Sub-Saharan Africa revenue grew 4% primarily driven by performance in South Africa. Early enteral feeding is recommended for all critical care patients, including those with COVID-19. Although the systematic search strategy followed by meticulous data abstraction allowed us to capture the majority of relevant studies, it is important to note that a portion of the data abstraction was conducted in studies that were not necessarily driven by validity but possessed other study purposes. Thus, a recommendation may be “strong” despite comparatively weak published evidence if the net benefits outweigh the harms from its adoption. Over the course of the 2018 financial year, we commenced the transition to Alula, our new global infant formula brand, across all our territories. We’ve summarized the recommendations in easy-to-understand terms. Only a few studies (DXA and BIA) included in this systematic review possessed study populations larger than 100 participants. When tube feeding pumps are scarce, prioritize pumps for patients with a small bowel feeding tube or those with poor tolerance to gravity or bolus feeding. The individual study correlations ranged from moderate to very strong (0.52–0.86), and the overall random effects summary correlation between the DXA measure of trunk fat/intra‐abdominal fat/VAT and the CT assessment of VAT was strong (0.74, 95% confidence interval [CI] 0.52‐0.86). For US, this could include comparisons to DXA, CT, MRI, or multicompartment models. Correlation coefficients were categorized as very low (r = 0–0.20), low (r = 0.21–0.40), moderate (r = 0.41–0.60), high (r = 0.61–0.80), and very high (r = 0.81–1.0). Equally important are concerns related to statistical analyses and data interpretation. For now, body composition data must be evaluated as a continuous variable, and only agreement estimates can be used to appraise the overall quality of the studies used. As noted in Table 1, lean soft tissue is often referred to as skeletal muscle, muscle tissue, or FFM. Rather, studies evaluating the validity of the body composition methodologies in question (ie, DXA, US, and BIA) were included. In addition, since muscle atrophy can be disproportionate,62 previous investigators have demonstrated the importance of investigating both upper and lower body muscle groups when evaluating longitudinal changes in body composition.63 Second, US measurements often report muscle and/or adipose tissue as thickness (mm) or cross‐sectional area (mm2), whereas DXA data are presented as whole body mass (kg) and CT data may be reported as muscle cross‐sectional area (cm2). No recommendations can be made regarding the validity of using BIA in clinical populations. Circumstances in clinical settings and patient indications may require actions different from those recommended in this document, and in those cases, the judgment of the treating professional should prevail. No data exist to support its validity in adult patient populations. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username. [This article was modified on September 05, 2019, after initial online publication to add the last four references and to correct three errors in Table 6. For a comprehensive review of this methodology, please refer to Heymsfield et al.2 The use of DXA for body composition assessment is more challenging in the acute care setting and at this time would likely not procure results to directly impact clinical care, specifically nutrition support. AF, abdominal fat; ALTMI, appendicular lean tissue mass index; AN, anorexia nervosa; ASMI, appendicular skeletal muscle index; ASMM, appendicular skeletal muscle mass; BAI, body adiposity index; BF, body fat; BMI; body mass index; CAF, central abdominal fat; CC, correlation coefficient; CRP, C‐reactive protein; CT, computed tomography; DBP, diastolic blood pressure; DSAT, deep subcutaneous adipose tissue; DXA, dual energy x‐ray absorptiometry; FFM, fat‐free mass; FFMI, fat‐free mass index; FM, fat mass; GE, General Electric; HDL, high density lipoprotein; IAF, intraabdominal fat; IMAT, intermuscular adipose tissue; IR, insulin resistance; IS; insulin sensitivity; LSTM, lean soft tissue mass; LTMI, lean tissue mass index; MRI, magnetic resonance imaging; PWS, Prader‐Willi syndrome; SAF, subcutaneous abdominal fat; SAT, subcutaneous adipose tissue; SBP, systolic blood pressure; SD, standard deviation; SF, subcutaneous fat; SSAT, superficial subcutaneous adipose tissue; TAAT, total abdominal adipose tissue; TAT, total adipose tissue; TF, total fat; TSAT, total subcutaneous adipose tissue; VAT, visceral adipose tissue. We’ve summarized the recommendations in easy-to-understand terms. On behalf of the American Society for Parenteral and Enteral Nutrition (ASPEN), a systematic review was conducted to evaluate the best available evidence regarding the validity of relevant body composition methods (eg, dual energy X‐ray absorptiometry [DXA], ultrasound [US], and bioelectrical impedance analysis [BIA]) in clinical populations. Furthermore, because of the variability of body compartments estimated within studies, it was not possible to conflate these data by manufacturer to support summary correlations and forest plots, DXA (Lunar Prodigy) vs CT (Helical CT scan), DXA (DPX‐L) vs CT (Siemens Magnetom TrioTim 3‐Tesla), To compare the relative importance of CT‐measured abdominal adiposity compartment areas, including adipose tissue located posterior to the subcutaneous fascia, in predicting plasma lipid‐lipoprotein alterations, To investigate the ability of the BAI to detect changes in % BF levels before and after a weight loss intervention when compared with % BF levels measured using DXA and to examine the relationship between BAI with cardiometabolic risk factors, DXA (GE Lunar) vs CT (High Speed Advantage), To report a comparative analysis of different trunk fat measurements predicting glycemia, IR, and β cell function in middle aged Indian men, DXA (Lunar DPX‐IQ 240) vs MRI (1 Tesla Imaging Device), DXA (Lunar Prodigy Advance) vs CT (Leonardo Syngo), DXA (Lunar DPX) vs CT (Siemens HIQ System), DXA (Lunar DPX‐L) vs CT (Sytec 3000 scanner), DXA (not specified) vs CT (GE High Speed Advantage), DXA (Hologic QDR 4) vs CT (variable across sites), % BF (DXA) vs VAT (area), SAT (area), abdominal muscle IMAT (area), paraspinal IMAT (area), and psoas IMAT (area) (CT), DXA (Lunar Prodigy High Speed Digital Fan Beam) vs CT (not specified), L3 adipose tissue (kg), whole body FM (kg), L3 FFM, whole body FFM, ASMM (DXA) vs adipose tissue (cm, DXA (GE Healthcare Lunar Prodigy) vs CT (Phillips Extended Brilliance), DXA (GE Prodigy, DPX, DPX‐IQ, and DPX‐L and Hologic QDR 2000 [pencil beam] and 4500 [fan beam]) vs MRI (not specified), Trunk, leg, arm, and TF (DXA) vs trunk, leg, arm, and TF (MRI), DXA (Hologic QDR 2000) vs CT (GE High Speed), DXA (Prodigy GE Healthcare) vs US (SonoSite X Porte, 13‐6 MHz Transducer), DXA (GE Lunar Prodigy Advance) vs US (GE Logiq Book XP with a GE 3C‐RS 2‐5 MHz transducer), MRI (GYROSCAN S15, Philips with a 1.5 T [64 MHz] magnetic field) versus US (Phillips 5000 SonoCT with an abdominal C 5‐2 40R 2‐5 MHz transducer, To validate the use of ultrasound in COPD patients against DXA to measure responsiveness to changes in quadriceps after resistance training, DXA (GE Lunar Prodigy Advance with manufacturer's software) vs US (Hitachi EUB‐425 with a 7.5 linear transducer), Thigh LM (DXA) vs rectal femoral thickness and QMT (US), To compare anthropometric, US, and CT measurements of body fat distribution at baseline and 1 year after laparoscopic adjustable gastric banding, CT (Toshiba TCT – 900S) versus US (GE RT 2800 convex transducer 3.5 MHz), To assess the correlation between US and CT for the evaluation of visceral adiposity in women with obesity (including those with hypertension and mild‐to‐moderate disturbances of lipid profile), CT (not specified) vs US (not specified, 3.5 MHz probe), To assess the correlation between US quadriceps muscle size with quadriceps strength and FFM in patients with COPD, CT (Siemens SOMATOM Sensation 64‐slice scanner, MagicView VE 40 software) vs US (Toshiba PLM805), Rectus femoralis cross‐sectional area (US) vs rectus femoralis cross‐sectional area (CT), To evaluate the adequacy of SF‐BIA and MF‐BIA, in comparison with DXA to evaluate body composition in maintenance hemodialysis patients, To determine the reliability of MF‐BIA in assessing the nutrition status of hemodialysis patients, To evaluate MF‐BIA in assessing body composition compared with standard DXA scanning in peritoneal dialysis patients, To determine the availability of BIA, CT, and MRI for measurement of SMM in patients with rheumatic diseases and quantitatively assess skeletal muscle loss after glucocorticoid treatment, To analyze precisely and critically which method SFA, BIA, and BIS is most accurate and available for common use in clinical practice for measurement of FFM in patients with COPD, BIA dual frequency InnerScan using manufacturer's software, To compare the agreement of absolute values estimated by BIS relative to ADP and DXA and changes in body composition detected by BIS relative to ADP at 3 months after peripheral blood stem cell transplantation, To elucidate early change of intra‐abdominal fat in response to calorie restriction in patients with obesity, diabetes or metabolic syndrome by weekly evaluation using a dual BIA, To assess MF‐BIA against DXA in patients with recent stroke or TIA using FM and FFM; the secondary objective was to examine the internal validity of MF‐BIA, To evaluate the use of multifrequency BIS measurements of ICV to model total body SMM and limb SMM in hemodialysis patients, To determine the applicability of a single BIA formula in subjects who were pre or post liver, lung, and heart transplantation, To apply a model to estimate FM using MF‐BIS in hemodialysis patients and control subjects, To analyze the validity of BIA for determining VF course in bariatric patients PO, To validate the data obtained by BIA to measure FM in HIV+ patients with/without lipoatrophy and to determine if BIA correctly diagnoses lipoatrophy in HIV+ patients, To assess the accuracy and precision of a BIS device and the relative contribution of BIS beyond the anthropometric parameters, DXA Hologic QDR4500A software version 12.6, To assess preoperative and postoperative agreement in FFM between BIS and DXA in patients undergoing cardiac surgery to assess preoperative and postoperative agreement in FFM between BIS and DXA in patients undergoing cardiac surgery, To evaluate the ability of BIS to estimate FFM in end‐stage renal disease patients using DXA as a reference, To examine the utility of a Dual BIA for measuring IAFA in patients with obesity and Metabolic Syndrome during weight reduction, To evaluate the relationship of calf bioimpedance with total body composition and fluid status as measured by gold standard methods, To compare the estimates of body FM by 2 simple bedside techniques such as BIA and skin fold measurements, To compare anthropometry with BIA in relation to DXA as methods of nutrition assessment and body composition in outpatients with COPD, To evaluate which method (BIA, anthropometry and body adiposity index), presents the higher accuracy to estimate body adiposity compared with DXA in nondialyzed CKD patients, To compare estimates of body FM and FFM by BIA and skinfold measurements with values obtained from DXA in males with HIV vs healthy controls, To determine the agreement of FFM, FM, and % FM measurements taken with DXA and BIA in adults with cystic fibrosis.

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