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Nursing Article review Assignment on Nutrition feeding and eating

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Nursing Article review Assignment on Nutrition feeding and eating

Research Nutritional screening tools and anthropometric measures associate with hospital discharge outcomes in older people Elsa Dent Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia Ian Chapman Royal Adelaide Hospital; and Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia Cynthia Piantadosi Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia Renuka Visvanathan The Queen Elizabeth Hospital; and Discipline of Medicine, University of Adelaide, Adelaide, South Australia, Australia Aim: To examine the association of nutritional screening tools (NSTs) and anthropometric measures with hospital outcomes in older people. Methods: In 172 patients aged ≥70 years admitted to a Geriatric Evaluation Management Unit (GEMU), nutritional status was measured using the Mini-Nutritional Assessment (MNA), MNA-short form (MNA-SF), Geriatric Nutritional Risk Index (GNRI), Simplified Nutritional Appetite Questionnaire, calf circumference (CC), mid-arm circumference (MAC) and BMI. Results: Malnutrition according to the MNA occurred in 53 (31%) patients. Functional change was associated with GNRI (Beta coefficient (β), 95% CI = 0.17, 0.001–0.33) and CC (β, 95% CI = 0.17, 0.01–0.33); GEMU length of stay was associated with MNA-SF-BMI (β, 95% CI = −0.02, −0.003 to −0.004) and MNA-SF-CC (β, 95% CI = −0.02, −0.003 to −0.001). MAC was associated with discharge to higher level of care (OR, 95% CI = 0.88, 0.81–0.96). Conclusion: In hospitalised older people, admission NSTs and anthropometric measures associate with discharge outcomes. Key words: aged, geriatric assessment/methods, hospitalisation, nutritional assessment. Introduction Malnutrition is common in older people and associated with functional decline and increased mortality, yet it often goes unrecognised in the hospital setting [1]. Screening of hospital patients has therefore been recommended to identity those malnourished or at risk of malnourishment [1]. Nutritional screening tools (NSTs) offer a fast and easy way to identify these at risk individuals, allowing referral for further nutritional evaluation and management [2,3]. Several NSTs have been validated for use in hospitalised older people, including the Mini-Nutritional Assessment (MNA) [4], the MNA-short form (MNA-SF) [5] and the Geriatric Nutritional Risk Index (GNRI) [6]. The Simplified Nutritional Appetite Questionnaire (SNAQ) has also been validated in older people to identify those at risk of weight loss [7]. Considerable work has been done to identify which NST is the best in identifying malnutrition [8]. Only limited research exists, however, in determining which NST best associates with adverse clinical outcomes in hospitalised older patients [6]. An association with adverse outcomes is important for validation of a NST in the clinical setting, particularly when compared to other screening tools [9]. To our knowledge, only three prospective studies have looked at NSTs and functional decline over hospitalisation [10–12], with mixed results, and none compared NSTs to each other. Additionally, anthropometric measures, such as body mass index (BMI), calf circumference (CC) and midarm circumference (MAC), have been reported to be associated with functional status [13], but their ability to predict in-hospital outcomes compared to NSTs is unknown. The aim of this study was to compare several NSTs and anthropometric measures in their relationships to discharge outcomes, including change in function, length of stay (LOS), and discharge to higher level of care, among patients in a Geriatric Evaluation and Management Unit (GEMU). Methods Setting and sample Consecutively admitted patients aged 70 years or older admitted to the GEMU at The Queen Elizabeth Hospital, Adelaide, Australia (TQEH), from October 22, 2010 to December 23, 2011 were recruited within the first 3 days of GEMU admission. Informed consent was obtained from patients, or in cases of low cognition, from a family member. The study was approved by the TQEH Human Research Ethics Committee (TQEH) and adhered to the Australian Code for the Responsible Conduct of Research. The GEMU at TQEH is a higher acuity, specialised geriatric unit providing comprehensive geriatric assessment and multidisciplinary management, including rehabilitation where Correspondence to: Ms Elsa Dent, Discipline of Medicine, University of Adelaide. Email: bs_bs_banner DOI: 10.1111/ajag.12130 Australasian Journal on Ageing, Vol 34 No 1 March 2015, E1–E6 E1 © 2014 ACOTA appropriate. The majority of patients are identified by the geriatric service in the Acute Medical Unit (short stay <72 hours), where they have been admitted for management of an acute medical illness, and then transferred to the GEMU. The GEMU aims to undertake comprehensive geriatric assessment and management, to maximise functional independence and discharge patients home where possible. Nutrition management is a key focus. Assessments All assessments were completed within 72 hours of a patient’s GEMU admission by the same researcher (ED) Function was assessed using Barthel’s Index (BI) of activities of daily living (ADL), with a total score of 100 indicating independence in all ADLs [14]. Interview data collected included health and lifestyle questions. Information obtained from patient medical records included nutritional blood markers (C-reactive protein (CRP), lymphocytes, haemaglobin, albumin, cholesterol (total), high-density lipoprotein, low-density lipoprotein), micronutrients (iron, folate, vitamin B12, vitamin D), medications, Geriatric Depression Scale (GDS-15) [15], cognition using the Mini Mental State Examination (MMSE) [16], and admission diagnosis. Admission diagnosis was placed into one of five categories: chronic condition, infection, injury or musculoskeletal condition, non-musculoskeletal condition and unclassified [17]. Charlson’s comorbidity index (CCI) was derived from patient medical records. Anthropometric measures Measurements were performed on the right hand side of the body where possible to standardise measurements. CC was measured as the widest calf girth, and MAC measured as the circumference of the upper arm, midway between the acromion process and the lateral epicondyle of the elbow. Each measurement was performed once (to the nearest 0.1 cm) per patient. Height was measured with a stadiometer to the nearest 0.5 cm for mobile patients and self-reported height recorded for non-mobile patients. The same calibrated weight chair was used to weigh all mobile patients to the nearest 0.01 kg. For immobile patients, a weigh sling was used. BMI was computed. MNA The MNA was designed for use in older people and is widely used [4]. It contains 18 components in four areas (anthropometry dietary questions, subjective questions and overall assessment), and classifies people as ‘malnourished’ (scores 0–23), ‘at risk of malnutrition’ (scores 17–23.5) or ‘well nourished’ (scores 24–30) [4]. A second version of the MNA (MNA-II), which excludes BMI, doubles CC score and triples MAC score, was also used [18]. The MNA-II was recently validated in Taiwanese older people and also has a total score of 30 [18]. For the purposes of our study, standard CC and MAC cut-offs were used, not Taiwanese specific cut-offs. A shorter version of the MNA, the MNA-SF was also used [5]. It comprises six questions from the MNA and can be used with either BMI or CC measures [5], termed the MNASF-BMI and MNA-SF-CC, respectively, in this study. The MNA-SF classifies individuals as ‘malnourished’ (scores 0–7), ‘at risk of malnutrition’ (scores 8–11) and ‘well nourished’ (scores 8–12) [5] and retains the accuracy of the full MNA in classifying nutritional status [2]. The geriatric nutritional risk index The GNRI is both a nutrition-related risk index and NST for use in older people [19]. GNRI albumin g L weight WLo = × [ ] 1 489 41 7 . . ( ) + × [ ] ( ) With WLo = Ideal Weight, using Lorentz equations as described by Boulianne et al. [6]: Men: WLo = H − 100 − [(H − 150)/4] Women: WLo = H − 100 − [(H − 150)/2.5] With H = height in cm GNRI categories are: major risk (scores < 82) moderate risk (scores < 92), low risk (scores 92 to ≤98) and no risk (>9) [7]. SNAQ The SNAQ is a weight loss prediction tool designed for use in older people, with scores ≤14/20 indicating significant risk of at least 5% weight loss within 6 months [7]. It comprises four questions on appetite and food intake. Discharge outcomes Discharge function was assessed using BI score. Functional decline and improvement were defined as a decrease or increase, respectively, in BI. Other discharge outcomes were GEMU LOS and discharge to a higher level of care. A higher level of care was defined as a move to a destination other than home, which included subacute care post-GEMU or move to an address that was not the patient’s pre-admission address. Patients who died during hospitalisation were classified as discharged to higher level care and were excluded from discharge function and LOS analyses. Statistical analyses Normally distributed variables were expressed as means (standard deviation (SD)) and non-normally distributed variables as medians (range). Categorical variables were expressed as number and percentage. Paired t-tests were performed to determine the difference between admission and discharge function. The association between NSTs, anthropometric variables, nutritional biomarkers and functional measures at admission were analysed using Spearman’s rank correlations. Dent E, Chapman I, Piantadosi C et al . E2 Australasian Journal on Ageing, Vol 34 No 1 March 2015, E1–E6 © 2014 ACOTA LOS and functional outcomes were analysed as continuous variables. Their association with each NST was determined using multiple regression models. Residual plots of the regression models were assessed visually for normality, constant variance and outliers. LOS was non-normally distributed and subsequently log transformed. For functional change, BI at discharge controlling for BI at admission was used. Associations between each NST and ‘move to higher level of care’ were assessed using logistic regression analyses. All regression models controlled for confounding variables found in previous research to be associated with hospital outcomes: age, gender, CCI, MMSE, admission BI and living alone. Because of limited statistical degrees of freedom, no further confounding variables could be included in the logistic regression analyses (move to higher level care). The multiple regression models (LOS and functional change) additionally controlled for GDS and CRP (a measure of inflammation). Variables in each regression model were checked for multicollinearity. All results were analysed using PASW Statistics 18 software, with statistical significance set at P < 0.05. Results During the study period, 427 new patients aged ≥70 years were admitted to the GEMU. Patients were excluded from the study for the following reasons: dementia or unresolved delirium within 72 hours of admission without a proxy (n = 77), did not speak English (with no proxy; n = 67), treating physician advised against patient inclusion (medically unwell elder abuse, physically aggressive: n = 33), infectious (n = 11), missed by researcher (n = 4) and did not wish to participate (n = 63). Table 1 shows baseline characteristics of patients recruited (n = 172). The mean (SD) age of patients was 85.2 (6.4 years). Weight, CC and MAC measures were performed for all patients. Height was self-reported in 71 (41%) patients due to immobility. The median GEMU LOS for surviving patients was 12 days, with a median of 4 days in hospital before GEMU admission. Mean function, as assessed by BI, improved from 59 (21) on admission to 68 (19) at discharge (P < 0.001). 129 (75%) patients had functional improvement, with 76 patients (44%) showing an improvement in BI of more than 10%. Twenty-eight (16%) had no change in function and 15 (8%) had functional decline. Eighty (47%) of patients were discharged to a location other than home and 7 died during hospitalisation. Thirty-four per cent of patients had low BMI values (<22 kg/m2 ). Malnutrition according to the MNA, MNA-SF and GNRI occurred in 53 (31%), 77 (45%) and 83 (48%) patients, respectively. Risk of malnutrition, according to the MNA, MNA-SF and GNRI, occurred in an additional 84 (49%), 67 (39%) and 24 (14%), respectively. Using the SNAQ, 109 (63%) patients were classified at risk of weight loss. Correlations of NSTs scores, anthropometric variables, nutritional biomarkers, micronutrients and functional measures at admission are shown in Table 2. All NSTs results were significantly correlated with each other with the exception of GNRI and SNAQ. All NSTs were correlated with anthropometric measures with the exception of SNAQ. Scores on all long and short versions of the MNA were correlated with admission function, grip strength and MMSE. SNAQ was associated with grip strength and GDS. GNRI was not associated with any functional measure. Table 3 shows the results of each regression model of individual NSTs against the outcome measures. MNA-II, MNA-SF-BMI and MNA-SF-CC scores were significantly associated with length of hospital stay. Both GNRI and CC were significantly associated with functional change. MAC was the only variable to show an association with discharge to higher level care; individuals with lower MAC measurements were 1.14 times more likely (1/0.88) to be discharged to higher level care. MNA, BMI and SNAQ showed no association with outcome variables. The coefficient of determination for each regression model was low (maximum value of 30.5%). Table 1: Descriptive characteristics of patients on admission (n = 172) Variable n (%) Gender (female) 129 (72) Age group (years) 70–79 31 (18) 80–89 100 (58) 90–101 41 (24) BMI category (kg/m2 ) <22 58 (34) 22–30 75 (44) >30 39 (23) Calf circumference (cm) 32 (5)† Mid-arm circumference (cm) 26 (5)† Admission function (BI) 59 (21)† Charlson’s comorbidity index 3 (range 0–12)‡ Low cognition (MMSE < 24) 74 (43) Lives alone 97 (56) Depressive risk (GDS score >5) 61 (40) Polypharmacy (≥6 medications) 131 (76) Use of dentures 84 (49) Problems with food supply Cooking 96 (56) Chewing or swallowing 58 (34) Cutting 54 (31) Transportation to shops 37 (22) Financial constraints 18 (11) Primary GEMU admission diagnosis§ Chronic condition 71 (41) Infection 52 (30) Injury or musculoskeletal condition 28 (16) Non-musculoskeletal symptoms 6 (4) Unclassified 15 (9) †Mean (SD). ‡Median (range). §Classifications based on Hastings et al., 2010 [17]. BI, Barthel Index; GDS, Geriatric Depression Scale −15; GEMU, Geriatric Evaluation and Management Unit; MMSe, Mini Mental State Examination. Nutritional screening tools and hospital outcomes Australasian Journal on Ageing, Vol 34 No 1 March 2015, E1–E6 E3 © 2014 ACOTA Discussion To our knowledge, this is the first study to evaluate the use of NSTs and anthropometric measures as predictors of discharge outcomes for older people in a GEMU. Malnutrition rates were high: 31, 45 and 48% for the MNA, MNA-SF and GNRI, respectively, comparable to those in other studies of hospitalised older people [2,6,20]. There were also a high number of people at risk of malnutrition: 49, 39 and 14% for the MNA, MNA-SF and GNRI, respectively. The large difference in prevalence rates of malnutrition and risk of malnutrition reflects the different classification systems of the NSTs included in the present study. Management of patients in the GEMU involves striking a balance between providing adequate time for functional rehabilitation and keeping LOS in hospital as short as Table 2: Spearman’s correlations of nutritional screening tools and anthropometric measures with nutritional parameters and functional measures on hospital admission (n = 172) Nutritional screening tool Anthropometric measure MNA MNA-II MNA-SF-BMI MNA-SF-CC GNRI SNAQ CC MAC BMI Nutritional screening tool MNA-II 0.964*** MNA-SF-BMI 0.893*** 0.854*** MNA-SF-CC 0.857*** 0.858*** 0.912*** GNRI 0.388*** 0.380*** 0.383*** 0.307*** SNAQ 0.418*** 0.412*** 0.372*** 0.338*** 0.188 CC 0.492*** 0.512*** 0.431*** 0.376*** 0.671*** 0.169 MAC 0.366*** 0.379*** 0.346*** 0.334*** 0.711*** 0.246 0.641*** BMI 0.378*** 0.357*** 0.376*** 0.282*** 0.854*** 0.182 0.723*** 0.772*** Nutritional biomarkers CRP −0.043 −0.062 −0.046 −0.044 −0.111 −0.137 0.006 −0.018 0.024 Lymph 0.162* 0.165* 0.155* 0.152* 0.148 0.161* 0.081 0.096 0.129 Hb 0.134 0.135 0.180* 0.169* 0.242** −0.021 0.116 0.144 0.109 Albumin 0.190* 0.186* 0.197* 0.172* 0.549*** 0.096 0.131 0.133 0.110 Chol 0.002 0.015 0.043 0.045 −0.031 0.081 −0.032 −0.041 −0.064 HDL −0.106 −0.097 −0.056 −0.058 −0.089 −0.018 −0.149 −0.115 −0.152 LDL 0.113 0.131 0.122 0.132 −0.112 0.125 0.009 −0.069 −0.065 Micronutrients Iron 0.105 0.087 0.104 0.106 0.270** 0.042 0.096 0.208* 0.243** Folate 0.093 0.095 0.114 0.129 −0.023 0.017 −0.083 −0.060 −0.109 B12 −0.107 −0.122 −0.140 −0.139 −0.086 −0.202* 0.047 0.023 −0.039 Vit D −0.132 −0.143 −0.119 −0.072 0.024 −0.085 −0.020 0.012 −0.070 Physical and mental functional measures BI 0.208** 0.195* 0.151* 0.233** 0.057 −0.013 0.097 0.082 0.028 Grip 0.379*** 0.376*** 0.321*** 0.349*** 0.060 0.242** 0.179* 0.232** 0.096 MMSE 0.355*** 0.368*** 0.306*** 0.352*** 0.110 0.138 0.155* 0.202** 0.191* GDS −0.197* −0.192* −0.188* −0.236 0.000 −0.179* 0.001 −0.050 0.024 *Indicates significance with P < 0.05; **Indicates significance with P < 0.01; ***Indicates significance with P < 0.001. B12, vitamin B-12; BI, Barthel Index; BMI, body mass index; CC, calf circumference (cm); Chol, cholesterol; CRP, C-reactive protein; GDS, Geriatric Depression Scale −15; GNRI, Geriatric Nutritional Risk Index; Grip, maximal grip strength; Hb, haemaglobin; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; Lymph, lymphocyte; MAC, mid-arm circumference (cm); MMSe, Mini Mental State Examination; MNA, Mini-Nutritional Assessment; MNA-II, MNA version II without BMI; MNA-SF, MNA-short form; SNAQ, Simplified Appetite Nutritional Questionnaire; Vit D, 25OH-vitamin D. Table 3: Results of each regression model showing the association of nutritional screening tools and anthropometric measures against hospital discharge outcomes† Predictor variable Functional change (BI; n = 165) Length of GEMU stay (log(days); n = 165) Discharge to high level care (n = 172) B 95% CI P B 95% CI P OR 95% CI P Nutritional screening tool MNA 0.380 −0.080 to 0.830 0.119 −0.010 −0.020 to 0.000 0.051 0.950 0.890 to 1.100 0.110 MNA-II 0.310 −0.130 to 0.740 0.168 −0.010 −0.020 to −0.000 0.017 0.960 0.900 to 1.020 0.163 MNA-SF-BMI 0.030 −0.020 to 0.070 0.352 −0.020 −0.030 to −0.000 0.015 0.920 0.830 to 1.020 0.114 MNA-SF-CC 0.370 −0.020 to 0.070 0.329 −0.020 −0.030 to −0.000 0.039 0.950 0.860 to 1.050 0.298 GNRI 0.170 0.010 to 0.330 0.038 −0.002 −0.010 to 0.001 0.233 0.980 0.960 to 1.010 0.164 SNAQ 0.440 −0.300 to 1.160 0.237 0.004 −0.010 to 0.001 0.590 0.980 0.890 to 1.080 0.641 Anthropometric measure CC 0.480 0.020 to 0.930 0.041 −0.003 −0.120 to 0.010 0.466 0.940 0.880 to 1.000 0.064 MAC 0.410 −0.110 to 0.960 0.123 −0.010 −0.020 to 0.000 0.170 0.880 0.810 to 0.960 0.002 BMI 0.230 −0.130 to 0.600 0.208 −0.004 −0.010 to 0.000 0.321 0.960 0.910 to 1.010 0.109 †All regression analyses controlling for age, gender, cognitive impairment risk (Mini Mental State Examination), function (Barthel’s Index), Charlson’s comorbitidy index and lives alone. The multiple regression models (LOS and functional change) also controlled for depressive risk (Geriatric Depression Scale-15) and inflammation (indicated by C-reactive protein levels). Each line represents a separate regression model. . Bold indicates significance (P < 0.05). B, unstandardised beta coefficient (regression co-efficient); BMI, body mass index; CC, calf circumference (cm); CI, confidence interval; GEMU, Geriatric Evaluation and Management Unit; GNRI, Geriatric Nutritional Risk Index; MAC, mid-arm circumference (cm); MNA, Mini-Nutritional Assessment; MNA-II, MNA version II without BMI; MNA-SF, MNA-short form; OR, odds ratio; SNAQ, Simplified Appetite Nutritional Questionnaire. Dent E, Chapman I, Piantadosi C et al . E4 Australasian Journal on Ageing, Vol 34 No 1 March 2015, E1–E6 © 2014 ACOTA possible, and thus reducing health-care costs [21]. The median LOS in our study was 12 days, in line with other GEMUs [21]. Although we did not find MNA to be associated with LOS, lower scores on the shorter and easier to implement version, the MNA-SF (with BMI), were associated with longer stays. Moreover, the MNA-SF-CC and the MNA-II, in which weight or BMI is not required, also showed a similar association with LOS. These findings have practical implications in the GEMU, as the often considerable burden of weighing frail older people in hospital may possibly be avoided [1]. Lower MAC was the only measure associated with discharge to higher level care, in agreement with previous reports that MAC is a good discriminator of independent versus institutionalized living in older people [22]. MAC is a measure of both fat and muscle mass and thus possibly indicative of late-stage muscle wastage and impending mortality [13,23]. It is likely that factors other than MAC, including illness and family situation influence discharge destination [3]. Our study, however, controlled for a range of covariates that could be influencing discharge destination including cognition, comorbidity and living alone. No NSTs were associated with discharge to higher level of care, in keeping with the conflicting results of studies as outlined in a recent systematic review [3]. For functional change, lower GNRI and CC measures, indicative of reduced nutritional status, were associated with functional decline as assessed by BI. No other studies, to our knowledge, have looked at GNRI as a predictor of functional decline. Studies of CC have found it is linked to functional decline in institutionalized older people [13], possibly because it reflects muscle wastage and an inability to walk [13,23]. In the present study, the GNRI may have shown an association with functional decline for a number of reasons. Firstly, it includes albumin, with lower levels linked to reduced physical function in older people [24]. Secondly, it was originally designed as a prognostic tool [6]. Thirdly, in our study, it showed high associations with both CC (r = 0.711, P < 0.001) and MAC (r = 0.854, P < 0.001). Finally, lower GNRI scores were associated with lower iron levels which have been linked with functional decline [25]. Perhaps surprisingly no other measures were associated with functional change. We had expected the MNA score to show an association with function, as it has been found previously to associate with in-hospital functional decline [12], although not functional recovery [20]. The MNA-SF score has also been found to be associated with in-hospital functional decline [11]. It could be that the MNA and its versions may not have been sensitive enough in detecting functional change during the relatively short hospitalisation period [20]. Strengths and limitations This study recruited consecutive patients and eliminated inter-tester bias as one researcher performed all assessments. Many confounding variables were controlled for which improved the generalisability of results to other populations of hospitalised older people. The predictive value of regression models was not high, however, there were still significant relationships identified. The current study was an observational study, so no inference about causation can be made. The sample size was also relatively small. Possible study bias could exist for several reasons, including proxy assistance in patient interview in the cases of low cognition and/or language barrier, self-reported height was used in 41% of patients due to immobility and CC may have been influenced by oedema. Additionally, ‘discharge to destination other than home’ included discharges to subacute care (rehabilitation). These patients may have returned home after spending time in such a setting; however, at the time of their discharge from the GEMU, they were not deemed functional enough for a direct discharge home. It should also be noted that, despite 75% of patients improving in function, many of these patients were not discharged home. Reasons for this lack of discharge home included dementia and the influence of external factors such as finances and familial wishes. It could also be that this improvement in function was not adequate to return to home immediately at discharge. Conclusion In hospitalised older people, admission NSTs and anthropometric measures are associated with negative outcomes, but different measures are associated with different outcomes. The MNA-SF (using BMI or CC) and MNA-II were associated with LOS. MNA-SF is fast and rapid to implement and MNA-II does not involve the timeconsuming measurement of weight. GNRI and CC were associated with functional decline during hospitalisation, perhaps because they reflected greater illness and muscle wastage, respectively. A lower MAC was associated with a greater need for discharge to a higher level of care, possibly because it is an indicator of end-stage decline. The use of a NST to detect both undernutrition and risk of adverse outcomes in hospital will assist time-pressured clinicians. Future research should focus on the predictive ability of NSTs post-hospitalisation and the efficacy of in-hospital interventions. Acknowledgements We wish to thank Kylie Lange, biostatistician, for her guidance on statistical analyses. We also wish to thank GEMU staff, patients and their families. Renuka Visvanathan has previously been supported by educational grants from Nestle Inc. Nutritional screening tools and hospital outcomes Australasian Journal on Ageing, Vol 34 No 1 March 2015, E1–E6 E5 © 2014 ACOTA Key Points • In hospitalised older people, nutritional screening tools (NSTs) and anthropometric measures are associated with adverse clinical outcomes, such as length of stay, change in physical function and discharge destination. • Different measures are associated with different outcomes: GNRI and CC were both associated with functional change, MNA-II and MNA-SF were associated with length of hospital stay and MAC was associated with discharge to higher level care. • The use of a NST to detect both undernutrition and risk of adverse outcomes in hospital could assist time-pressured clinicians. References 1 Volkert D, Saeglitz C, Gueldenzoph H, Sieber CC, Stehle P. Undiagnosed malnutrition and nutrition-related problems in geriatric patients. The Journal of Nutrition, Health & Aging 2010; 14: 387–392. 2 Guigoz Y. The Mini Nutritional Assessment (MNA) review of the literature – What does it tell us? The Journal of Nutrition, Health & Aging 2006; 10: 466–485, discussion 85-7. 3 Dent E, Visvanathan R, Piantadosi C, Chapman I. Nutritional screening tools as predictors of mortality, functional decline and move to higher level care in older people: A systematic review. Journal of Nutrit


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