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

Utility of Lean Body Mass Equations and Body Mass Index for Predicting Outcomes in Critically Ill Adults with Sepsis: A Retrospective Study

Lean body mass is a significant component of survival from sepsis. Several equations can be used for calculating lean body mass based on age, sex, body weight, and height. We hypothesized that lean body mass is a better predictor of outcomes than the body mass index (BMI). This study used a multicenter cohort study database. The inclusion criteria were age ≥18 years and a diagnosis of sepsis or septic shock. BMI was classified into four categories: underweight (<18.5 kg/m2), normal (≥18.5-<25 kg/m2), overweight (≥25-<30 kg/m2), and obese (≥30 kg/m2). Four lean body mass equations were used and categorized on the basis of quartiles. The outcome was in-hospital mortality among different BMI and lean body mass groups. Among 85,558 patients, 3916 with sepsis were included in the analysis. Regarding BMI, in-hospital mortality was 36.9%, 29.8%, 26.7%, and 27.9% in patients who were underweight, normal weight, overweight, and obese, respectively (p < 0.01). High lean body mass did not show decreased mortality in all four equations. In critically ill patients with sepsis, BMI was a better predictor of in-hospital mortality than the lean body mass equation at intensive care unit (ICU) admission. To precisely predict in-hospital mortality, ICU-specific lean body mass equations are needed.

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

Background and Objectives

REC-2282 is a novel histone deacetylase inhibitor that has shown antitumor activity in in vitro and in vivo models of malignancy.  The aims of this study were to characterize the population pharmacokinetics of REC-2282 (AR-42) from the first-in-human (NCT01129193) and phase I acute myeloid leukemia trials (NCT01798901) and to evaluate potential sources of variability. Additionally, we sought to understand alternate body size descriptors as sources of inter-individual variability (IIV), which was significant for dose-normalized maximum observed concentration and area under the concentration-time curve (AUC).

Methods

Datasets from two clinical trials were combined, and population pharmacokinetic analysis was performed using NONMEM and R softwares; patient demographics were tested as covariates.

Results

A successful population pharmacokinetic model was constructed. The pharmacokinetics of REC-2282 were best described by a two-compartment model with one transit compartment for absorption, first-order elimination and a proportional error model. Fat-free mass (FFM) was retained as a single covariate on clearance (CL), though it explained < 3% of the observed variability on CL. Tumor type and formulation were retained as covariates on lag time, and a majority of variability, attributed to absorption, remained unexplained. Computed tomography (CT)-derived lean body weight estimates were lower than estimated lean body weight and fat-free mass measures in most patients. Analysis of dose-normalized AUC vs. body size descriptors suggests flat dosing is most appropriate for REC-2282.

Conclusions

FFM was identified as a significant covariate on CL; however, it explained only a very small portion of the IIV; major factors contributing significantly to REC-2282 pharmacokinetic variability remain unidentified.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13318-021-00722-z.

The Novel Use of Point-of-Care Ultrasound to Predict Resting Energy Expenditure in Critically Ill Patients

OBJECTIVES

Accurate estimation of a critically ill patient's caloric requirements is essential for a proper nutritional plan. This study aimed to evaluate the use of point-of-care ultrasound (US) to predict the resting energy expenditure (REE) in critically ill patients.

METHODS

In 69 critically ill patients, we measured the REE using indirect calorimetry (REE_IC), muscle layer thicknesses (MLTs), and cardiac output (CO). Muscle thickness was measured at the biceps and the quadriceps muscles. Patients were randomly split into a model development group (n = 46) and a cross-validation group (n = 23). In the model development group, a multiple regression analysis was applied to generate REE using US (REE_US) values. In the cross-validation group, REE was calculated by the REE_US and the resting energy expenditure using the Harris-Benedict equation (REE_HB), and both were compared to the REE_IC.

RESULTS

In the model development group, the REE_US was predicted by the following formula: predicted REE_US (kcal/d) = 206 + 173.5 × CO (L/min) + 137 × MLT (cm) - 230 × (women = 1; men = 0) (R²  = 0.8; P < .0001). In the cross-validated group, the REE_IC and REE_US values were comparable (mean difference, -66 [-3.3%] kcal/d; P = .14). However, the difference between the mean REE_IC and the mean REE_HB was 455.8 (26%) kcal/d (P < .001). According to a Bland-Altman analysis, the REE_US agreed well with the REE_IC, whereas the REE_HB did not.

CONCLUSIONS

Resting energy expenditure could be estimated from US measurements of MLTs and CO. Our point-of-care US model explains 80% of the change in the REE in critically ill patients.

Predictive Value of Estimated Lean Body Mass for Neurological Outcomes after Out-of-Hospital Cardiac Arrest

Background: Postcardiac arrest patients with a return of spontaneous circulation (ROSC) are critically ill, and high body mass index (BMI) is ascertained to be associated with good prognosis in patients with a critically ill condition. However, the exact mechanism has been unknown. To assess the effectiveness of skeletal muscles in reducing neuronal injury after the initial damage owing to cardiac arrest, we investigated the relationship between estimated lean body mass (LBM) and the prognosis of postcardiac arrest patients. Methods: This retrospective cohort study included adult patients with ROSC after out-of-hospital cardiac arrest from January 2015 to March 2020. The enrolled patients were allocated into good- and poor-outcome groups (cerebral performance category (CPC) scores 1–2 and 3–5, respectively). Estimated LBM was categorized into quartiles. Multivariate regression models were used to evaluate the association between LBM and a good CPC score. The area under the receiver operating characteristic curve (AUROC) was assessed. Results: In total, 155 patients were analyzed (CPC score 1–2 vs. 3–5, n = 70 vs. n = 85). Patients’ age, first monitored rhythm, no-flow time, presumed cause of arrest, BMI, and LBM were different (p < 0.05). Fourth-quartile LBM (≥48.98 kg) was associated with good neurological outcome of postcardiac arrest patients (odds ratio = 4.81, 95% confidence interval (CI), 1.10–25.55, p = 0.04). Initial high LBM was also a predictor of good neurological outcomes (AUROC of multivariate regression model including LBM: 0.918). Conclusions: Initial LBM above 48.98kg is a feasible prognostic factor for good neurological outcomes in postcardiac arrest patients.

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

Practice guidelines provide clear evidence-based recommendations for the use of drug therapy to manage pain, agitation, and delirium associated with critical illness. Dosing recommendations however are often based on strategies used in patients with normal body habitus. Recommendations specific to critically ill patients with extreme obesity are lacking. Nonetheless, clinicians must craft dosing regimens for this population. This paper is intended to help clinicians design initial dosing regimens for medications commonly used in the management of pain, agitation, and delirium in critically ill patients with extreme obesity. A detailed literature search was conducted with an emphasis on obesity, pharmacokinetics, and dosing. Relevant manuscripts were reviewed and strategies for dosing are provided.

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

This phase I trial sought to determine a biologically safe and effective dose of AR-42, a novel histone deacetylase inhibitor, which would lead to a doubling of miR-29b prior to decitabine administration. Thirteen patients with previously untreated or relapsed/refractory AML were treated at 3 dose levels (DL): AR-42 20 mg qd on d1,3,5 in DL1, 40 mg qd on d1,3,5 in DL2 and 40 mg qd on d1,3,4,5 in DL3. Patients received decitabine 20 mg/m² on d6-15 of each induction cycle and 20 mg/m² on d6-10 of each maintenance cycle. One DLT of polymicrobial sepsis and multi-organ failure occurred at DL3. Two patients achieved a CRi and one patient achieved a CR for an ORR of 23.1%. The higher risk features of this patient population and the dosing schedule of AR-42 may have led to the observed clinical response and failure to meet the biologic endpoint.

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

BACKGROUND & AIMS

The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantification of body composition, but manual analysis is laborious and costly. The primary aim of this study was to develop an automated body composition analysis framework using CT scans.

METHODS

CT scans of the 3rd lumbar vertebrae from critically ill, liver cirrhosis, pancreatic cancer, and clear cell renal cell carcinoma patients, as well as renal and liver donors, were manually analyzed for body composition. Ninety percent of scans were used for developing and validating a neural network for the automated segmentation of skeletal muscle and adipose tissues. Network accuracy was evaluated with the remaining 10 percent of scans using the Dice similarity coefficient (DSC), which quantifies the overlap (0 = no overlap, 1 = perfect overlap) between human and automated segmentations.

RESULTS

Of the 893 patients, 44% were female, with a mean (±SD) age and body mass index of 52.7 (±15.8) years old and 28.0 (±6.1) kg/m², respectively. In the testing cohort (n = 89), DSC scores indicated excellent agreement between human and network-predicted segmentations for skeletal muscle (0.983 ± 0.013), and intermuscular (0.900 ± 0.034), visceral (0.979 ± 0.019), and subcutaneous (0.986 ± 0.016) adipose tissue. Network segmentation took ~350 milliseconds/scan using modern computing hardware.

CONCLUSIONS

Our network displayed excellent ability to analyze diverse body composition phenotypes and clinical cohorts, which will create feasible opportunities to advance our capacity to predict health outcomes in clinical populations.

Estimation of appendicular skeletal muscle: Development and validation of anthropometric prediction equations in Chinese patients with knee osteoarthritis

OBJECTIVE

To develop anthropometric prediction equations for estimating appendicular skeletal muscle (ASM) in Chinese knee osteoarthritis patients.

METHODS

Subjects were divided into the model development group (MD group: 104 cases, 47 men and 57 women) and cross-validation group (CV group: 69 cases, 38 men and 31 women). Stepwise multiple linear regression analyses were undertaken in the MD group to identify the best equations. Agreement between the estimated ASM and ASM measured by dual-energy X-ray absorptiometry (DXA) was tested in the CV group.

RESULTS

Two models were developed in the MD group. Validation in the CV group showed that our models (R²  = 0.83 and R²  = 0.90) had a high coefficient of determination. The mean bias of ASM estimated by the two models from the ASM measured by DXA in the CV group showed no significant difference (P > 0.05).

CONCLUSION

These models could be useful for older Chinese patients with knee osteoarthritis to estimate ASM.

Measuring and monitoring lean body mass in critical illness

PURPOSE OF REVIEW

To help guide metabolic support in critical care, an understanding of patients' nutritional status and risk is important. Several methods to monitor lean body mass are increasingly used in the ICU and knowledge about their advantages and limitations is essential.

RECENT FINDINGS

Computed tomography scan analysis, musculoskeletal ultrasound, and bioelectrical impedance analysis are emerging as powerful clinical tools to monitor lean body mass during ICU stay. Accuracy, expertise, ease of use at the bedside, and costs are important factors which play a role in determining which method is most suitable. Exciting new research provides an insight into not only quantitative measurements, but also qualitative measurements of lean body mass, such as infiltration of adipose tissue and intramuscular glycogen storage.

SUMMARY

Methods to monitor lean body mass in the ICU are under constant development, improving upon bedside usability and offering new modalities to measure. This provides clinicians with valuable markers with which to identify patients at high nutritional risk and to evaluate metabolic support during critical illness.

Body Composition Technology: Implications for the ICU

Malnutrition continues to be highly prevalent in hospitalized and critically ill patients and is associated with significant morbidity and mortality. Additionally, survivors of critical illness have an increased risk for sarcopenia, which leads to weakness and physical debilitation that can persist for years. Nutrition risk assessment tools have been developed and validated in critically ill patients but have limitations. Variables such as body weight, body mass index, weight change, or percentage of food intake can be difficult to obtain in critically ill patients and may be misleading given changes in body composition, such as an increase in body water. Assessment of body composition through new techniques provides a unique opportunity to counter some of these limitations and develop improved methods of nutrition risk assessment based on objective data. The present manuscript provides a review of the most commonly available clinical technology for assessment of body composition (bioimpedance, computed tomography, and ultrasound), including data from trials in critically ill patients highlighting the benefits and weaknesses of each modality.

Patient-specific lean body mass can be estimated from limited-coverage computed tomography images

OBJECTIVE

In PET/CT, quantitative evaluation of tumour metabolic activity is possible through standardized uptake values, usually normalized for body weight (BW) or lean body mass (LBM). Patient-specific LBM can be estimated from whole-body (WB) CT images. As most clinical indications only warrant PET/CT examinations covering head to midthigh, the aim of this study was to develop a simple and reliable method to estimate LBM from limited-coverage (LC) CT images and test its validity.

PATIENTS AND METHODS

Head-to-toe PET/CT examinations were retrospectively retrieved and semiautomatically segmented into tissue types based on thresholding of CT Hounsfield units. LC was obtained by omitting image slices. Image segmentation was validated on the WB CT examinations by comparing CT-estimated BW with actual BW, and LBM estimated from LC images were compared with LBM estimated from WB images. A direct method and an indirect method were developed and validated on an independent data set.

RESULTS

Comparing LBM estimated from LC examinations with estimates from WB examinations (LBMWB) showed a significant but limited bias of 1.2 kg (direct method) and nonsignificant bias of 0.05 kg (indirect method).

CONCLUSION

This study demonstrates that LBM can be estimated from LC CT images with no significant difference from LBMWB.