A significant variation in the methods used by dietitians to estimate the energy requirements was found, particularly in the obese patient group. In an age of rapidly increasing rates of obesity a professional consensus of treatment of this patient group is needed.

• Malnutrition in adults on admission to hospitals and care homes affects almost 1 in 3 subjects, who were mostly in the high risk category. Malnutrition is common in all types of care homes and hospitals, all types of wards and diagnostic categories, and all ages. It is also common in mental health units.• Nutritional screening policies and practice vary between and within health care settings, whilst malnutrition continues to be under-recognised and under-treated.• Much of the malnutrition present on admission to institutions originates in the community. Consistent and integrated strategies to detect, prevent and treat malnutrition should exist within and between all care settings. 1 2 Summary 1. The Nutrition Screening Survey 1.1 This report provides a summary of the largest nutrition screening survey undertaken in the UK. Reporters from 175 hospitals, 173 care homes and 22 mental health units in the UK completed a general questionnaire and an anonymous patient questionnaire as part of a national audit on nutritional screening. Unlike previous studies that used different criteria to identify malnutrition in various care settings, this survey used the same criteria based on the 'Malnutrition Universal Screening Tool' ('MUST') in all care settings. Data were collected on patients during the first three days of admission to hospitals and acute mental health units, and on residents admitted to care homes and long stay / rehabilitation mental health units in the previous six months. Hospitals 2.1Of 9336 patients who were screened on admission to hospital, 28% were found to be at risk of malnutrition, (high risk, 22%; medium risk, 6%).The combination of these two risk categories is henceforth referred to as 'malnutrition' for simplicity. 2.2Since the presence of 'malnutrition' at or shortly after admission to hospitals suggests that it largely originated in the community, strategies to prevent and treat malnutrition in the community setting should be considered. 2.3'Malnutrition'varied significantly according to source of admission (25% from home, 31% from another hospital, 32% from another ward, and 43% from a care home), type of admission (32% for emergency admission, 20% for elective admission), and type of ward (e.g. 43% in oncology wards and 15% in orthopaedic/trauma wards). It was also greater in hospitals that had a screening policy than those that did not (28% v 24%), and considerably greater in large hospitals with ≥1000 beds than in those with <1000 beds (38% v 26%).2.4'Malnutrition'was common in all age groups and diagnostic categories, but it was significantly more common in women, who were older than men (29% v 26%), in subjects aged over 65 years than under 65 years (30% v 24%), and in certain diagnostic categories than others (e.g. gastrointestinal disease (43%) and neurological disease (33%) versus cardiovascular (21%) and musculoskeletal conditions (18%)). A low body mass index (BMI <20 kg/m 2 ) contributed to a 'MUST' score in 4 out of 10 'malnourished' patients. 2.5Most hospitals reported that th...

Background:  Many methods are available to determine energy requirements; however, all have limitations and their use in clinical practice is variable and not universally understood (Green et al., 2008). Estimating energy requirements of obese patients is particularly problematic (Breen et al., 2004). The aim of the current survey was to investigate current practice in the estimation of energy requirements in an obese and non‐obese patient in a large cohort of UK dietitians. Methods:  A cross‐sectional web‐based survey of UK Registered Dietitians was performed. An opportunistic sample was recruited via e‐mail providing a link to the survey (contact details openly available on NHS Trust websites) and through the online newsletter of the British Dietetic Association. The anonymous online questionnaire was developed specifically for this project by dietitians working in nutrition support and was based on the structure of two previous surveys (Reeves et al., 2003; Green et al., 2008). Respondents were asked to estimate energy requirements using two theoretical case scenarios: one patient was obese and one was not. Demographic information including training, experience and job role were also collected. Data were analysed using SPSS, version 18 (SPSS Inc., Chicago, IL, USA); chi‐squared tests for independence, Kruskal–Wallis and Mann–Whitney U‐tests were performed. Results:  Six hundred and seventy‐two responses were received from all areas of the UK. For the non‐obese patient, prediction equations and adjustment for metabolic stress and physical activity was used by 90.3% of respondents. The median estimated energy requirement was 8704 kJ (2079 kcal) [interquartile range (IQR): 8122–9295 kJ (1940–2220 kcal)] day−1. The median target volume of feed prescribed was 2000 (IQR: 2000–2000) mL day−1; significantly less than estimated requirements (P < 0.001). Estimated energy requirement using kcal/kg method was significantly lower compared to the equations method: 7536 kJ (1800 kcal) [range: 2428–10798 kJ (580–2579 kcal)] versus 8704 kJ (2079 kcal) [range: 2428–12385 kJ (580–2958 kcal)] day−1 (P < 0.001). For the obese patient, prediction equations to estimate basal metabolic rate (BMR) alone was used by 50.7% of respondents. Nutrition support dietitians used a lower stress factor compared to non‐nutrition support dietitians (10.3 ± 6.3 versus 13.6 ± 6.05 %; P = 0.016). The method used to estimate energy requirements was associated with years in clinical practice (P < 0.001); those in practice <5 years were more likely to use BMR alone when estimating energy requirements for the obese patient compared to those in practice >10 years. Respondents used a significantly lower kcal kg−1 for the obese patient (25 (IQR: 20–30) kcal kg−1) compared to the non‐obese patient (30 (IQR: 25–35) kcal kg−1) (P = 0.014). Discussion:  This survey found the majority of respondents used prediction equations to estimate energy requirements which is similar to the results of previous surveys (Green et al., 2008). Many more respondents used BMR al...