Critically appraising and utilising qualitative health research evidence in nutrition practice.

BACKGROUND: Evidence-based decision-making and practice recommendations are commonly based on findings from quantitative studies or reviews. In the present study, we provide an overview of how to incorporate findings from qualitative research into the evidence-based decision-making process. METHODS: To illustrate how qualitative evidence can be integrated into the decision-making process, we have outlined a clinical nutrition scenario and the process for sourcing credible evidence to inform decision-making. A qualitative health research study was selected and appraised using the Critical Appraisal Skill Programme (CASP) appraisal tool for qualitative research. Based on the results of the critical appraisal, the study quality is considered, and we discuss whether the qualitative evidence can be applied to practice. RESULTS: A detailed description of how the qualitative findings can be used conceptually and instrumentally in practice to address the clinical nutrition scenario is provided. CONCLUSIONS: Developing skills in critically appraising findings from qualitative studies will increase awareness and utilisation of this type of evidence in practice and policy, with a goal to ensure that patient/client perceptions are considered, leading to enhanced person-centred care or systems.

The role of nutrition rehabilitation in the recovery of survivors of critical illness: underrecognized and underappreciated.

Many survivors of critical illness face significant physical and psychological disability following discharge from the intensive care unit (ICU). They are often malnourished, a condition associated with poor outcomes, and nutrition remains problematic particularly in the early phases of ICU recovery. Yet nutrition rehabilitation, the process of restoring or optimizing nutritional status following illness, is seldom prioritized, possibly because it is an underrecognized and underappreciated area in critical care rehabilitation and research. To date, 16 original studies have been published where one of the objectives includes measurement of indices relating to nutritional status (e.g., nutrition intake or factors impacting nutrition intake) in ICU survivors. The primary aim of this narrative review is to provide a comprehensive summary of key themes arising from these studies which form the basis of our current understanding of nutritional recovery and rehabilitation in ICU survivors. ICU survivors face a multitude of barriers in achieving optimal nutrition that are of physiological (e.g., poor appetite and early satiety), functional (e.g., dysphagia, reduced ability to feed independently), and psychological (e.g., low mood, body dysmorphia) origins. Organizational-related barriers such as inappropriate feeding times and meal interruptions frequently impact an ICU survivor's ability to eat. Healthcare providers working on wards frequently lack knowledge of the specific needs of recovering critically ill patients which can negatively impact post-ICU nutrition care. Unsurprisingly, nutrition intake is largely inadequate following ICU discharge, with the largest deficits occurring in those who have had enteral nutrition prematurely discontinued and rely on an oral diet as their only source of nutrition. With consideration to themes arising from this review, pragmatic strategies to improve nutrition rehabilitation are explored and directions for future research in the field of post-ICU nutrition recovery and rehabilitation are discussed. Given the interplay between nutrition and physical and psychological health, it is imperative that enhancing the nutritional status of an ICU survivor is considered when developing multidisciplinary rehabilitation strategies. It must also be recognized that dietitians are experts in the field of nutrition and should be included in stakeholder meetings that aim to enhance ICU rehabilitation strategies and improve outcomes for survivors of critical illness.

Advancing qualitative health research approaches in applied nutrition research.

Applied health research methods are evolving to meet the demands of increasingly complex health research needs. Qualitative health research, focused on individual perspectives of health, wellness, illness and recovery, has emerged as a unique discipline of this field. With distinct foci, methods and rules, qualitative health research has the potential to answer applied health research questions to inform practice, education and policy. Despite this potential, there are challenges to the application of qualitative health research methods in nutrition and dietetics research. These include limited training and mentorship availability for the rigorous application of these methods, as well as misaligned goals between the traditional social science-based qualitative approaches and emerging applied nutrition science needs. Recognising these limitations, this review aims to provide guidance to the nutrition scientist conducting applied qualitative health research. Using nutrition and dietetic examples from the literature, this review defines qualitative health research and advances the Emphasis-Purposeful sample-Phenomenon of interest-Context (EPPiC) framework as a tool for constructing structured overarching research questions and introduces four qualitative health research designs (qualitative description, interpretive description, case study and focused ethnography) relevant to applied nutrition science. This includes guidance on defining the sample, identifying strategies for data collection, analytic techniques and data reporting.

Adequacy of Protein and Energy Intake in Critically Ill Adults Following Liberation From Mechanical Ventilation Is Dependent on Route of Nutrition Delivery.

BACKGROUND: Studies examining nutrition intake of critically ill patients following liberation from mechanical ventilation (LMV) are scarce. The objectives of this prospective, observational feasibility study were to quantify and assess protein and energy intake in hospitalized, critically ill patients following LMV, to determine barriers to optimal intake, and to report on the feasibility of recruiting and retaining patients into this study. METHODS: Adult patients requiring MV for >72 hours in a medical/surgical intensive care unit (ICU) were recruited. Protein and energy intakes were quantified up to 14 days following LMV. Patients also identified barriers to eating. RESULTS: Nineteen patients (mean age, 60 years [SD, 12 years]) were studied over 125 days. Over all study days, the median amounts of protein and energy consumed in comparison with amounts prescribed by dietitians were 46% (interquartile range [IQR], 26-100) and 71% (IQR, 38-100), respectively. When stratified by route of nutrition delivery, on days (n = 54) when patients consumed an oral diet as the sole nutrition source, median amounts of protein and energy consumed in comparison with those prescribed were only 27% (IQR, 15-41) and 47% (IQR, 29-66), respectively. The most frequently reported barriers to eating were poor appetite, early satiety, and taste changes. CONCLUSIONS: Protein and calorie intake is below prescribed amounts for patients whose enteral nutrition is discontinued and an oral diet prescribed as sole nutrition source following LMV. Acceptable strategies to enhance nutrition intake in post-ICU patients during the recovery stages of critical illness are needed.

Clinically Practical Approach for Screening of Low Muscularity Using Electronic Linear Measures on Computed Tomography Images in Critically Ill Patients.

BACKGROUND: Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass. METHODS: We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region. RESULTS: There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R2 = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively. CONCLUSIONS: A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically ill patients with low muscularity who are at risk for worse clinical outcomes.

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

BACKGROUND & AIMS: Lean body mass (LBM), quantified using computed tomography (CT), is a significant predictor of clinical outcomes in the critically ill. While CT analysis is precise and accurate in measuring body composition, it may not be practical or readily accessible to all patients in the intensive care unit (ICU). Here, we assessed the agreement between LBM measured by CT and four previously developed equations that predict LBM using variables (i.e. age, sex, weight, height) commonly recorded in the ICU. METHODS: LBM was calculated in 327 critically ill adults using CT scans, taken at ICU admission, and 4 predictive equations (E1-4) that were derived from non-critically adults since there are no ICU-specific equations. Agreement was assessed using paired t-tests, Pearson's correlation coefficients and Bland-Altman plots. RESULTS: Median LBM calculated by CT was 45 kg (IQR 37-53 kg) and was significantly different (p < 0.001) from E1 (52.5 kg; IQR: 42-61 kg), E2 (55 kg; IQR 45-64 kg), E3 (55 kg; IQR 44-64 kg), and E4 (54 kg; IQR 49-61 kg). Pearson correlation coefficients suggested moderate correlation (r = 0.739, 0.756, 0.732, and 0.680, p < 0.001, respectively). Each of the equations overestimated LBM (error ranged from 7.5 to 9.9 kg), compared with LBM calculated by CT, suggesting insufficient agreement. CONCLUSIONS: Our data indicates a large bias is present between the calculation of LBM by CT imaging and the predictive equations that have been compared here. This underscores the need for future research toward the development of ICU-specific equations that reliably estimate LBM in a practical and cost-effective manner.

Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients.

INTRODUCTION: As the population ages, the number of injured elderly is increasing. We sought to determine if low skeletal muscle mass adversely affected outcome in elderly patients following trauma. METHODS: Patients ≥ 65 years of age with an admission abdominal computed tomography scan and requiring intensive care unit (ICU) stay at a Level I trauma center in 2009-2010 were reviewed. Muscle cross-sectional area at the 3rd lumbar vertebra was quantified and muscle index, a normalized measure of muscle mass, was calculated and related to clinical parameters including ventilator-free days, ICU-free days, and mortality. Using previously established sex-specific, muscle index cut-points, patients were then categorized as sarcopenic or non-sarcopenic and differences in clinical outcomes between these two groups were also compared. We also examined muscle index as a continuous variable relative to the same clinical outcomes. RESULTS: There were 149 severely injured elderly patients (median age 79 years) enrolled in this study of which 71% were sarcopenic. Of the patients who were sarcopenic, 9% were underweight, 44% normal weight, and 47% overweight/obese as per body mass index (BMI) classifications. The overall mortality rate was 27% and univariate analysis demonstrated higher mortality among those who were sarcopenic (32% vs. 14%, P = 0.018). After controlling for age, sex, and injury severity, multiple logistic regression demonstrated that increased muscle index was significantly associated with decreased mortality (OR per unit muscle index = 0.93, 95% CI: 0.875-0.997, P = 0.025). In addition, multivariate linear regression showed that sarcopenia, but not muscle index, was associated with decreased ventilator-free (P = 0.004) and ICU-free days (P = 0.002). Neither BMI, serum albumin nor total adipose tissue on admission were indicative of survival, ventilator-free or ICU-free days. CONCLUSIONS: Sarcopenia is highly prevalent in the elderly population with traumatic injuries. Traditional measures of nutritional assessment, such as BMI and serum albumin, do not accurately predict outcome in the injured elderly. Sarcopenia, however, represents a potential new predictor for mortality, discharge disposition, and ICU utilization. Measurement of muscularity allows for the early identification of at-risk patients who may benefit from aggressive and multidisciplinary nutritional and rehabilitative strategies.

Consumption of caffeinated coffee and a high carbohydrate meal affects postprandial metabolism of a subsequent oral glucose tolerance test in young, healthy males.

Caffeine and caffeinated coffee (CC) elicit acute insulin insensitivity when ingested before a carbohydrate load. The effects of CC on glucose tolerance and insulin sensitivity when co-ingested with a high carbohydrate meal and on postprandial metabolism of a subsequent (second) carbohydrate load have not been studied. In a randomised, crossover design, ten healthy males ingested either CC (5 mg caffeine/kg body weight), decaffeinated coffee (DC) or water (W; equal volume) co-ingested with a high glycaemic index cereal followed 3 h later by a 75 g oral glucose tolerance test. After the initial meal, insulin area under the curve (AUC) and insulin sensitivity index did not differ between treatments, although glucose AUC for CC (107 (sem 18) mmol/l x 3 h) and DC (74 (sem 15) mmol/l x 3 h) was greater than W ( - 0.2 (sem 29) mmol/l x 3 h, P < 0.05). After the second carbohydrate load, insulin AUC for CC was 49 % and 57 % greater (P < 0.01) than for DC and W, respectively. Despite the greater insulin response, glucose AUC for CC (217 (sem 24) mmol/l x 2 h) was greater than both DC (126 (sem 11) mmol/l x 2 h, P = 0.01) and W (55 (sem 34) mmol/l x 2 h, P < 0.001). Insulin sensitivity index after the second meal was lower after CC (8.2 (sem 0.9)) compared with both DC (12.4 (sem 1.2), P < 0.01) and W (13.4 (sem 1.4), P < 0.001). Co-ingestion of CC with one meal resulted in insulin insensitivity during the postprandial phase of a second meal in the absence of further CC ingestion. Thus, CC may play a role in daily glycaemic management.

Caffeinated coffee consumption impairs blood glucose homeostasis in response to high and low glycemic index meals in healthy men.

BACKGROUND: The ingestion of caffeine (5 mg/kg body weight) and a 75-g oral glucose load has been shown to elicit an acute insulin-insensitive environment in healthy and obese individuals and in those with type 2 diabetes. OBJECTIVE: In this study we investigated whether a similar impairment in blood glucose management exists when coffee and foods typical of a Western diet were used in a similar protocol. DESIGN: Ten healthy men underwent 4 trials in a randomized order. They ingested caffeinated (5 mg/kg) coffee (CC) or the same volume of decaffeinated coffee (DC) followed 1 h later by either a high or low glycemic index (GI) cereal (providing 75 g of carbohydrate) mixed meal tolerance test. RESULTS: CC with the high GI meal resulted in 147%, 29%, and 40% greater areas under the curve for glucose (P < 0.001), insulin (NS), and C-peptide (P < 0.001), respectively, compared with the values for DC. Similarly, with the low GI treatment, CC elicited 216%, 44%, and 36% greater areas under the curve for glucose (P < 0.001), insulin (P < 0.01), and C-peptide (P < 0.01), respectively. Insulin sensitivity was significantly reduced (40%) with the high GI treatment after CC was ingested compared with DC; with the low GI treatment, CC ingestion resulted in a 29% decrease in insulin sensitivity, although this difference was not significant. CONCLUSION: The ingestion of CC with either a high or low GI meal significantly impairs acute blood glucose management and insulin sensitivity compared with ingestion of DC. Future investigations are warranted to determine whether CC is a risk factor for insulin resistance.