Nutrition therapy during critical illness has been a focus of recent research, with a rapid increase in publications accompanied by two updated international clinical guidelines. However, the translation of evidence into practice is challenging due to the continually evolving, often conflicting trial findings and guideline recommendations. This narrative review aims to provide a comprehensive synthesis and interpretation of the adult critical care nutrition literature, with a particular focus on continuing practice gaps and areas with new data, to assist clinicians in making practical, yet evidence-based decisions regarding nutrition management during the different stages of critical illness.

Introduction The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed hospital systems globally, resulting in less experienced staff caring for critically ill patients within the intensive care unit (ICU). Many guidelines have been developed to guide nutrition care. Aim To identify key guidelines or practice recommendations for nutrition support practices in critically ill adults admitted with COVID-19, to describe similarities and differences between recommendations, and to discuss implications for clinical practice. Methods A literature review was conducted to identify guidelines affiliated with or endorsed by international nutrition societies or dietetic associations which included recommendations for the nutritional management of critically ill adult patients with COVID-19. Data were extracted on pre-defined key aspects of nutritional care including nutrition prescription, delivery, monitoring and workforce recommendations, and key similarities and discrepancies, as well as implications for clinical practice were summarized. Results Ten clinical practice guidelines were identified. Similar recommendations included: the use of high protein, volume restricted enteral formula delivered gastrically and commenced early in ICU and introduced gradually, while taking into consideration non-nutritional calories to avoid overfeeding. Specific advice for patients in the prone position was common, and non-intubated patients were highlighted as a population at high nutritional risk. Major discrepancies included the use of indirect calorimetry to guide energy targets and advice around using gastric residual volumes (GRVs) to monitor feeding tolerance. Conclusion Overall, common recommendations around formula type and route of feeding exist, with major discrepancies being around the use of indirect calorimetry and GRVs, which reflect international ICU nutrition guidelines.

Introduction The development of bedside methods to assess muscularity is an essential critical care nutrition research priority. We aimed to compare ultrasound‐derived muscle thickness at 5 landmarks with computed tomography (CT) muscle area at intensive care unit (ICU) admission. Secondary aims were to (1) combine muscle thicknesses and baseline covariates to evaluate correlation with CT muscle area and (2) assess the ability of the best‐performing ultrasound model to identify patients with low CT muscle area. Methods Adult patients who underwent CT scanning at the third lumbar area <72 hours after ICU admission were prospectively recruited. Muscle thickness was measured at mid‐upper arm, forearm, abdomen, and thighs. Low CT muscle area was determined using published cutoffs. Pearson correlation compared ultrasound‐derived muscle thickness and CT muscle area. Linear regression was used to develop ultrasound prediction models. Bland‐Altman analyses compared ultrasound‐predicted and CT‐measured muscle area. Results Fifty ICU patients were enrolled, aged 52 ± 20 years. Ultrasound‐derived muscle thickness at each landmark correlated with CT muscle area (P < .001). The sum of muscle thickness at mid‐upper arm and bilateral thighs, including age, sex, and the Charlson Comorbidity Index, improved the correlation with CT muscle area (r = 0.85; P < .001). Mean difference between ultrasound‐predicted and CT‐measured muscle area was −2 cm2 (95% limits of agreement, −40 cm2 to +36 cm2). The best‐performing ultrasound model demonstrated good ability to identify 14 patients with low CT muscle area (area under curve = 0.79). Conclusion Ultrasound shows potential for assessing muscularity at ICU admission (Clinicaltrials.gov NCT03019913).

Critically ill patients experience significant and rapid loss of skeletal muscle mass, which has been associated with negative clinical outcomes. The aetiology of muscle wasting is multifactorial and nutrition delivery may play a role. A systematic literature review was conducted to examine the association of energy and/or protein provision on changes in skeletal muscle mass in critically ill patients. Key databases were searched up until March 2016 to identify studies that measured skeletal muscle mass and/or total body protein (TBP) at 2 or more time points during acute critical illness (up to 2 weeks after an intensive care unit [ICU] stay). Studies were included if there was documentation of participant energy balance or mean energy delivered to participants during the time period between body composition measurements. Six studies met inclusion criteria. A variety of methods were used to assess skeletal muscle mass or TBP. Participants in included studies experienced differing levels of muscle loss (0%-22.5%) during the first 2 weeks of ICU admission. No association between energy and protein delivery and changes in skeletal muscle mass were observed. This review highlights that there is currently limited high-quality evidence to clearly define the association between energy and/or protein delivery and skeletal muscle mass changes in acute critical illness. Future studies in this area should be adequately powered, account for all potential confounding factors to changes in skeletal muscle mass, and detail all sources and quantities of energy and protein delivered to participants.

Background: Indirect calorimetry (IC) is recommended to guide energy delivery over predictive equations in critical illness due to its precision. However, the impact of using IC to measure energy expenditure on clinical outcomes is uncertain. Objective: To evaluate whether using IC to measure energy expenditure to inform energy delivery reduced hospital mortality and improved other important outcomes compared to using predictive equations in critically ill adults. Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline. Medline, Embase, CINAHL, and the Cochrane Library were searched for studies using IC to guide energy delivery compared to a predictive equation in adult critically ill patients with the primary outcome (hospital mortality) or any of the secondary outcomes reported (including but not limited to hospital and intensive care unit (ICU) length of stay (LOS) and duration mechanical ventilation (MV). Risk of bias within studies was assessed using the Cochrane “Risk of Bias” 1 tool. Random-effect meta-analyses were used when heterogeneity between studies existed (I2 > 50%). Data are reported as median (interquartile range [IQR]), binomial outcomes as odds ratio (OR), 95% confidence interval (CI), and continuous outcomes as mean difference (MD). Results: Of 4060 articles, 4 randomized controlled trials were identified with 396 patients included in analysis. Three studies were considered low risk of bias and 1 as high risk. Two studies reported hospital mortality (n = 130 and 40 participants, respectively). When combined, no association between IC-guided energy delivery and hospital mortality was found (OR = 0.81, 95% CI = [0.25, 2.67], P = 0.73, I2 = 52). No differences were reported with ICU mortality and hospital LOS between groups, but ICU LOS and duration of MV varied across all studies. According to the meta-analysis, no differences were observed in ICU LOS (MD = 1.39, 95% CI = [–5.01, 7.79], P = 0.67, I2 = 81%), although the duration of MV was increased when energy delivery was guided by IC (MD = 2.01, 95% CI = [0.45, 3.57], P = 0.01, I2 = 26%). In all 4 studies, prescribed energy targets were more closely met when energy delivery was informed by IC compared to a predictive equation. Three studies reported the percentage delivered versus the prescribed energy target, with the median (IQR) delta between the IC and predictive equation arms 19% (10%-32%). Conclusion: Limited data exist to assess the impact of using IC to inform energy delivery in comparison to predictive equations on hospital mortality. The association of IC use with other important outcomes, including duration of MV, needs to be further explored before definitive conclusions can be made.

Background: Low muscularity and malnutrition at intensive care unit (ICU) admission have been associated with negative clinical outcomes. There are limited data available evaluating the validity of bedside techniques to measure muscle mass in critically ill adults. We aimed to compare bedside methods for muscle mass assessment [bioimpedance spectroscopy (BIS), arm anthropometry and subjective physical assessment] against reference technology [computed tomography (CT)] at ICU admission. Methods: Adults who had CT scanning at the third lumbar area <72 h after ICU admission were prospectively recruited. Bedside methods were performed within 48 h of the CT scan. Pearson's correlation compared CT muscle area with BIS-derived fat-free mass (FFM) (kg) and FFM-Chamney (kg) (adjusted for overhydration), mid-upper arm circumference (cm) and mid-arm muscle circumference (cm). Depleted muscle stores were determined using published thresholds for each method. Cohen's kappa (j) was used to evaluate the agreement between bedside and CT assessment of muscularity status (normal or low). Results: Fifty participants were enrolled. There were strong correlations between CT muscle area and FFM values and mid-arm muscle circumference (P < 0.001). Using FFM-Chamney, all six (100%) participants with low CT muscle area were detected (j = 0.723). FFM-BIS, arm anthropometry and subjective physical assessment methods detected 28%-38% of participants with low CT muscle area. Conclusions: BIS-derived FFM using an adjustment algorithm for overhydration was correlated with CT muscle area and had good agreement with muscularity status assessed by CT image analysis. Arm anthropometry and subjective physical assessment techniques were not able to reliably detect participants with low CT muscle area.

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Purpose of reviewThis review describes considerations preintensive care unit (ICU), within ICU and in the post-ICU period regarding nutrition management and the current state of the literature base informing clinical care.Recent findingsWithin ICU, studies have focussed on the first 5–7 days of illness in mechanically ventilated patients who are heterogeneous and with minimal consideration to premorbid nutrition state. Many evidence gaps in the period within ICU remain, with the major ones being the amount of protein to provide and the impact of longer-term nutrition interventions. Personalised nutrition and nutrition in the post-ICU period are becoming key areas of focus.SummaryNutrition for the critically ill patient should not be viewed in isolated time periods; what happens before, during and after ICU is likely important to the overall recovery trajectory. It is critical that the impact of nutrition on clinical and functional outcomes across hospitalisation is investigated in specific groups and using interventions in ways that are biologically plausible to impact. Areas that show promise for the future of critical care nutrition include interventions delivered for a longer duration and inclusion of oral nutrition support, individualised nutrition regimes, and use of emerging bedside body composition techniques to identify patients at nutritional risk.