Serum albumin and prealbumin, well‐known visceral proteins, have traditionally been considered useful biochemical laboratory values in a nutrition assessment. However, recent literature disputes this ...contention. The aim of this document is to clarify that these proteins characterize inflammation rather than describe nutrition status or protein‐energy malnutrition. Both critical illness and chronic illness are characterized by inflammation and, as such, hepatic reprioritization of protein synthesis occurs, resulting in lower serum concentrations of albumin and prealbumin. In addition, the redistribution of serum proteins occurs because of an increase in capillary permeability. There is an association between inflammation and malnutrition, however, not between malnutrition and visceral‐protein levels. These proteins correlate well with patients’ risk for adverse outcomes rather than with protein‐energy malnutrition. Therefore, serum albumin and prealbumin should not serve as proxy measures of total body protein or total muscle mass and should not be used as nutrition markers. This paper has been approved by the American Society for Parenteral and Enteral Nutrition Board of Directors.
Critical illness is a hypercatabolic state. It has been hypothesized that timely and adequate nutrition support may optimize the host response and thereby minimize nutritionally related complications ...while improving overall outcome. Any illness in due course can lead to a malnourished state—critical illness can worsen this state as patients may become immunocompromised and unable to mount an adequate inflammatory response and therefore susceptible to poor outcomes. Data indicate that prevalence of malnutrition in the ICU ranges from 38% to 78% and is independently associated with poor outcomes. Hence, exploring the role of nutrition as a way to mitigate critical illness is important. In this review, the basic pathophysiology of critical illness and how it alters carbohydrate, protein, and fat metabolism are discussed. This is followed by a discussion of malnutrition and how it affects patient and hospital outcomes. Finally, a summary of the available evidence regarding nutrition support and its impact on outcomes are provided. This review is not intended to provide practice‐based guidelines; instead, it intends to highlight available data on the role of nutrition support in critically ill patients.
OBJECTIVES:The association between nutritional status and mortality in critically ill patients is unclear based on the current literature. To clarify this relation, we analyzed the association ...between nutrition and mortality in a large population of critically ill patients and hypothesized that mortality would be impacted by nutritional status.
DESIGN:Retrospective observational study.
SETTING:Single academic medical center.
PATIENTSSix thousand five hundred eighteen adults treated in medical and surgical ICUs between 2004 and 2011.
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:All cohort patients received a formal, in-person, standardized evaluation by a registered dietitian. The exposure of interest, malnutrition, was categorized as nonspecific malnutrition, protein-energy malnutrition, or well nourished and determined by data related to anthropometric measurements, biochemical indicators, clinical signs of malnutrition, malnutrition risk factors, and metabolic stress. The primary outcome was all-cause 30-day mortality determined by the Social Security Death Master File. Associations between nutrition groups and mortality were estimated by bivariable and multivariable logistic regression models. Adjusted odds ratios were estimated with inclusion of covariate terms thought to plausibly interact with both nutrition status and mortality. We used propensity score matching on baseline characteristics to reduce residual confounding of the nutrition status category assignment. In the cohort, nonspecific malnutrition was present in 56%, protein-energy malnutrition was present in 12%, and 32% were well nourished. The 30-day and 90-day mortality rates for the cohort were 19.1% and 26.6%, respectively. Nutritional status is a significant predictor of 30-day mortality following adjustment for age, gender, race, medical versus surgical patient type, Deyo-Charlson index, acute organ failure, vasopressor use, and sepsisnonspecific malnutrition 30-day mortality odds ratio, 1.17 (95% CI, 1.01–1.37); protein-energy malnutrition 30-day mortality odds ratio, 2.10 (95% CI, 1.70–2.59), all relative to patients without malnutrition. In the matched cohort, the adjusted odds of 30-day mortality in the group of propensity score-matched patients with protein-energy malnutrition was two-fold greater than that of patients without malnutrition.
CONCLUSION:In a large population of critically ill adults, an association exists between nutrition status and mortality.
Key approaches to diagnosing malnutrition in adults Malone, Ainsley; Mogensen, Kris M.
Nutrition in clinical practice,
February 2022, 2022-Feb, 2022-02-00, 20220201, Letnik:
37, Številka:
1
Journal Article
Recenzirano
Nutrition assessment is used to describe nutrition status–related nutrition problems and their causes, one of which includes malnutrition. Four malnutrition diagnostic tools are currently in use ...today in adults: Subjective Global Assessment, the Mini Nutritional Assessment, the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition malnutrition consensus characteristics, and the Global Leadership Initiative on Malnutrition criteria. The aim of this article is to provide sufficient background of these methodologies to assist clinicians in choosing their approach in diagnosing malnutrition. There is substantial overlap between the criteria included in these malnutrition diagnostic approaches. A desired goal is to identify a core data set in order to evaluate malnutrition prevalence globally and to assess the impact of nutrition interventions on nutrition and clinical outcomes.
INTRODUCTION:The association between obesity and mortality in critically ill patients is unclear based on the current literature. To clarify this relationship, we analyzed the association between ...obesity and mortality in a large population of critically ill patients and hypothesized that mortality would be impacted by nutritional status.
METHODS:We performed a single-center observational study of 6,518 adult patients treated in medical and surgical ICUs between 2004 and 2011. All patients received a formal, in-person, and standardized evaluation by a registered dietitian. Body mass index was determined at the time of dietitian consultation from the estimated dry weight or hospital admission weight and categorized a priori as less than 18.5 kg/m (underweight), 18.5–24.9 kg/m (normal/referent), 25–29.9 kg/m (overweight), 30–39.9 kg/m (obesity class I and II), and more than or equal to 40.0 kg/m (obesity class III). Malnutrition diagnoses were categorized as nonspecific malnutrition, protein-energy malnutrition, or well nourished. The primary outcome was all-cause 30-day mortality determined by the Social Security Death Master File. Associations between body mass index groups and mortality were estimated by bivariable and multivariable logistic regression models. Adjusted odds ratios were estimated with inclusion of covariate terms thought to plausibly interact with both body mass index and mortality. We utilized propensity score matching on baseline characteristics and nutrition status to reduce residual confounding of the body mass index category assignment.
RESULTS:In the cohort, 5% were underweight, 36% were normal weight, 31% were overweight, 23% had class I/II obesity, and 5% had class III obesity. Nonspecific malnutrition was present in 56%, protein-energy malnutrition was present in 12%, and 32% were well nourished. The 30-day and 90-day mortality rate for the cohort was 19.1 and 26.6%, respectively. Obesity is a significant predictor of improved 30-day mortality following adjustment for age, gender, race, medical versus surgical patient type, Deyo-Charlson index, acute organ failure, vasopressor use, and sepsisunderweight odds ratio 30-day mortality is 1.09 (95% CI, 0.80–1.48), overweight 30-day mortality odds ratio is 0.93 (95% CI, 0.80–1.09), class I/II obesity 30-day mortality odds ratio is 0.80 (95% CI, 0.67–0.96), and class III obesity 30-day mortality odds ratio is 0.69 (95% CI, 0.49–0.97), all relative to patients with body mass index 18.5–24.9 kg/m. Importantly, there is confounding of the obesity-mortality association on the basis of malnutrition. Adjustment for only nutrition status attenuates the obesity–30-day mortality associationunderweight odds ratio is 0.74 (95% CI, 0.54–1.00), overweight odds ratio is 1.05 (95% CI, 0.90–1.23), class I/II obesity odds ratio is 0.96 (95% CI, 0.81–1.15), and class III obesity odds ratio is 0.81 (95% CI, 0.59–1.12), all relative to patients with body mass index 18.5–24.9 kg/m. In a subset of patients with body mass index more than or equal to 30.0 kg/m (n = 1,799), those with either nonspecific or protein-energy malnutrition have increased mortality relative to well-nourished patients with body mass index more than or equal to 30.0 kg/modds ratio of 90-day mortality is 1.67 (95% CI, 1.29–2.15; p < 0.0001), fully adjusted. In a cohort of propensity score matched patients (n = 3,554), the body mass index–mortality association was not statistically significant, likely from matching on nutrition status.
CONCLUSIONS:In a large population of critically ill adults, the association between improved mortality and obesity is confounded by malnutrition status. Critically ill obese patients with malnutrition have worse outcomes than obese patients without malnutrition.
Background
Malnutrition risk estimates vary greatly, and no robust data on the association between food intake and outcomes exist for hospitals in the United States (U.S.). This study aimed to ...determine the prevalence of malnutrition risk and to evaluate the impact of food intake on mortality using the nutritionDay in the U.S. dataset.
Methods
This study analyzed data from 2009 to 2015 for all adult patients from participating hospitals. Prevalence of malnutrition risk was determined by mapping self‐reported nutritionDay survey questions to the Malnutrition Screening Tool (MST). Fine and Gray competing‐risk analysis with clustering was used to evaluate the impact of nutrition risk and food intake on patients’ 30‐day in‐hospital mortality, while controlling for age, mobility, and other disease‐related factors.
Results
Analysis included data from 9959 adult patients from 601 wards. The overall prevalence of malnutrition risk (MST score ≥2) was 32.7%. On nutritionDay, 32.1% of patients ate a quarter of their meal or less. Hospital mortality hazard ratio was 3.24 (95% CI: 1.73, 6.07; P‐value < 0.001) for patients eating a quarter compared with those who ate all their meal and increased to 5.99 (95% CI: 3.03, 11.84; P‐value < 0.0001) for patients eating nothing despite being allowed to eat.
Conclusion
This study provides the most robust estimate of malnutrition risk in U.S. hospitalized patients to date, finding that approximately 1 in 3 are at risk. Additionally, patients who have diminished meal intake experience increased mortality risk. These results highlight the ongoing issue of malnutrition in the hospital setting.
Nutrition support is a therapy that crosses all ages, diseases, and conditions as health care practitioners strive to meet the nutritional requirements of individuals who are unable to meet ...nutritional and/or hydration needs with oral intake alone. Registered dietitian nutritionists (RDNs), as integral members of the nutrition support team provide needed information, such as identification of malnutrition risk, macro- and micronutrient requirements, and type of nutrition support therapy (eg, enteral or parenteral), including the route (eg, nasogastric vs nasojejunal or tunneled catheter vs port). The Dietitians in Nutrition Support Dietetic Practice Group, American Society for Parenteral and Enteral Nutrition, along with the Academy of Nutrition and Dietetics Quality Management Committee, have updated the Standards of Practice (SOP) and Standards of Professional Performance (SOPP) for RDNs working in nutrition support. The SOP and SOPP for RDNs in Nutrition Support provide indicators that describe the following 3 levels of practice: competent, proficient, and expert. The SOP uses the Nutrition Care Process and clinical workflow elements for delivering patient/client care. The SOPP describes the 6 domains that focus on professional performance. Specific indicators outlined in the SOP and SOPP depict how these standards apply to practice. The SOP and SOPP are complementary resources for RDNs and are intended to be used as a self-evaluation tool for assuring competent practice in nutrition support and for determining potential education and training needs for advancement to a higher practice level in a variety of settings.
Background
This initiative aims to build a global consensus around core diagnostic criteria for malnutrition in adults in clinical settings.
Methods
The Global Leadership Initiative on Malnutrition ...(GLIM) was convened by several of the major global clinical nutrition societies. Empirical consensus was reached through a series of face‐to‐face meetings, telephone conferences, and e‐mail communications.
Results
A 2‐step approach for the malnutrition diagnosis was selected, that is, first screening to identify at risk status by the use of any validated screening tool, and second, assessment for diagnosis and grading the severity of malnutrition. The malnutrition criteria for consideration were retrieved from existing approaches for screening and assessment. Potential criteria were subjected to a ballot among GLIM participants that selected 3 phenotypic criteria (non‐volitional weight loss, low body mass index, and reduced muscle mass) and 2 etiologic criteria (reduced food intake or assimilation, and inflammation or disease burden). To diagnose malnutrition at least 1 phenotypic criterion and 1 etiologic criterion should be present. Phenotypic metrics for grading severity are proposed. It is recommended that the etiologic criteria be used to guide intervention and anticipated outcomes. The recommended approach supports classification of malnutrition into four etiology‐related diagnosis categories.
Conclusions
A consensus scheme for diagnosing malnutrition in adults in clinical settings on a global scale is proposed. Next steps are to secure endorsements from leading nutrition professional societies, to identify overlaps with syndromes like cachexia and sarcopenia, and to promote dissemination, validation studies, and feedback. The construct should be re‐considered every 3–5 years.
Malnutrition remains a significant problem in patients with acute or chronic illnesses. Nutrition assessment is an important component in detecting malnutrition; but not always performed using a ...standardized tool. This survey on nutrition assessment evaluates current clinical practices on the assessment, diagnosis, and treatment of malnutrition.
This 2022 survey of US-based nutrition clinicians collected data on assessment parameters used in hospitals, long-term care facilities, and the home care setting.
A total of 686 surveys were available for analysis. Ninety-seven percent of adult and 91% of pediatric responding clinicians indicated that a dietitian completed the assessment. Parameters used most frequently among adult clinician respondents included nutrient intake, current weight, and weight history, those used by pediatric clinician respondents included nutrient intake, weight-for-age z score, and weight-for-length/height z score. Eighty-nine percent of adult clinicians in all care settings and 85% of pediatric clinicians use the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Indicators of Malnutrition (AAIM). Respondents reported malnutrition rates of 32%-40% for adults and 4%-30% for pediatric patients, depending on the setting. Appropriate interventions for those with malnutrition (as perceived by the survey respondents) were ordered 70% of the time.
This survey demonstrated significant use of the AAIM by both adult and pediatric clinicians across care settings. Reported malnutrition rates are consistent with others published in the literature. The authors suggest that quality improvement efforts should focus on the 30% of patients with malnutrition but without a reported appropriate nutrition intervention.
PDF
