predicting hospital mortality in patients with severe pneumonia: a prospective cohort study.

BMC Pulm Med

Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Niao Sung District, No. 123 Ta Pei Road, Kaohsiung, 83301, Taiwan.

Published: September 2024

Purpose: This study aimed to investigate the impact of body composition variables on hospital mortality compared to other predictive factors among patients with severe pneumonia. Additionally, we aimed to monitor the dynamic changes in body composition variables over the course on days 1, 3, and 8 after intensive care unit (ICU) admission for each patient.

Methods: We conducted a prospective study, enrolling patients with severe pneumonia admitted to the medical intensive care unit at Kaohsiung Chang Gung Memorial Hospital from February 2020 to April 2022. We collected clinical data from all patients and assessed their body composition at 1, 3, and 8 days post-ICU admission. On day 1, we analyzed clinical and body composition variables to predict in-hospital mortality.

Results: Multivariate analysis identified the Modified Nutrition Risk in the Critically Ill (mNUTRIC) score and the ratio of total body water to fat-free mass (TBW/FFM) as independent factors associated with in-hospital mortality in severe pneumonia patients. Receiver operating characteristic analysis determined that the TBW/FFM ratio was the most reliable predictive parameter of in-hospital mortality, with a cutoff value of 0.74. General linear regression with repeated measures analysis showed that hospital non-survivors displayed notable fluctuations in body water, fat, and muscle variables over the course of days 1, 3, and 8 after ICU admission.

Conclusions: The mNUTRIC score and TBW/FFM ratio emerged as independent factors for predicting hospital mortality, with the TBW/FFM ratio demonstrating the highest reliability as a predictive parameter.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11437920PMC
http://dx.doi.org/10.1186/s12890-024-03302-4DOI Listing

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