Poor nutritional status of older subacute patients predicts ... - Nature

European Journal of Clinical Nutrition (2012) 66, 1224–1228 & 2012 Macmillan Publishers Limited All rights reserved 0954-3007/12 www.nature.com/ejcn

ORIGINAL ARTICLE

Poor nutritional status of older subacute patients predicts clinical outcomes and mortality at 18 months of follow-up K Charlton1, C Nichols1, S Bowden2, M Milosavljevic2, K Lambert2, L Barone2, M Mason2 and M Batterham3 BACKGROUND/OBJECTIVES: Older malnourished patients experience increased surgical complications and greater morbidity compared with their well-nourished counterparts. This study aimed to assess whether nutritional status at hospital admission predicted clinical outcomes at 18 months follow-up. SUBJECTS/METHODS: A retrospective analysis of N ¼ 2076 patient admissions (65 þ years) from two subacute hospitals, New South Wales, Australia. Analysis of outcomes at 18 months, according to nutritional status at index admission, was performed in a subsample of n ¼ 476. Nutritional status was determined within 72 h of admission using the Mini Nutritional Assessment (MNA). Outcomes, obtained from electronic patient records, included hospital readmission rate, total Length of Stay (LOS), change in level of care at discharge and mortality. Survival analysis, using a Cox proportional hazards model, included age, sex, Major Disease Classification, mobility and LOS at index admission as covariates. RESULTS: At baseline, 30% of patients were malnourished and 53% were at risk of malnutrition. LOS was higher in malnourished and at risk, compared with well-nourished patients (median (interquartile range): 34 (21, 58); 26 (15, 41); 20 (14, 26) days, respectively; Po0.001). Hazard rate for death in the malnourished group is 3.41 (95% confidence interval: 1.07–10.87; P ¼ 0.038) times the well-nourished group. Discharge to a higher level of residential care was 33.1%, 16.9% and 4.9% for malnourished, at-risk and well-nourished patients, respectively; Pp0.001). CONCLUSION: Malnutrition in elderly subacute patients predicts adverse clinical outcomes and identifies a need to target this population for nutritional intervention following hospital discharge. European Journal of Clinical Nutrition (2012) 66, 1224–1228; doi:10.1038/ejcn.2012.130; published online 19 September 2012 Keywords: malnutrition; elderly; subacute hospitals; MNA; mortality

INTRODUCTION Almost 40 years ago, Dr Charles Butterworth drew attention to the widespread prevalence of malnutrition in hospitalised patients and its detrimental effect on recovery and subsequent prognosis.1 Since his seminal article in 1974, there have been attempts to improve the detection of malnutrition through screening initiatives in hospital settings. In older patients, malnutrition is an important predictor of adverse clinical outcomes and is associated with a prolonged hospital stay (length of stay, LOS), a decline in functional ability, an increased risk of institutionalisation and higher rates of mortality.2–5 As well as impacting on patient quality of life, malnutrition places a burden on the health-care system, with related costs estimated to be in excess of h9.2 billion per year in the United Kingdom.6 In recognition of the importance of early identification of malnutrition and its appropriate management in older patients admitted to hospital, a number of authorities have published clinical guidelines that recommend routine nutrition screening in this age group.7–10 However, these guidelines have not been translated into practice. A 25-country study in Europe and Israel (‘nutritionDay’) that included over 21 000 patients recruited from 325 hospitals identified wide

variation in nutrition screening practices both between and within countries.11 Similarly, in Australia and New Zealand, a large number of acute care hospital wards do not comply with evidence-based practice guidelines for nutritional management of malnourished patients.12 In pooled analyses of studies that have used the same validated instrument to define nutritional status (that is, Mini Nutritional Assessment (MNA)) in older people (mean age ¼ 82.3 years), the prevalence of malnutrition is estimated to be 22.8%. However, prevalence differs according to residential setting, being highest in hospitals (50.5 and 38.7% in subacute (rehabilitation) and acute hospitals, respectively), followed by nursing homes (13.8%) and lowest in independently living elders (5.8%).13 Appropriate screening and intervention have been shown to result in remarkably improved outcomes. A moderate exercise programme, together with adequate dietary intake, can improve muscle strength and mobility in frail residents of aged care facilities.14 Additional protein and energy supplementation given to hospitalised frail and undernourished elderly patients can be of benefit15,16 through increased weight, and muscle bulk and strength, improved function and independence, reduced mortality

1 School of Health Sciences, Faculty of Health and Behavioural Sciences, University of Wollongong, Wollongong, Australia; 2Nutrition and Dietetics Department, Illawarra and Shoalhaven Local Health District, Wollongong Hospital, Wollongong, New South Wales, Australia and 3Centre for Statistical and Survey Methodology, University of Wollongong, Wollongong, New South Wales, Australia. Correspondence: Professor KE Charlton, School of Health Sciences, Faculty of Health and Behavioural Sciences, University of Wollongong, Wollongong, New South Wales 2522, Australia. E-mail: [email protected] Contributors: KEC—conceptualisation of study design, data analysis and primary responsibility for writing the article; CN—data entry, data analysis and writing the article; SB— study design, data collection, entry and cleaning and data analysis; KL and LB—data collection, data entry and writing the article; SM—data collection and writing the article; MM—study design and writing the article; MB—statistical data analysis and editing. Received 17 July 2012; revised 23 August 2012; accepted 24 August 2012; published online 19 September 2012

Malnutrition predicts poor clinical outcomes K Charlton et al

1225 and a greater number of patients who are able to return home.17 Effective management of malnutrition in older persons can reduce health-care costs associated with hospital admissions. In one study, intensive nutritional support of malnourished, hospitalised, older surgery patients resulted in a 15–30% reduction in rehabilitation time and a 40% reduction in the duration of hospitalisation.18 The current study was undertaken to determine whether malnutrition in older patients admitted to subacute hospitals predicts poor clinical outcomes at 18 months of follow-up. MATERIALS AND METHODS Retrospective secondary data analysis was conducted of all patients aged 65 years and older who were admitted to two rehabilitation hospitals in the South East Sydney and Illawarra Area Health Service region, New South Wales, Australia between the periods of 1 March 2003 to 30 June 2004, and again from 11 January 2005 to 10 December 2008 (reported elsewhere).19 To be eligible for admission to these facilities (predominantly from acute care hospitals in the surrounding local area), patients needed to meet the 2005 Australasian Faculty of Rehabilitation Medicine Guidelines20 namely: (i) have defined rehabilitation goals; (ii) be willing to participate actively in therapy to maximise functional abilities; (iii) not require palliative or maintenance care. A total of 2514 patients were identified as eligible for inclusion from the database; of these 438 had incomplete data, resulting in a total sample of 2076 patients. For the purpose of the current analysis, a subsample of n ¼ 476 was recruited for whom follow-up data of at least 12 months was available. The introduction of an electronic admissions system in December 2006 allowed tracking of patients at 12 months. Prior to this, subsequent admissions were difficult to extract. Thus, all patients aged 65 þ years who were admitted between 12 December 2006 and 6 February 2008 for whom MNA data were recorded were eligible for the follow-up analyses.

Data collection Nutritional status was determined on admission using the 18-item MNA21 scored as follows: malnourished (scoreo17); at risk of malnutrition (17–23.9); and well-nourished (X24).22 The MNA comprises four domains including anthropometric assessment [recent weight loss, BMI (body mass index), mid-arm circumference and calf circumference], global assessment (lifestyle, mobility and medications taken), dietary assessment (changes in appetite, meals per day, daily intake of protein, fruit vegetables and fluid) and subjective assessment (self-perception of health and nutritional status). Dietitians working within the two rehabilitation hospitals sampled received standardised training in the use of the MNA by senior dietitians and routinely administered the MNA within 72 h of admission. Patient demographic details, admission dates and location, MNA assessment scores, weight, BMI and MDC (Major Disease Classification) data were entered into the departmental computerised patient database. Hard copies of patients’ files were reviewed to obtain missing information. Date of hospital discharge was obtained from the state-based computerised patient information system (iPM). Age was calculated as the number of years between recorded date of birth and date of admission. Length of hospital stay (LOS) was the total number of days in hospital undergoing rehabilitation excluding the discharge date. Change in level of care was recorded if the discharge location differed from location when admitted to hospital. Number of hospital readmissions was computed from electronic patient records, and included admissions to the emergency department. Hospital transfers, defined as patients discharged and readmitted to another hospital on the same day, were counted as a single admission. Mortality was recorded from the iPM between 2 February and 6 February 2009. All data were collated using Microsoft Office Excel (1985–2003, Microsoft Corporation, Redmond, WA, USA).

Statistics Statistical analysis was conducted using Microsoft Office Excel (1985–2003, Microsoft Corporation) and SPSS (V17.0: 2006, SPSS, Inc., Chicago, IL, USA). Descriptive statistics were performed and categorical variables expressed as proportions. Data were assessed for normality using the Shapiro–Wilk test. Between-group comparisons were made using one-way ANOVA, Mann–Whitney U or Kruskal–Wallis tests. Spearman’s correlation was used & 2012 Macmillan Publishers Limited

Table 1. Characteristics of total sample of rehabilitation admissions, compared with subsample of 18-month follow-up participants Characteristic Age (years) Range Weight (kg) Range BMI (kg/m2) Range Total MNA score median (IQR)

Follow-up sample (n ¼ 469)

Total sample (n ¼ 2076)

80.2 (7.1) 65–97 66.5 (16.0) 30–108 24.7 (5.4) 14–52 20 (16, 22.5)

80.6 (7.2) 65–103 66.5 (16.0) 30–137 24.7 (5.3) 13–52 19.5 (15.5, 23)

29.6 53.1

32.8 51.5

17.3

15.7

26 (16, 42)

24 (14, 41)

MNA classification (% subjects) Malnourished (MNAp17) At risk of malnutrition (MNA ¼ 17.1–23.9) Well-nourished (MNAX24) LOS (days), median (IQR) Abbreviation: IQR, interquartile range.

to test for associations between variables of interest. A small correlation was defined as r ¼ 0.10–0.29, medium r ¼ 0.30–0.49 and large 0.50–1.00. The sample was categorised into subgroups based on age, gender and MNA score as shown in Table 1. Proportions were compared using w2 or Exact tests. A Cox proportional hazards model was used to investigate differences in survival between MNA categories, controlling for potential confounders, including MDC at admission, age, sex, mobility and LOS at index admission (loge transformed). Investigating the association between malnutrition status and length of hospital stay (LOS) is problematic since those who die earlier on in the follow-up period may, by definition, have a lower LOS. In order to account for this source of cofounding, a Cox proportional hazards model was used with death as the censoring variable (event) and including the covariates of MNA category, MDC, mobility, LOS at index admission and gender. The covariate of interest is the effect of MNA on survival status so the survival plots displaying the cumulative survival function on a linear scale for each MNA category and the associated hazard ratios from the cox regression are presented. Statistical significance was defined as Pp0.05.

Ethical considerations Ethical approval was obtained from the joint Human Research Ethics Committee of the University of Wollongong and the South East Sydney Illawara Area Health Services. All data were deidentified to ensure confidentiality.

RESULTS Demographics Four hundred and seventy-six patients met the inclusion criteria and their data were analysed in this study. Mean time of follow-up was 18.97±3.84 months (12–26 months). No significant differences were found for age, anthropometric characteristics, initial LOS, nor classification of nutritional status between the subsample and the larger sample of 5-year hospital admissions (N ¼ 2076) (Table 1). Mean age of the patients was 80.2 years (s.d. ¼ ±7.1, range 65–97 years) and 61.4% (n ¼ 289) of the sample were women. The proportion of patients within each disease classification included amputees (2.3%, n ¼ 11), cardiovascular/respiratory (6.2%, n ¼ 29), neurological (3.2%, n ¼ 15), orthopaedic (40.2%, n ¼ 189), stroke (20.0%, n ¼ 94) and ‘other’ (28.0%, n ¼ 132). Based on recorded diagnoses obtained from medical discharge summaries, 22.8% (n ¼ 105) of patients were diabetic, 37.8% (n ¼ 178) had a history of fracture, 45.3% (n ¼ 252) a history of falls, 23.2% (n ¼ 107) had osteoporosis, 16.5% (n ¼ 76) were diagnosed as exhibiting cognitive decline (dementia and other related diseases), 15.1% (n ¼ 71) had medically diagnosed depression and 17.2% (n ¼ 81) were deceased at the time of data collection. European Journal of Clinical Nutrition (2012) 1224 – 1228


1226 Table 2.

The comparison of nutritional and anthropometric variables and outcome indicators between MNA nutritional risk categories at baseline

Variables, mean (s.d.) range

Malnourished (MNA total o17 points)

At risk of malnutrition (MNA total 17–23.5 points)

Well-nourished (MNA total X24 points)

Difference between MNA categories (P-value)

N Age (years) BMI (kg/m2) Length of stay (days), median (IQR) MNAd Screening Score MNAd Assessment Score MNA totald Cognitive decline (%) Depression (%) Change in level of care (%) Deceased (%)

137 (F ¼ 89, M ¼ 50) 80.9 (7.2) 66–97 20.8 (4.67) 13.9–48.0 34 (21, 58)

253 (F ¼ 151, M ¼ 98) 80.6 (7.1) 65–96 25.5 (4.57) 14.5–38.2 26 (15, 41)

81 (F ¼ 47, M ¼ 34) 77.8 (6.4) 65–86 28.9 (5.0) 21.9–52.2 20 (14, 26)

P ¼ 0.655a 0.0041b,e o0.001b,f o0.001c,f

5 (4, 6) 8.5 (7, 9.5) 14 (11, 15.5) 23.0 24.3 33.1 23.7

9 (7, 10) 12 (11, 13) 20.5 (19, 22) 15.6 10.7 16.9 16.5

11 (10.5, 12.0) 14 (13.5, 15) 25 (24, 26) 8.8 14.8 4.9 8.6

0.022a,e 0.002a,e o0.001a,f 0.015a,e

Abbreviations: BMI, body mass index; IQR, interquartile range; LOS, length of hospital stay (total number of days in hospital undergoing rehabilitation excluding the discharge date); MNA, mini nutritional assessment. %Change in level of care ¼ percentage of patients in MNA nutritional risk category discharged to a location not equal to admission location. Number of rehospitalisations ¼ number of inpatient admissions 12 months post initial admission date including emergency admissions. %History of falls ¼ percentage of patients in MNA nutritional risk category with primary or secondary diagnosis of fall. %History of fracture ¼ percentage of patients in MNA nutritional risk category with primary or secondary diagnosis of fracture. History of osteoporosis ¼ percentage of patients in MNA nutritional risk category with primary or secondary diagnosis of osteoporosis. %History of depression ¼ percentage of patients in MNA nutritional risk category with primary or secondary diagnosis of depression. %History of cognitive decline ¼ percentage of patients in MNA nutritional risk category with primary or secondary diagnosis of cognitive decline/dementia. aw2 test. bOne-way analysis of variance. cKruskal–Wallis test. dGroups not compared statistically as scores used to create categories. ePp0.05. fPp0.001.

Table 3.

Discharge location of patients divided into MNA nutritional risk categories (% (n))

Discharged to

N

Home

Hostel

Malnourished (MNA total o17 points) At risk of malnutrition (MNA total 17–23.5 points) Well-nourished (MNA total424 points)

122 223 77

54.0% (67) 76.1% (166) 92.2% (71)

25.8% (32) 12.4% (27) 6.5% (5)

Nursing home

Died during hospitalisation

18.5% (23) 11.0% (24) 1.3% (1)

1.6% (2) 0.5% (1) 0% (0)

Abbreviation: MNA, Mini Nutritional Assessment. % Change in level of care ¼ percentage of patients in MNA nutritional risk category discharged to a location not equal to admission location. w2 ¼ Po0.001 (Exact statistic).

At the index hospital admission, more than half (53.1%, n ¼ 249) of the patients were classified as being at risk of malnutrition, with a further a 17.3% (n ¼ 81) classified as malnourished. Median (interquartile range) MNA score was 20 (16, 22.5), which fell within the ‘at-risk’ category. Factors associated with poor nutritional status There was no significant difference in nutritional or anthropometric indices between genders, with the exception of age. Women were significantly older than men (81.2±7.1 vs. 78.5±14.3, Pp0.001). MNA score was negatively associated with LOS (r ¼ 0.23; Po0.001) and positively associated with BMI (r ¼ 0.59, Po0.001). There was a negative correlation between age and nutritional status (MNA) (r ¼ 0.31; Pp0.001). Patients who were well-nourished were more likely to discharged home (92.2% of this nutritional risk subgroup) than other groups (Po0.01). Forty six per cent (n ¼ 55) of malnourished patients had a poor clinical outcome (admission to higher level care and/or died during index admission). Outcomes at follow-up Before admission, 80.8% (n ¼ 379) of patients were living at home, 9.4% (n ¼ 44) at nursing homes or hostels and 9.8% (n ¼ 46) were unknown. The proportion of patients recording a change in level of care was significantly different between MNA groups (Table 2) and discharge destination is shown according to nutritional status in Table 3. Median number of readmissions was 0 (range ¼ 0–12); however, 62% (n ¼ 291) of patients recorded at least one European Journal of Clinical Nutrition (2012) 1224 – 1228

readmission to hospital during the follow-up period. Number of hospital readmissions was not associated with MNA score (r ¼ 0.04, P ¼ 0.45) nor with other variables. The malnourished group had a lower survival curve compared with the ‘at-risk’ and well-nourished groups (Figure 1). The hazard rate for death in the ‘at-risk’ group is 1.61 (95% confidence interval: 0.52–5.17; P ¼ 0.397) times the well-nourished group. The hazard rate for death in the malnourished group is 3.41 (95% confidence interval: 1.07–10.87; P ¼ 0.038) times the well-nourished group.

DISCUSSION This study provides convincing evidence that older patients that are admitted to a subacute setting in a malnourished state experience poorer clinical outcomes and an increased risk of mortality within the subsequent 18 months, compared with their well-nourished peers. Our data confirm that of others23 in that the malnourished and at-risk groups had increased cumulative lengths of hospital stay over an 18-month period, taking into account differences in MDC at hospital admission. It has been estimated that the cost of treating a nutritionally at-risk patient is 20% higher than the average for the respective diagnosis-related group.24 It was not the purpose of the present analysis to assess risk factors for malnutrition, as these are well established in older people, and include dementia, depression, decreased visual acuity, poor dentition, pain from acute or chronic diseases, especially cancer, polypharmacy, social isolation and financial burden.25 & 2012 Macmillan Publishers Limited


1227 1.0

Cumulative survival

0.8

MNA category

0.6

Well nourished At risk Malnourished 0.4

0.2 0.00

200.00 400.00 600.00 Days since discharge

800.00

Figure 1. Survival curve of mortality at 18 months, according to category of nutritional risk.

Accompanied with these risk factors are age-related physiological changes that increase an older individual’s susceptibility to an inadequate food intake, and impaired nutrient absorption. The current analysis aimed to describe the magnitude of the problem of malnutrition in a representative sample of older patients admitted to subacute hospitals in a defined geographical area, and to investigate functional and clinical consequences of a poor nutritional status. Within the hospitals sampled, it is clinical protocol for a full nutritional assessment to be completed within 72 h of admission in all patients over the age of 65 years using the validated MNA tool.21 The MNA has been used extensively in older patients in community, hospital and nursing home settings in many countries around the world26 and more recently a short version (MNA-Short Form) has been validated that is able to be completed within a few minutes.13 Availability of reliable, high-quality data on the nutritional status of all patients admitted within a defined time frame offered the opportunity of retrospective data analysis. The subsample of patients included in the 18-month outcomes analysis had similar baseline characteristics to the total sample of inpatients (n ¼ 2076), thus enabling generalisability of the results. Nutrition screening and assessment represent only the first steps in the pathway to improved nutritional care, and without intervention is unlikely to result in beneficial patient outcomes.27 Ensuring an optimal nutritional status is not just about providing adequate food intake. An Australian study reported that less than a quarter (23%) of older patients in rehabilitation hospitals were able to consume sufficient energy through oral intake, including supplement prescription, despite being provided with their energy and protein requirements.28 A systematic review of the evidence regarding the impact of improved nutritional care on nutritional and clinical outcomes identified that high-quality studies on this topic are few.27 Non-traditional effective ways to improve patient dietary intake have been demonstrated, including the incorporation of feeding assistants onto a trauma ward29 or provision of a family-style dinner arrangement instead of a tray service within a nursing home setting.30 It is noteworthy that over half of the patients in the present study who were characterised as malnourished and 76% of those & 2012 Macmillan Publishers Limited

at risk of malnutrition were discharged home from hospital. Community-based services exist that provide support to such patients; however, these generally require an in-home eligibility assessment that may take a number of weeks, if not months, to be completed. The services available include Home Care, community Aged Care Packages and Community Options; however, the limited number of packages available for each local area are insufficient to meet demand, and patients discharged from hospital may be placed on a waiting list. In the United States, home-delivered meals are provided under the Older Americans Act Nutrition Program with the objective of assisting older adults to remain in community residence and to reduce the risk of rehospitalisation.31 However, due to an increased demand for these services, waiting lists of between 3 and 5 months are common32 which exceeds the critical 2-week period of recovery following discharge.33 In Australia, as in many other countries, poor referral mechanisms operate between hospital and community-based medical services, perhaps with the exception of retired servicemen and women who fall under the care of the Department of Veteran Affairs. An investigation of the home food environment of 512 older patients in the United States that were transitioning from hospital to home found that, despite a variety of foods being available in their homes, a third of this group were unable to both shop or prepare food for themselves.34 These two activities of daily living are important determinants of nutritional intake and functional ability in community-dwelling elders.35 Our data confirm a need for community-based services to prevent further weight loss in older adults that are poorly nourished. Locally, a CONECT (Community & Outpatient Nutrition Extended Care Team), established in 1997, provides outpatient dietetic services, including face-to-face consultations, telephonic support and home visits, to nutritionally at-risk patients for 6 months after hospital discharge. Preliminary assessment of this service found that the ambulatory care group had improvements in nutritional status; however, this was only significant using a per protocol analysis due to the high refusal rate (60%) for the service.36 The model of care needs to be tested on a larger scale and compared against the cost-effectiveness of such services such as referral to Meals on Wheels or oral nutritional supplementation at home. Qualitative methods are required to better understand older patients’ needs, and to identify barriers to participation in such services. In conclusion, a third of older patients admitted to subacute hospitals are in a malnourished state and are at higher risk of a prolonged hospital stay, discharge to a higher level of care, as well as increased risk of mortality within an 18-month period, even accounting for the underlying medical reason for hospital admission. Well-designed studies are required that demonstrate which interventions, both in-hospital and after discharge, are most effective in this patient group to reverse or prevent further nutritional decline to result in improved functional outcomes, in the most cost-effective manner.

CONFLICT OF INTEREST Karen Charlton was a member of the International MNA Revision Group and attended a workshop in Switzerland in October 2008 that was fully funded by Nestle Nutrition Institute, Switzerland. She has received honoraria from Nestle Healthcare Nutrition (Australia) for participation in a Malnutrition in the Elderly Advisory Board and for educational presentations to general practitioners and geriatricians on the topic of malnutrition screening. The other authors declare no conflict of interest.

ACKNOWLEDGEMENTS No external sponsorship was received for this study. A summer scholarship from the Smart Foods Centre, University of Wollongong was provided to CN. Joanna Russell is thanked for editorial assistance.

European Journal of Clinical Nutrition (2012) 1224 – 1228


1228 REFERENCES 1 Butterworth C. The skeleton in the hospital closet. Nutr Today 1974; 9: 4–8. 2 Kagansky N, Berner Y, Koren-Morag N, Perelman L, Knobler H, Levy S. Poor nutritional habits are predictors of poor outcome in very old hospitalized patients. Am J Clin Nutr 2005; 82: 784–791. 3 Correia M, Isabel TD, Waitzberg DL. The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin Nutr 2003; 22: 235–239. 4 Brantervik ÅM, Jacobsson IE, Grimby A, Walleń TCE, Bosaeus IG. Older hospitalised patients at risk of malnutrition: correlation with quality of life, aid from the social welfare system and length of stay? Age Ageing 2005; 34: 444–449. 5 Visvanathan R, Macintosh C, Callary M, Penhall R, Horowitz M, Chapman I. The nutritional status of 250 older australian recipients of domiciliary care services and its association with outcomes at 12 months. J Am Geriatr Soc 2003; 51: 1007–1011. 6 Elia M. Nutrition and health economics. Nutr 2006; 22: 576–578. 7 Kondrup J, Allison SP, Elia M, Vellas B, Plauth M. ESPEN Guidelines for Nutrition Screening 2002. Clin Nutr 2003; 22: 415–421. 8 National Collaborating Centre for Acute Care. Nutrition Support in Adults. Oral Nutrition Support, Enteral Tube Feeding and Parenteral Nutrition. NICE Clinical Guideline 32, February) London: National Collaborating Centre for Acute Care, 2006. 9 Council of Europe. Food and nutritional care in hospitals: how to prevent undernutrition. In: Nutrition Programmes in Hospitals Group for the Committee of Experts on Nutrition FSaCH (edn). Council of Europe: Strasbourg, 2003. 10 Watterson C, Fraser A, Banks M, Isenring E, Miller M, Silvester C et al. Evidence based practice guidelines for the nutritional management of malnutrition in adult patients across the continuum of care. Nutr Diet 2009; 66: S1–S34. 11 Schindler K, Pernicka E, Laviano A, Howard P, Schu¨tz T, Bauer P et al. How nutritional risk is assessed and managed in European hospitals: a survey of 21,007 patients findings from the 2007–2008 cross-sectional nutritionDay survey. Clin Nutr 2010; 29: 552–559. 12 Agarwal E, Ferguson M, Banks M, Batterham M, Bauer J, Capra S et al. Nutrition care practices in hospital wards: results from the Nutrition Care Day Survey 2010. Clin Nutr 2012; 31: 41–47. 13 Kaiser M, Bauer J, Ramsch C, Uter W, Guigoz Y, Cederholm T et al. Validation of the mini nutritional assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging 2009; 13: 782–788. 14 Fiatarone MA, O’Neill EF, Ryan ND, Clements KM, Solares GR, Nelson ME et al. Exercise training and nutritional supplementation for physical frailty in very elderly people. N Engl J Med 1994; 330: 1769–1775. 15 Milne AC, Avenell A, Potter J. Meta-analysis: protein and energy supplementation in older people. Ann Intern Med 2006; 144: 37–48. 16 Milne AC, Avenell A, Potter J. Improved food intake in frail older people. BMJ 2006; 332: 1165–1166. 17 Potter JM, Roberts MA, McColl JH, Reilly JJ. Protein energy supplements in unwell elderly patients—a randomized controlled trial. JPEN J Parenter Enteral Nutr 2001; 25: 323–329. 18 Delmi M, Rapin CH, Bengoa JM, Delmas PD, Vasey H, Bonjour JP. Dietary supplementation in elderly patients with fractured neck of the femur. Lancet 1990; 335: 1013–1016.

European Journal of Clinical Nutrition (2012) 1224 – 1228

19 Charlton KE, Nichols C, Bowden S, Lambert K, Barone L, Mason M et al. Older rehabilitation patients are at high risk of malnutrition: evidence from a large Australian database. J Nutr Health Aging 2010; 14: 622–628. 20 Australasian Faculty of Rehabilitation Medicine (AFRM). Standards 2005—Adult Rehabilitation Medicine Services in Public and Private Hospitals: Sydney, 2005. 21 Guigoz Y, Vellas B, Garry PJ. Mini nutritional assessment: a practical assessment tool for grading the nutritional state of elderly patients. In: Guigoz Y, Vellas B, Garry PJ, Albarede JL (eds). Facts Research in Gerontology. vol 4 (Suppl 2). Serdi: Paris, 1994, pp 15–59. 22 Mini Nutritional Assessment (MNA): Research and Practice in the Elderly) Lausanne: Nestle, 1999. 23 Kyle UG, Genton L, Pichard C. Hospital length of stay and nutritional status. Curr Opin Clin Nutr Metab Care 2005; 8: 397–402. 24 Amaral TF, Matos LC, Tavares MM, Subtil A, Martins R, Nazare´ M et al. The economic impact of disease-related malnutrition at hospital admission. Clin Nutr 2007; 26: 778–784. 25 Inzitari M, Doets E, Bartali B, Benetou V, Di Bari M, Visser M et al. Nutrition in the age-related disablement process. J Nutr Health Aging 2011; 15: 599–604. 26 Bauer JM, Kaiser MJ, Anthony P, Guigoz Y, Sieber CC. The mini nutritional assessments-its history, today’s practice, and future perspectives. Nutr Clin Pract 2008; 23: 388–396. 27 Weekes CE, Spiro A, Baldwin C, Whelan K, Thomas JE, Parkin D et al. A review of the evidence for the impact of improving nutritional care on nutritional and clinical outcomes and cost. J Hum Nutr Diet 2009; 22: 324–335. 28 Walton K, Williams P, Tapsell L, Batterham M. Rehabilitation inpatients are not meeting their energy and protein needs. E SPEN Eur E J Clin Nutr Metab 2007; 2: e120–e126. 29 Duncan DG, Beck SJ, Hood K, Johansen A. Using dietetic assistants to improve the outcome of hip fracture: a randomised controlled trial of nutritional support in an acute trauma ward. Age Ageing 2006; 35: 148–153. 30 Nijs KAND, De Graaf C, Siebelink E, Blauw YH, Vanneste V, Kok FJ et al. Effect of family-style meals on energy intake and risk of malnutrition in Dutch nursing home residents: a randomized controlled trial. J Gerontol A Biol Sci Med Sci 2006; 61: 935–942. 31 Millen BE, Ohls JC, Ponza M, McCool AC. The Elderly Nutrition Program: an effective national framework for preventive nutrition interventions. J Am Diet Assoc 2002; 102: 234–240. 32 Salmon M, Bridges J. Waiting for dinner: elderly on the waiting list for homedelivered meals. Mississippi State. MS: Southern Rural Development Center. 2005. Report No. 9. 33 Fethke CC, Smith IM. The critical post-discharge period for older patients leaving a hospital. J Gerontol Soc Work 1991; 16: 93–105. 34 Anyanwu UO, Sharkey JR, Jackson RT, Sahyoun NR. Home food environment of older adults transitioning from hospital to home. J Nutr Gerontol Geriatr 2011; 30: 105–121. 35 Payette H, Gray-Donald K, Cyr R, Boutier V. Predictors of dietary intake in a functionally dependent elderly population in the community. Am J Public Health 1995; 85: 677–683. 36 Milosavljevic M, Bowden S, Mason S, Barone L, Williams P, Noble G. The dietetic management of malnutrition in and out of hospitals; is it time dietitians COMMIT to a long term model of care? Nutr Diet 2011; 68: 6.

& 2012 Macmillan Publishers Limited