GLIM in nursing homes; practical implications

Insights into the associated risk factors of malnutrition among nursing home residents: A longitudinal study

BACKGROUND & AIMS

Malnutrition often remains undetected in older persons, leading to increased health problems and comorbidity, prolonged hospital stays and readmissions. In 2020, data from the interRAI Home Care (interRAI HC) instrument was used to determine malnutrition status according to some of the criteria of the Global Leadership Initiative on Malnutrition (GLIM). The interRAI HC instrument showed to be effective as a screening tool for the risk of malnutrition. The goal of the present study is to use the interRAI Long Term Care Facilities (interRAI LTCF) instrument for nursing home residents to identify factors related to older people's health that are significantly associated with the development of malnutrition.

METHODS

This study analyzes data collected in the period 2019-2023 from nursing home residents, 65 or older, with a follow-up period of 1 year. After applying the GLIM criteria to the available interRAI LTCF data, a cross-sectional sample a longitudinal sample were analyzed by means of bivariate analysis. Factors included in the bivariate analysis were based in previous studies and expert opinion. Unadjusted and adjusted regression models were built to explore associations between several potential risk factors and nutritional status.

RESULTS

The sample consisted of 5598 older people with a mean age of 83.98 ± 7.30 years old and 71.2 % being female. Most people needed extensive assistance with activities of daily living (70.9%) and had at least a mild cognitive impairment (63.9%). According to the GLIM definition using the interRAI items, 8.43% of the residents were malnourished and 4.67% of the residents became malnourished over the period of 1 year. The final adjusted logistic regression yielded significant odds ratios for seven determinants: age (O.R. 1.03; C.I.: 1.01; 1.04), depressive symptoms (O.R.: 1.32; 1.01; 1.73), assistance needed for walking (O.R. 1.49; C.I.: 1.13; 1.95), wandering behavior (O.R. 1.16; C.I.: 1.01; 1.33), falls (O.R. 1.17; C.I.: 1.02; 1.35), visual impairment (O.R. 1.22; C.I.: 1.05; 1.42) and diabetes (protective factor - O.R.: 0.67; C.I.: 0.46; 0.98).

CONCLUSIONS

The study showed the main risk factor for malnourishment in nursing home residents, such as age, depressions, assistance for walking, wandering and visual impairment. These significant factors can be used to identify people at risk of malnourishment. Periodically screening residents with the interRAI LTCF can help identify malnourished residents or residents at risk of malnourishment.

Meta-analysis of postoperative incision infection risk factors in colorectal cancer surgery

Objective

To evaluate the risk factors for postoperative incision infection in colorectal cancer, this meta-analysis aimed to identify key variables impacting infection incidence following colorectal cancer surgery.

Methods

Utilizing a meta-analytical approach, studies published from January 2015 to December 2022 were systematically collected and analyzed through the assessment of factors like body mass index, diabetes, albumin levels, malnutrition, and surgical duration.

Results

The meta-analysis of eleven high-quality studies revealed that elevated BMI, diabetes, low albumin levels, malnutrition, and extended surgical duration were associated with increased infection risk, while laparoscopic procedures showed potential for risk reduction.

Conclusions

This study underscores the significance of preoperative risk assessment and management in mitigating postoperative incision infections in colorectal cancer patients. The findings present actionable insights for clinicians to enhance patient prognoses and overall quality of life.

Challenges in identifying malnutrition in obesity; An overview of the state of the art and directions for future research

(Protein-energy) malnutrition in individuals living with obesity presents complex diagnostic challenges due to the distinctive physiological characteristics of obesity. This narrative review critically examines the identification of malnutrition within the population with obesity, distinguishing malnutrition in obesity from related conditions such as sarcopenic obesity. While noting some shared features, the review highlights key differences between these conditions. The review also highlights the limitations of current malnutrition screening tools, which are not designed for individuals living with obesity. These tools primarily rely on anthropometric measurements, neglecting (among others) nutrient intake assessment, which hinders accurate malnutrition detection. Additionally, this review discusses limitations in existing diagnostic criteria, including the Global Leadership Initiative on Malnutrition (GLIM) criteria, when applied to individuals living with obesity. Challenges include the identification of appropriate cut-off values for phenotypic criteria (unintentional weight loss, low body mass index and muscle mass) and aetiological criteria such as reduced food intake and inflammation for the population with obesity. Overall, this review emphasises the need for modified screening tools and diagnostic criteria to recognise and assess malnutrition in obesity, leading to improved clinical outcomes and overall wellbeing.

Bayesian diagnostic test evaluation and true prevalence estimation of malnutrition in gastric cancer patients

BACKGROUND & AIMS

Malnutrition is prevalent among gastric cancer (GC) patients, necessitating early assessment of nutritional status to guide monitoring and interventions for improved outcomes. We aim to evaluate the accuracy and prognostic capability of three nutritional tools in GC patients, providing insights for clinical implementation.

METHODS

The present study is an analysis of data from 1308 adult GC patients recruited in a multicenter from July 2013 to July 2018. Nutritional status was assessed using Nutritional Risk Screening 2002 (NRS-2002), Patient-Generated Subjective Global Assessment (PG-SGA) and Global Leadership Initiative on Malnutrition (GLIM) criteria. Bayesian latent class model (LCM) estimated the malnutrition prevalence of GC patients, sensitivity and specificity of nutritional tools. Cox regression model analyzed the relationship between nutritional status and overall survival (OS) in GC patients.

RESULTS

Among 1308 GC patients, NRS-2002, PG-SGA, and GLIM identified 50.46%, 76.76%, and 68.81% as positive, respectively. Bayesian LCM analysis revealed that PG-SGA had the highest sensitivity (0.96) for malnutrition assessment, followed by GLIM criteria (0.78) and NRS-2002 (0.65). Malnutrition or being at risk of malnutrition were identified as independent prognostic factors for OS. Use any of these tools improved survival prediction in TNM staging system.

CONCLUSION

PG-SGA is the most reliable tool for diagnosing malnutrition in GC patients, whereas NRS-2002 is suitable for nutritional screening in busy clinical practice. Given the lower sensitivity of NRS-2002, direct utilization of GLIM for nutritional assessment may be necessary. Each nutritional tool should be associated with a specific course of action, although further research is needed.

Prevalence of malnutrition among older adults in a population-based study - the HUNT Study

BACKGROUND

Malnutrition is common in older adults and is associated with increased morbidity and mortality rates.

AIM

The aim of the study is to describe the prevalence of malnutrition based on low BMI, involuntary weight loss, and reduced food intake, in a Norwegian population of community-dwelling older adults and older adults living in nursing homes.

METHODS

This population-based study is part of the fourth wave of the Trøndelag Health Study (HUNT4) and includes participants ≥70 years from the HUNT4 70+ cohort. The HUNT4 70+ cohort consist of 9930 (response rate 51.2%) participants. In the current study 8127 older people had complete dataset for inclusion in the analyses. Participants completed a self-report questionnaire and standardised interviews and clinical assessments at field stations, in participants' homes or at nursing homes. Malnutrition was defined using the following criteria: low BMI, involuntary weight loss and severely reduced food intake. The standardised prevalence of malnutrition was estimated using inverse probability weighting (IPW) with weights for sex, age and education of the total population in the catchment area of HUNT.

RESULTS

Of the 8127 included participants, 7671 (94.4%) met at field stations, 356 (4.4%) were examined in their home, and 100 (1.2%) in nursing homes. In total, 14.3% of the population were malnourished based on either low BMI, weight loss, or reduced food intake, of which low BMI was the most frequently fulfilled criterion. The prevalence of malnutrition was less common among men than among women (10.1 vs 18.0%, p < 0.001), also after adjustment for age (OR 0.53, 95% confidence interval (CI) 0.46-0.61). The prevalence increased gradually with increasing age and the regression analysis adjusted for sex showed that for each year increase in age the prevalence of malnutrition increased with 4.0% (OR 1.04, 95% CI 1.03-1.05). The prevalence was higher both among older adults examined in their homes (26.4%) and residents in nursing home (23.6%), as compared to community-dwelling older adults who met at field stations (13.5%).

CONCLUSION

The prevalence of malnutrition is high in the older population. Special attention on prevention and treatment of malnutrition should be given to older women, the oldest age groups, and care-dependent community-dwelling older adults and nursing home residents.

Malnutrition in older adults

Malnutrition is a highly prevalent condition in older adults, and poses a substantial burden on health, social, and aged-care systems. Older adults are vulnerable to malnutrition due to age-related physiological decline, reduced access to nutritious food, and comorbidity. Clinical guidelines recommend routine screening for malnutrition in all older adults, together with nutritional assessment and individually tailored nutritional support for older adults with a positive screening test. Nutritional support includes offering individualised nutritional advice and counselling; oral nutritional supplements; fortified foods; and enteral or parenteral nutrition as required. However, in clinical practice, the incorporation of nutritional guidelines is inadequate and low-value care is commonplace. This Review discusses the current evidence on identification and treatment of malnutrition in older adults, identifies gaps between evidence and practice in clinical care, and offers practical strategies to translate evidence-based knowledge into improved nutritional care. We also provide an overview of the prevalence, causes, and risk factors of malnutrition in older adults across health-care settings.

Systematic Review and Meta-Analysis: Malnutrition and In-Hospital Death in Adults Hospitalized with COVID-19

BACKGROUND

Malnutrition and increased malnutrition risk are frequently identified in hospitalized adults. The increase in hospitalization rates during the COVID-19 pandemic was accompanied by the documentation of adverse hospitalization outcomes in the presence of certain co-morbidities, including obesity and type 2 diabetes. It was not clear whether the presence of malnutrition increased in-hospital death in patients hospitalized with COVID-19.

OBJECTIVES

To estimate the effect of malnutrition on in-hospital mortality in adults hospitalized with COVID-19; and secondarily, to estimate the prevalence of malnutrition in adults hospitalized with malnutrition during the COVID-19 pandemic.

METHODS

EMBASE, MEDLINE, PubMed, Google Scholar, and Cochrane Collaboration databases were queried using the search terms malnutrition and COVID-19 and hospitalized adults and mortality. Studies were reviewed using the 14-question Quality Assessment Tool for Studies with Diverse Designs (QATSDD) (questions appropriate for quantitative studies). Author names; date of publication; country; sample size; malnutrition prevalence; malnutrition screening/diagnostic method; number of deaths in malnourished patients; and number of deaths in adequately nourished patients were extracted. Data were analyzed using MedCalc software v20.210 (Ostend, Belgium). The Q and I² tests were calculated; a forest plot was generated, and the pooled odds ratio (OR) with 95% confidence intervals (95%CI) were calculated using the random effects model.

RESULTS

Of the 90 studies identified, 12 were finally included in the meta-analysis. In the random effects model, malnutrition or increased malnutrition risk increased odds of in-hospital death by more than three-fold: OR 3.43 (95% CI 2.549-4.60), p < 0.001. The pooled prevalence estimate for malnutrition or increased malnutrition risk was 52.61% (95% CI 29.50-75.14%).

DISCUSSION AND CONCLUSIONS

It is clear that malnutrition is an ominous prognostic sign in patients hospitalized with COVID. This meta-analysis, which included studies from nine countries on four continents with data from 354,332 patients, is generalizable.

Diseases, Health-Related Problems, and the Incidence of Malnutrition in Long-Term Care Facilities

Certain diseases and malnutrition are known to co-occur in residents of long-term care facilities (LTCF). We assessed which diseases and health-related problems are associated with malnutrition at admission or with incident malnutrition during stays and how different definitions of malnutrition affect these associations. Data of Dutch LTCF residents were obtained from the InterRAI-LTCF instrument (2005-2020). We analyzed the association of diseases (diabetes, cancer, pressure ulcers, neurological, musculoskeletal, psychiatric, cardiac, infectious and pulmonary diseases) and health-related problems (aspiration, fever, peripheral edema, aphasia, pain, supervised/assisted eating, balance, psychiatric, GI tract, sleep, dental and locomotion problems) with malnutrition (recent weight loss (WL), low age-specific BMI (BMI), and ESPEN 2015 definition (ESPEN)) at admission (n = 3713), as well as with incident malnutrition during stay (n = 3836, median follow-up ~1 year). Malnutrition prevalence at admission ranged from 8.8% (WL) to 27.4% (BMI); incident malnutrition during stay ranged from 8.9% (ESPEN) to 13.8% (WL). At admission, most diseases (except cardiometabolic diseases) and health-related problems were associated with higher prevalence of malnutrition based on either criterion, but strongest with WL. This was also seen in the prospective analysis, but relationships were less strong compared to the cross-sectional analysis. A considerable number of diseases and health-related problems are associated with an increased prevalence of malnutrition at admission and incident malnutrition during stays in LTCFs. At admission, low BMI is a good indicator of malnutrition; during stays, we advise use of WL.