Body mass index (BMI), body composition and mortality of nursing home elderly residents

Kimyagarov, S.; Klid, R.; Levenkrohn, Shalom; Fleissig, Y.; Kopel, B.; Arad, Marina; Adunsky, Abraham

doi:10.1016/j.archger.2009.10.013

Cited by 50 publications

(28 citation statements)

References 21 publications

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“…Kimyagarov S et al, in the paper cited before (21), found that 1-year mortality was associated in particular with low levels of BMi, fat-free body mass, skeletal muscle mass (oR respectively 1.73, 2.42 and 2,55 respectively). a weight loss during the 6-months period was associated with a nearly two-fold increase in the likelihood of dying (adj-RR: 1.95, 95% ci 1.43 to 2.66) nursing home residents at high nutritional risk to determine (42).…”

Section: Discussionmentioning

confidence: 95%

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Predicting the outcome of long-term care by clinical and functional indices: The role of nutritional status

Donini

Felice²,

Savina³

et al. 2011

The Journal of nutrition, health and aging

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The univariate analysis found that some variables were significantly correlated with the outcome: comorbidity, ADL, cognitive status, pressure sores, albumin, transferrin, CRP, mucoprotein, cholesterol, cholinesterase, MAMC and MNA. The predictive value of the block model of the logistic regression analysis was 77.9% (specificity = 85.3%, sensitivity = 63.9%). With the forward stepwise analysis only MNA, cholinesterase, CRP and mucoprotein were considered in the final model. In this case the predictive value of the model was 79.3% (specificity = 84.6%, sensitivity = 69.46%).

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Section: Discussionmentioning

confidence: 95%

“…in another recent paper (21), the authors found that the 1-year mortality rate was 29.3% in 82 disabled institutionalized elderly patients. in this case mortality was correlated with malnutrition (low levels of BMi and fat-free mass).…”

Section: Discussionmentioning

confidence: 96%

Predicting the outcome of long-term care by clinical and functional indices: The role of nutritional status

Donini

Felice²,

Savina³

et al. 2011

The Journal of nutrition, health and aging

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“…In several clinical situations, i.e. hospital admission, chronic obstructive pulmonary disease (COPD) [21][22][23] , dialysis [24][25][26] , chronic heart failure [27] , amyotrophic lateral sclerosis [28] , cancer [5,29] , liver transplantation [30] , nursing home residence [31] , and Alzheimer's disease [32] , changes in body compartments are detected with the techniques of body composition evaluation. At hospital admission, body composition evaluation could be used for the detection of FFM loss and undernutrition.…”

Section: Body Composition Techniques For Ffm Measurementmentioning

confidence: 99%

The Evaluation of Body Composition: A Useful Tool for Clinical Practice

Thibault

Pichard²

2011

Ann Nutr Metab

151

121

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Undernutrition is insufficiently detected in in- and outpatients, and this is likely to worsen during the next decades. The increased prevalence of obesity together with chronic illnesses associated with fat-free mass (FFM) loss will result in an increased prevalence of sarcopenic obesity. In patients with sarcopenic obesity, weight loss and the body mass index lack accuracy to detect FFM loss. FFM loss is related to increasing mortality, worse clinical outcomes, and impaired quality of life. In sarcopenic obesity and chronic diseases, body composition measurement with dual-energy X-ray absorptiometry, bioelectrical impedance analysis, or computerized tomography quantifies the loss of FFM. It allows tailored nutritional support and disease-specific therapy and reduces the risk of drug toxicity. Body composition evaluation should be integrated into routine clinical practice for the initial assessment and sequential follow-up of nutritional status. It could allow objective, systematic, and early screening of undernutrition and promote the rational and early initiation of optimal nutritional support, thereby contributing to reducing malnutrition-induced morbidity, mortality, worsening of the quality of life, and global health care costs.

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“…This is the reason why more sensitive and specific tools that include weight loss as the key indicator for evaluating malnutrition must be used. The advisability of using BMI as an appropriate indicator of nutritional status has been widely discussed, especially in patients with heart failure (16) or elderly residents of nursing homes (17) , and some recent studies have shown no associations between BMI and mortality risk in cancer or respiratory disease patients (6) . However, in some specific cases (nursing home residents), BMI showed superiority among three parameters considered to be nutritional predictors (BMI, Mini Nutritional Assessment score and serum albumin levels) (10) and might be used as a simple screening tool, although its poor sensitivity must still be carefully considered.…”

Section: Discussionmentioning

confidence: 99%

Ten-year all-cause mortality in hospitalized non-surgical patients based on nutritional status screening

Kissova¹,

Rosenberger²,

Goboova³

et al. 2015

Public Health Nutr.

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Objective: Malnutrition is common in patients admitted to hospital due to acute illness and contributes to negative patient outcomes. In Slovakia there is a lack of relevant data on malnutrition in hospitalized patients, particularly based on chronic co-morbidity and survival. The aim of the present study was to explore the prevalence of malnutrition in hospitalized chronic patients, its relationship to co-morbidity and its impact on 10-year survival. Design: Retrospective cohort study. Setting: Nutritional status was estimated by Subjective Global Assessment (SGA), BMI and serum albumin level. Survival was assessed from the National Insurance Registry over a 10-year period. The association between nutritional status measured by SGA and 10-year survival controlling for age, gender, BMI and serum albumin was analysed using Cox regression. Subjects: Data were taken from the medical records of 202 consecutively admitted chronic patients. Results: Median age was 63·5 years; 55·4 % were males; median BMI was 25·9 kg/m 2 ; median serum albumin level was 39·0 g/l. Based on SGA evaluation, 38·1 % did not have sufficient nutritional status (SGA classification B and C). Malnutrition was more common in patients who were older (P = 0·023), with lower BMI (P < 0·001), who had gastrointestinal (P = 0·049) and oncologic co-morbidity (P = 0·021) and lower albumin level (P = 0·049). In-hospital mortality was 3 %, but during the following 10 years 52 % died. Cox regression analysis controlling for age, gender, BMI and serum albumin showed that SGA was an independent predictor of death (hazard ratio = 1·55; 95 % CI 1·04, 2·32; P = 0·031). Conclusions: SGA is a simple screening tool that can be routinely used in hospitalized Slovak medical patients to predict the risk of death. Improving patient nutrition could thus reduce mortality.

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Body mass index (BMI), body composition and mortality of nursing home elderly residents

Cited by 50 publications

References 21 publications

Predicting the outcome of long-term care by clinical and functional indices: The role of nutritional status

Predicting the outcome of long-term care by clinical and functional indices: The role of nutritional status

The Evaluation of Body Composition: A Useful Tool for Clinical Practice

Ten-year all-cause mortality in hospitalized non-surgical patients based on nutritional status screening

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