Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Apr 4.
Published in final edited form as: JPEN J Parenter Enteral Nutr. 2024 Feb 23;48(3):300–307. doi: 10.1002/jpen.2610

Malnutrition and pectoralis muscle index in medical intensive care unit patients: A matched cohort study

Luke O Smith 1, Michael T Vest 2, Alisha J Rovner 1, Richard J Caplan 3, Jillian C Trabulsi 1, Juhie B Patel 4, Sarah W Meng 5, Mary Shapero 6, Carrie P Earthman 1
PMCID: PMC10990767  NIHMSID: NIHMS1970680  PMID: 38400547

Abstract

Background:

Muscle assessment is an important component of nutrition assessment. The Global Leadership Initiative on Malnutrition (GLIM) consortium recently underscored the need for more objective muscle assessment methods in clinical settings. Various assessment techniques are available; however, many have limitations in clinical populations. Computed tomography (CT) scans, obtained for diagnostic reasons, could serve multiple purposes, including muscle measurement for nutrition assessment. Although CT scans of the chest are commonly performed clinically, there is little research surrounding the utility of pectoralis muscle measurements in nutrition assessment. The primary aim was to determine whether CT-derived measures of pectoralis major cross-sectional area (PMA) and quality (defined as mean pectoralis major Hounsfield units [PMHU]) could be used to identify malnutrition in patients who are mechanically ventilated in an intensive care unit (ICU). A secondary aim was to evaluate the relationship between these measures and clinical outcomes in this population.

Methods:

A retrospective analysis was conducted on 33 pairs of age- and sex-matched adult patients who are being mechanically ventilated in the ICU. Patients were grouped by nutrition status. Analyses were performed to determine differences in PMA and mean PMHU between groups. Associations between muscle and clinical outcomes were also investigated.

Results:

Compared with nonmalnourished controls, malnourished patients had a significantly lower PMA (P = 0.001) and pectoralis major (PM) index (PMA/height in m2; P = 0.001). No associations were drawn between PM measures and clinical outcomes. Conclusion: These findings regarding CT PM measures lay the groundwork for actualizing the GLIM call to action to validate quantitative, objective muscle assessment methods in clinical settings.

Keywords: adult, body composition, critical care, life cycle, nutrition, nutrition assessment, research and diseases

BACKGROUND

Assessment of muscularity is an important component of nutrition assessment, and it is one of the defining criteria for the diagnosis of malnutrition in all of the major diagnostic frameworks used globally,1 including the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition (AND/ASPEN) malnutrition consensus criteria (MCC)2 and, more recently, the Global Leadership Initiative on Malnutrition (GLIM) criteria.3,4 The GLIM consortium has recently underscored the need for more objective, quantitative muscle assessment methods in clinical settings.4,5

There are a variety of bedside techniques that could be used in clinical settings, including anthropometrics, bioimpedance methods, and ultrasound.1 However, these methods have limitations, including device costs, interoperator and intraoperator variability, and physiological assumptions of hydration status.6 There is growing interest in the use of computed tomography (CT) scans, initially obtained for unrelated clinical diagnostic reasons, for nutrition assessment in clinical populations. The use of abdominal CT at the level of the third lumbar spinous process to diagnose low muscularity was pioneered in cancer populations,7–9 and reference cut points for muscle size based on the cross-sectional area of the abdominal muscles have been determined to define low muscularity in these populations.5 The use of CT scans taken at the third lumbar vertebra to assess muscularity was recently expanded to include liver failure10 and critical illness,11 and low-muscularity reference cut points for the abdominal cross-sectional area have also been defined for these patient populations. Although far less well studied, muscle attenuation, measured by mean Hounsfield units, may also be useful for assessing muscularity; it has been suggested that attenuation of the muscle is an indicator of muscle density and quality.12,13 Mean Hounsfield units of abdominal muscle from CT scans taken at the third lumbar vertebra have been shown to be an important predictor of mortality and clinical outcomes in patients with cancer.14,15

Although many published studies in this area have used CT scans taken at the third lumbar vertebra because of their wide availability, abdominal CT scans are not a routine part of medical monitoring and treatment in all patients in the ICU. Chest CT scans are also commonly acquired in the ICU, when patients require imaging of the heart and lungs, patients with cancers located in the chest region and other populations with conditions affecting the heart and lungs (eg, heart failure).16 In fact, CT scans that include the pectoralis muscles were reported to be among the most common scan site in ICU patients.17 The pectoralis muscle cross-sectional area measured by CT scan has been associated with mortality in patients with lung disease, cancer, coronavirus disease 2019 (COVID-19) infection, pneumonia, heart failure, and critical illness.18–21 Combining insights from prior studies using lumbar CT scan muscle mass for nutrition assessment and studies documenting the association of pectoralis muscle mass with outcomes, we hypothesize that the pectoralis muscles of the chest are potential alternative regions for assessing muscularity by CT in patients in the ICU. Although scans at this region of interest are commonly performed in clinical settings, there is little research surrounding the utility of pectoralis muscle measurements in nutrition assessment, and, as such, reference cut points for low muscularity using CT-derived measures of the pectoralis muscles have not yet been established. It is unclear whether pectoralis muscle measures can be used to reflect nutrition status, and this has been identified as an area in need of further study.13

The primary aim of this research study was to determine whether CT-derived measures of pectoralis major (PM) muscle size and quality could be useful in the identification of malnutrition in mechanically ventilated ICU patients. We hypothesized that registered dietitian–identified malnutrition in mechanically ventilated ICU patients is associated with decreased pectoralis muscle index. A secondary aim was to evaluate the relationship between PM size and quality and clinical outcomes in this population.

METHODS

Cohort

This was a retrospective matched cohort analysis of patients admitted to the ChristianaCare hospital system ICU (Wilmington, DE, USA) from January 1, 2019, to December 31, 2020. All mechanically ventilated patients in the ChristianaCare hospital system are evaluated by a registered dietitian, and this evaluation includes identifying malnutrition, if present. Our exposure of interest was registered dietitian–identified malnutrition, defined by a modified version of the AND/ASPEN MCC,2 which assesses recent dietary intake, body weight history, subcutaneous fat, muscle mass, and fluid accumulation and replaces handgrip strength with body mass index (BMI), as described in a previous study.22 Adult (>18 years of age) patients who were mechanically ventilated were eligible for inclusion. To create the cohort, we first identified all patients who were mechanically ventilated in the ICU admitted during the study time frame who had a CT scan of chest performed within 24 h of admission. We then excluded patients admitted to the surgical, neurologic, and cardiac ICUs to focus on medical patients. We conducted a chart review to determine which of the remaining patients were identified by a registered dietitian as malnourished. Patients who were malnourished were matched 1:1 with patients who were nonmalnourished based on biologic sex and exact age in years. PM size (cross-sectional area, cm2) and quality (mean pectoralis major Hounsfield units [PMHU]) were assessed by CT scans, taken for clinical purposes, of 35 patients who were mechanically ventilated and identified with malnutrition by registered dietitians and 35 age- and biological sex–matched patients who were mechanically ventilated without a malnutrition diagnosis during the same time period. Patients were excluded if they were diagnosed with COVID-19 during their admission because we could not be certain these patients were clinically evaluated for malnutrition. We also excluded patients who did not have a CT scan of the PM during their admission or were not mechanically ventilated during their admission.

Patient data were collected via chart review by a physician at the ChristianaCare hospital system (Juhie B. Patel). To ensure the accuracy of the data collected, a trained researcher (Luke O. Smith) abstracted data in duplicate on a subset of randomly selected patients to determine interobserver reliability. Any discrepancies between the two observers were reviewed by a third member of the research team (Michael T. Vest) for clarification.

Sample size calculation

The primary outcome was the pectoralis muscle index, defined as the PM muscle cross-sectional area divided by the patient’s height in meters squared. Secondary outcomes included pectoralis major cross-sectional area (PMA) and mean pectoralis major hounsfield units (PMHU), discharge disposition, in-hospital mortality, and length of stay. The primary outcome measure, PM index, and the secondary outcome measure, mean PMHU, were used for the calculation of the required sample size.16 A separate power calculation was performed for each of these measures. Based on a PM index SD of 0.9 cm2/m2, a sample size of 25 patients per group had 90% power to detect a difference of 1 PM index between those with and without malnutrition. Based on a mean PMHU SD of 6, a sample size of 25 patients per group had 80% power to detect a difference of 1 Hounsfield unit between those with and without malnutrition.

CT scan analysis

CT scans were analyzed using a picture archiving and communication system workstation (Phillips IntelliSpace 4.7, Koninklijke Philips, NV) by an attending radiologist (Sarah W. Meng) who was blinded to malnutrition diagnosis and patient outcomes. The muscle of interest was the patient’s right-side PM, which was manually shaded using a previously defined attenuation range for muscle of −29 to 150 Hounsfield units.23,24 All measurements were taken at the level of the main pulmonary artery. The primary measures that were derived from these scans were pectoralis major cross-sectional area (PMA; cm2), pectoralis muscle quality (measured in mean Hounsfield units), and PM index. The PM index was calculated to normalize muscle mass by height (PMA [cm2]/height [m2]). The primary outcome of the study was the difference in mean PM index between patients with and patients without registered dietitian–identified malnutrition. Our secondary outcomes were the differences in PMA, and mean PMHU, between patients with and without malnutrition, as well as the associations between the three muscle measures and the clinical outcomes (discharge disposition, in-hospital mortality, and length of stay).

Statistical analysis

Variables were tested for normality, and nonparametric tests were used for those that were not normally distributed (weight, BMI, PMA, mean PMHU, and PM index). Differences in demographic and medical characteristics, body composition, and clinical outcomes by nutrition status group (malnutrition or no malnutrition) were determined by Fisher exact tests for categorical variables and Mann-Whitney U tests for continuous variables. Mann-Whitney U tests were performed to address the primary study objective, which was to assess differences in PMA and mean PMHU in patients with and without registered dietitian–identified malnutrition.

The secondary objectives were to determine the relationship between PM size and quality and clinical outcomes. The outcomes (dependent variables) of interest were discharge disposition (ie, death, discharge to home, discharge to skilled nursing facility or rehabilitation center, or left against medical advisory), in-hospital mortality, and the variable “length of stay,” which was log transformed before parametric statistical analyses. Length of stay was defined as the number of days between hospital admission and hospital discharge. For discharge disposition, multinomial logistic regression was used to examine the relationships between this outcome and predicting the PMA, mean PMHU, and PM index variables (each independently) while controlling for nutrition status. For in-hospital mortality specifically, which was also included as an outcome in the multinomial logistic regression analysis conducted on discharge disposition, bivariate logistic regression models were used to examine the relationships between this outcome and predicting the PMA, mean PMHU, and PM index variables (each independently) while controlling for nutrition status. Additionally, linear regression models were used to examine the relationships between length of stay and predicting the PMA, mean PMHU, and PM index variables (each independently) while controlling for nutrition status.

Statistical significance was set a priori, at α < 0.05. Statistical analyses were performed using STATA software (Stata Statistical Software: Release 17; StataCorp LLC).

This study was approved by the institutional review boards at both collaborating institutions: the Christiana Care hospital system and the University of Delaware.

RESULTS

Sample characteristics

A total of 35 pairs of patients who were mechanically ventilated in the ICU with chest CT scans met the inclusion criteria. Two of these pairs were excluded from analyses because of missing data, yielding a total sample of 66 patients (33 with malnutrition and 33 without malnutrition). The interobserver reliability for data collection was 95.3%. The sample was primarily White (68.2%) and male (59.7%), with a mean ± SD age of 67 ± 13.4 years old. Patients with malnutrition and those who were nonmalnourished were similar in age, sex, race, reason for admission, and comorbidities (Table 1).

TABLE 1.

Demographic and medical characteristics of patients with and without malnutrition.

Population characteristics Malnutritiona (n = 33) No malnutritiona (n = 33) χ2 P value*
Age, mean (median [IQR]), years 67 (66 [59–80]) 67 (66 [59–80]) 0 1.000
Female, n (%) 10 (30) 10 (30) 0 1.000
Race, n (%) 1.2 0.789
 White 23 (70) 22 (67)
 Black 9 (27) 11 (33)
 Asian 1 (3) 0 (0)
Reason for admission, n (%) 1.704 0.672
 Respiratory failure 9 (27) 13 (39)
 Sepsis 16 (49) 13 (39)
 Overdose 1 (3) 2 (7)
 Other 7 (21) 5 (15)
Comorbidities, n (%)
 CHF 6 (18) 11 (33) 1.981 0.260
 COPD 6 (18) 12 (36) 2.750 0.166
 DM 11 (33) 14 (42) 0.579 0.612
 Substance abuse 9 (27) 4 (14) 4.472 0.108
Open in a new tab

Abbreviations: BMI, body mass index; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; DM, diabetes mellitus; IQR, interquartile range.

a

Column percentages may add to over 100% because of patients having multiple comorbidities.

*

P values reported are for Fisher exact test and independent t test/Mann-Whitney U tests for categorical and continuous variables, respectively.

Between-group differences in body size and composition

For the primary outcome, patients identified as malnourished had a significantly lower PM index (2.74 cm2/m2) compared with that of those identified as nonmalnourished (4.51 cm2/m2, P < 0.001). Similarly, patients who were malnourished had a lower PMA (8.2 cm2) compared with that of patients without malnutrition (13.5 cm2; P < 0.001). Compared with that of patients who were not malnourished, those with malnutrition also had a significantly lower body weight (P < 0.001) and BMI (P < 0.001). No significant between-group differences were found for height or mean PMHU (Table 2).

TABLE 2.

Body composition in patients with and without malnutrition.

Population characteristics, body composition parameters Malnutrition (n = 33), mean (median [IQR]) No malnutrition (n = 33), mean (median [IQR]) z P value*
Height, cm 173.37 (177.8 [167–182.8]) 171.77 (172 [165–178]) −0.937 0.353
Weight, kg 62.47 (61.3 [52.2–68.2]) 96.57 (91.9 [76.8–112.3]) 5.438 <0.001*
BMI, kg/m2 20.76 (19.61 [17.06–22.18]) 32.88 (33.22 [25.21–37.92]) 5.752 <0.001*
PMA, cm2 8.20 (7.44 [5.15–9.93]) 13.54 (11.54 [7.94–15.92]) 3.200 0.001*
Mean PMHU 25.92 (31.39 [13.19–40.06]) 17.10 (21.78 [1.12–34.15]) −1.802 0.072
PMI, cm2/m2 2.74 (2.60 [1.58–3.38]) 4.51 (4.12 [2.74–5.58]) 3.277 0.001*
Open in a new tab

Abbreviations: BMI, body mass index; IQR, interquartile range; PMA, pectoralis major cross-sectional area; PMHU, pectoralis major Hounsfield unit; PMI, pectoralis major muscle index.

*

P values reported are for independent t test/Mann-Whitney U tests.

Patient outcomes

There were no differences between groups for length of stay (P = 0.248), discharge disposition (P = 0.062), or in-hospital mortality (P = 0.460) (Table 3).

TABLE 3.

Clinical outcomes in patients with and without malnutrition.

Population characteristics and patient outcomes Malnutrition (n = 33) No malnutrition (n = 33) χ2 P value*
Length of stay, mean (median [IQR]), days 26.42 (15 [7–28]) 15.97 (13 [7–24]) −1.155 0.248
Discharge disposition, n (%) 6.623 0.062
 Death 18 (55) 14 (42) 0.971 0.460
 Home 6 (18) 14 (42)
 Skilled nursing facilitya 9 (27) 4 (12)
 Left against medical advice 0 (0) 1 (3)
Open in a new tab

Abbreviation: IQR, interquartile range.

a

Skilled nursing facility includes nursing homes and medical rehabilitation centers.

*

P values reported are for the Fisher exact test and independent t test/Mann-Whitney U tests for categorical and continuous variables, respectively.

Predictive outcomes

PMA, mean PMHU, and PM index were not significant predictors of discharge disposition, in-hospital mortality, or length of stay.

DISCUSSION

The pectoralis muscles of the chest have been proposed as a potential alternative region of interest for assessing muscularity with CT. We found that patients who were mechanically ventilated and critically ill diagnosed with malnutrition had a lower PMA and PM index than that of age- and sex-matched patients who were critically ill and mechanically ventilated without malnutrition. This is a novel finding because there is a paucity of data on the use of CT-derived PM measures to compare nutrition status among ICU patients.1 If confirmed in larger studies, it raises the possibility of using chest CT scans obtained in the course of usual clinical care as phenotypic criteria to identify patients at high nutrition risk.

Although there were no statistically significant differences between groups for comorbid conditions, patients with malnutrition exhibited greater rates of substance abuse, which is shown to impact nutrition status and muscle mass.22,25 Our group has previously reported this observation in a general sample of hospital patients,22,25 and it merits further investigation as a potential challenge for nutrition interventions.

The PMA was significantly lower in the malnutrition group. This result is not surprising because cut-offs have been derived for low muscularity using CT scans taken at the third lumbar vertebra and used for the diagnosis of sarcopenia,26 a disease that shares the characteristic loss of muscle mass with malnutrition. However, this is an important and novel result for the field because this is the first study to compare CT-derived PM measures between ICU patients with and without malnutrition, laying the groundwork for future research including pectoral CT scans in the assessment of nutrition status.1 Most, if not all, other research using CT to differentiate patients based on nutrition status uses scans of the abdominal muscles as a whole or the psoas muscle alone.1 We also found a significant difference between groups for PM index. Similar to the PMA finding, no other study has used the PM index to determine differences between ICU patients with and without malnutrition. Finally, the finding that there was no difference between groups for muscle quality (mean PMHU) is unexpected because previous research using third lumbar vertebra CT scans has reported lower mean Hounsfield units in patients with malnutrition than without.14,27 Further, low PMA has been associated with mortality in hospitalized patients with COVID-19, patients with pneumonia, and patients in the ICU.18–20 Although not well studied, there are a variety of potential confounders that are thought to impact CT-derived muscle size and quality measures, such as variations in scanner, tube voltage, patient positioning, use of contrast dye, edema, and patient geometry.23 Additionally, there is no one specific value but, rather, a range for Hounsfield units to determine muscle and fat,28 which could cause tissue measures based on this unit to include more (or less) than just the target tissue.29 Patients with excess edema or fatty infiltration of the muscle would typically be interpreted as having lower muscle qualityit is not clear what impact other confounding variables can have on this variable. These issues merit further investigation in the application of CT-derived muscle measures at the chest and other regions of interest.

No statistically significant differences were found in any of the clinical outcome variables of interest (eg, in-hospital mortality or length of stay) between patients with and without malnutrition. This finding differs from that of previous literature. For example, a study of generalized hospital patients admitted to a tertiary care hospital reported both increased length of stay and increased mortality in the patients who had malnutrition.30 In a more recent large-scale multisite study, hospitalized adult inpatients with variable admission diagnoses and comorbid malnutrition were less likely to be discharged home independently and more likely to experience a longer length of stay.31 An important difference between our study and previous research is that our sample consisted only of those who were mechanically ventilated and critically ill. Clinical outcomes in ICU settings are impacted by several factors, including disease severity and/or complexity,31,32 which can vary widely among ICU patients33 and were not adjusted for in this research.

This study also examined the ability of CT-derived PM parameters to predict clinical outcomes. In our patient population, these measures were not significant predictors of in-hospital mortality, discharge disposition, or length of stay. This finding is in contrast to a previous report, which showed that PM index and mean PMHU were significant predictors of mortality in patients recovering from left-ventricular assist device implantation.23 The discrepancy between findings from prior studies and our findings is not surprising, given that our study was not adequately powered to determine the predictive ability of PMA, mean PMHU, and PM index on clinical outcomes. Future studies based on larger sample sizes with adequate power are needed to investigate the utility of PM measures to predict clinical outcomes in the ICU setting.

STRENGTHS

This study has several notable strengths. The two groups consisting of patients with and without malnutrition were similar in demographic and medical characteristics, which, in part, was a result of the matching process. Particularly important is the equal distribution of male patients and female patients between groups, given that sex is an important determinant of muscle mass.34 Similarly, matching based on age mitigates the effects of age-related muscle loss.34,35 Additionally, interobserver reliability for data collection was high. Finally, this study was adequately powered to detect differences in pectoralis muscle measures between patients with and without malnutrition.

LIMITATIONS

This research does have some limitations. This was a retrospective study, therefore, we were limited to data that were previously collected; thus, data on potential confounding factors, such as severity and stage of disease, were not available. Furthermore, the diagnosis of malnutrition in patients who are mechanically ventilated is challenging because of sedation and the inability of patients to provide information regarding diet and weight history; therefore, it is possible that patients with malnutrition may have been missed by the traditional methods of nutrition assessment used in this study. These unmeasured factors could play a role in the primary measures of body composition because severe acute disease causes inflammation that increases nutrition demands, which, if left unaccounted for, will lead to malnutrition and loss of muscle mass.36 In addition, this study was underpowered to determine the predictive ability of PMA, mean PMHU, and PM index, and, thus, we were unable to define cut points for low muscularity based on pectoralis muscle measures.

CONCLUSION

This research is the first to provide evidence that the PM index is lower in patients with malnutrition compared with those without malnutrition. Pectoralis muscle area was also lower in patients with malnutrition compared with that of those without malnutrition. This study provides initial evidence that is an important step forward in actualizing the GLIM call to action for validation of more quantitative, objective muscle assessment methods in clinical settings.5 Prospective research in a larger sample of patients in the ICU should investigate the utility of PM measures to predict clinical outcomes and define low-muscularity cut points that could be used to interpret these measures. Such work could result in the development of these measures for potential application in nutrition assessment. If this occurs, it may be possible to incorporate routine CT scan reports into registered dietitians’ practice in collaboration with radiology departments (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Computed tomography scan of chest. The large arrow pointing downward shows the pectoralis major, and the small arrow pointing upward shows the pectoralis minor.

CLINICAL RELEVANCY STATEMENT.

Computed tomography (CT) scanning of the chest is frequently performed for clinical reasons unrelated to nutrition assessment in patients being admitted to intensive care units (ICUs). In this study, CT-derived pectoralis major (PM) muscle size and quality were evaluated as a novel component of nutrition status assessment. We sought to determine if these factors could be useful in the identification of malnutrition. Pectoralis muscle parameters were compared between mechanically ventilated medical ICU patients with and without malnutrition. These parameters were also used to compare medical outcomes such as length of stay, in-hospital mortality, and discharge disposition. Patients with malnutrition had lower PM muscle size than adequately nourished patients; however, PM muscle quality and patient outcomes were similar between groups. For clinicians, the assessment of pectoralis muscle size using CT scans obtained for other reasons may aid in the diagnosis of malnutrition and monitoring of nutrition status to improve quality of care and clinical outcomes.

Funding information

Richard J. Caplan was partially supported in this work by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health, Grant/Award Number: U54-GM104941 (PI: Hicks).

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

REFERENCES

  • 1.Smith LO, Olieman JF, Berk KA, Ligthart-Melis GC, Earthman CP. Clinical applications of body composition and functional status tools for nutrition assessment of hospitalized adults: a systematic review. JPEN J Parenter Enteral Nutr. 2022;47(1):11–29. [DOI] [PubMed] [Google Scholar]
  • 2.White JV, Guenter P, Jensen G, Malone A, Schofield M. Consensus statement: Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition). JPEN J Parenter Enteral Nutr. 2012; 36(3):275–283. [DOI] [PubMed] [Google Scholar]
  • 3.Barazzoni R, Jensen GL, Correia MITD, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition (GLIM) diagnosis of malnutrition. Clin Nutr. 2022;41(6):1425–1433. [DOI] [PubMed] [Google Scholar]
  • 4.Keller H, de van der Schueren MAE, Jensen GL, et al. Global Leadership Initiative on Malnutrition (GLIM): guidance on validation of the operational criteria for the diagnosis of protein-energy malnutrition in adults. JPEN J Parenter Enteral Nutr. 2020;44(6):992–1003. [DOI] [PubMed] [Google Scholar]
  • 5.Compher C, Cederholm T, Correia MITD, et al. Guidance for assessment of the muscle mass phenotypic criterion for the Global Leadership Initiative on Malnutrition diagnosis of malnutrition. JPEN J Parenter Enteral Nutr. 2022;46(6):1232–1242. [DOI] [PubMed] [Google Scholar]
  • 6.Earthman CP. Body composition tools for assessment of adult malnutrition at the bedside: a tutorial on research considerations and clinical applications. JPEN J Parenter Enteral Nutr. 2015;39(7): 787–822. [DOI] [PubMed] [Google Scholar]
  • 7.Prado CM, Lieffers JR, McCargar LJ, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 2008;9(7):629–635. [DOI] [PubMed] [Google Scholar]
  • 8.Martin L, Birdsell L, MacDonald N, et al. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol. 2013;31(12): 1539–1547. [DOI] [PubMed] [Google Scholar]
  • 9.Mourtzakis M, Prado CMM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. 2008; 33(5):997–1006. [DOI] [PubMed] [Google Scholar]
  • 10.Carey EJ, Lai JC, Wang CW, et al. A multicenter study to define sarcopenia in patients with end-stage liver disease. Liver Transpl. 2017;23(5):625–633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Weijs PJM, Looijaard WGPM, Dekker IM, et al. Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Crit Care. 2014;18(2):R12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Goodpaster BH, Carlson CL, Visser M, et al. Attenuation of skeletal muscle and strength in the elderly: the health ABC study. J Appl Physiol. 2001;90(6):2157–2165. [DOI] [PubMed] [Google Scholar]
  • 13.Xie L, Jiang J, Fu H, Zhang W, Yang L, Yang M. Malnutrition in relation to muscle mass, muscle quality, and muscle strength in hospitalized older adults. J Am Med Dir Assoc. 2022;23(5): 722–728. [DOI] [PubMed] [Google Scholar]
  • 14.Huang DD, Yu DY, Song HN, et al. The relationship between the GLIM-defined malnutrition, body composition and functional parameters, and clinical outcomes in elderly patients undergoing radical gastrectomy for gastric cancer. Eur J Surg Oncol. 2021;47(9):2323–2331. [DOI] [PubMed] [Google Scholar]
  • 15.Martin L, Gioulbasanis I, Senesse P, Baracos VE. Cancer-associated malnutrition and CT-defined sarcopenia and myosteatosis are endemic in overweight and obese patients. JPEN J Parenter Enteral Nutr. 2020;44(2):227–238. [DOI] [PubMed] [Google Scholar]
  • 16.Teigen LM, John R, Kuchnia AJ, et al. Preoperative pectoralis muscle quantity and attenuation by computed tomography are novel and powerful predictors of mortality after left ventricular assist device implantation. Circ Heart Fail. 2017;10(9):e004069. [DOI] [PubMed] [Google Scholar]
  • 17.Aliaga M, Forel JM, De Bourmont S, et al. Diagnostic yield and safety of CT scans in ICU. Intensive Care Med. 2015;41(3):436–443. [DOI] [PubMed] [Google Scholar]
  • 18.Jaitovich A, Khan MMHS, Itty R, et al. ICU admission muscle and fat mass, survival, and disability at discharge. Chest. 2019; 155(2):322–330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.van Bakel SIJ, Gietema HA, Stassen PM, et al. CT scan-derived muscle, but not fat, area independently predicts mortality in COVID-19. Chest. 2023;164(2):314–322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Yokosuka R, Imai R, Ro S, et al. Pectoralis muscle mass on chest CT at admission predicts prognosis in patients with pneumonia. Can Respir J. 2021;2021:3396950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.McDonald M-LN, Diaz AA, Rutten E, et al. Chest computed tomography-derived low fat-free mass index and mortality in COPD. Eur Respir J. 2017;50(6):1701134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Smith LO, Vest MT, Rovner AJ, et al. Prevalence and characteristics of starvation-related malnutrition in a mid-Atlantic healthcare system: a cohort study. JPEN J Parenter Enteral Nutr. 2021;46(2):357–366. [DOI] [PubMed] [Google Scholar]
  • 23.Teigen LM, Kuchnia AJ, Mourtzakis M, Earthman CP. The use of technology for estimating body composition: strengths and weaknesses of common modalities in a clinical setting. Nutr Clin Pract. 2017;32(1):20–29. [DOI] [PubMed] [Google Scholar]
  • 24.Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R. Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol. 1998;85(1):115–122. [DOI] [PubMed] [Google Scholar]
  • 25.Jeynes KD, Gibson EL. The importance of nutrition in aiding recovery from substance use disorders: a review. Drug Alcohol Depend. 2017;179:229–239. [DOI] [PubMed] [Google Scholar]
  • 26.Derstine BA, Holcombe SA, Ross BE, Wang NC, Su GL, Wang SC. Skeletal muscle cutoff values for sarcopenia diagnosis using T10 to L5 measurements in a healthy US population. Sci Rep. 2018;8(1):11369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Yeh DD, Ortiz-Reyes LA, Quraishi SA, et al. Early nutritional inadequacy is associated with psoas muscle deterioration and worse clinical outcomes in critically ill surgical patients. J Crit Care. 2018;45:7–13. [DOI] [PubMed] [Google Scholar]
  • 28.Bryant JA, Drage NA, Richmond S. CT number definition. Radiat Phys Chem. 2012;81(4):358–361. [Google Scholar]
  • 29.Mazonakis M, Damilakis J. Computed tomography: what and how does it measure. Eur J Radiol. 2016;85(8):1499–1504. [DOI] [PubMed] [Google Scholar]
  • 30.Leiva Badosa E, Badia Tahull M, Virgili Casas N, et al. Cribado de la desnutrición hospitalaria en la admisión: la desnutrición aumenta la mortalidad y la duración de la estancia hospitalaria. Nutr Hosp. 2017;34(4):907–913.29095016 [Google Scholar]
  • 31.Lengfelder L, Mahlke S, Moore L, Zhang X, Williams G, Lee J. Prevalence and impact of malnutrition on length of stay, readmission, and discharge destination. JPEN J Parenter Enteral Nutr. 2022;46(6): 1335–1342. [DOI] [PubMed] [Google Scholar]
  • 32.Godinjak A, Iglica A, Rama A, et al. Predictive value of SAPS II and APACHE II scoring systems for patient outcome in medical intensive care unit. Acta Med Acad. 2016;45(2):89–95. [DOI] [PubMed] [Google Scholar]
  • 33.Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10): 818–829. [PubMed] [Google Scholar]
  • 34.Janssen I, Heymsfield SB, Wang ZM, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr. J Appl Physiol. 2000;89(1):81–88. [DOI] [PubMed] [Google Scholar]
  • 35.Wilkinson DJ, Piasecki M, Atherton PJ. The age-related loss of skeletal muscle mass and function: measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev. 2018;47:123–132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Jensen GL, Mirtallo J, Compher C, et al. Adult starvation and disease-related malnutrition: a proposal for etiology-based diagnosis in the clinical practice setting from the International Consensus Guideline Committee. JPEN J Parenter Enteral Nutr. 2010;34(2):156–159. [DOI] [PubMed] [Google Scholar]

RESOURCES