Development and Validation of the Malnutrition Universal Screening Tool - Case Study Example

I have included all research about MUST. I can not do any thing more. However you can request a new and get your paper done afresh. Iwill use this article for my own website in that case. It will be absolutely ok for me. thanks Malnutrition Universal Screening tool (MUST): An Assessment Tool for Nutritional Risk Malnutrition is a state when a deficiency, excess or imbalance of energy, protein and other nutrients causes unfavourable effects on physical shape, function and clinical outcome (Harris & Haboubi, 2005). A screening tool is used to find out a disease or a condition for e. g. malnutrition or obesity. It should ideally be used at first contact with the patient to detect nutritional risk and implement a care plan. The screening tool should be simple, quick to use, valid, reliable and acceptable to both patient and the nurse. It should be convenient for use in all types of settings and for all types of patients. MUST was developed in Britain with these aims (Gibney et al, 2005). It was developed by the Malnutrition Advisory group, a multidisciplinary group of British Association for parenteral and Enteral Nutrition (BAPEN, 2008), in 2000. It is recommended by British dietetic Association, The Royal College of Nursing, The registered nursing homes Association and The Royal college of Physician (England). It is an evidence based tool designed to detect malnutrition and obesity but not vitamin and mineral deficiency. MUST is developed for use in all adults including elderly, sick or healthy , hospital wards, outpatient clinics, general practice , community settings and public health (Thomas et al, 2007; Morrison et al 2007). A number of studies were conducted in these settings. Agreement was >95% in all studies prior to recommending the tool in practice, according to BAPEN (2008) MUST meets all the key requirements of a good tool. The tool is developed by experts in the field and validated by subsequent research. It is easy, quick and reliable besides recommended by recognized professional bodies. The tool has incorporated three elements viz. BMI, unintentional weight loss and acute disease effect to minimise error occurring from single element measurement. The present article is intends to discuss reliability and validity of the tool in light of controversial views about the BMI and other factors as indicator of nutritional status. Reliability and Validity of MUST: MUST Tool: Procedure MUST is a five-step screening tool to identify adults who are malnourished, at risk of malnutrition (undernutrition), or obese. MUST is designed to detect protein-energy malnutrition. Three parameters are used: current weight status (BMI), unintentional weight loss and acute disease status. A total score is calculated putting patient in low, medium or high risk category. The main advantage of this tool is that it could be used with adults of all ages across all health care settings. Moreover, the tool provides user the intervention guidelines once the risk category is determined (BAPEN, 2009; DeLegge, 2008). The brief procedure, according to BAPEN (2009), is carried out as given below: STEP1. Measure height and weight and find out BMI score using the chart. The BMI scores are classified as BMI (kg/m2) Score >20 (>30 obese) 0 18.5-20 1 30 Kg/m2 does not (Cook et al, 2005). Wilson et al (2007) commented that comparing a patient’s usual body weight is more useful than comparing the current weight with ideal body weight. BMI of less than 20 was found to be highly sensitive in older patients and cancer patients but not in cirrhotic patients with tense ascites, cardiovascular and neurological patients. A study of lung transplant patients revealed that BMI OF 25Kg/m2 increased mortality within 90 days of transplantation. MUST has kept a cut off of BMI 20 Kg/m2 but BMI less than 22 as already at risk for malnutrition in elderly. The MUST tool also has no items to assess functionality, clearly demonstrating that this test was not developed for the elderly population. It focuses too much on acute diseases thus limiting use in care homes (Sieber, 2006). However studies differ considerably in conclusion on elderly. Henderson et al (2008) carried out a study of nutritional risk, using MUST, in elderly patients with mean age 82.1 year. MUST significantly predicted death (log rank test, p = 0.022) correctly while neither BMI (log rank p = 0.37) or Birmingham nutrition score (log rank p = 0.35) predicted death. Certainly more studies are needed to enhance evidence based quality of the tool. STEP 2. Find out percentage unexplained weight loss and assign score from the table provided % Unplanned weight loss score (In 3-6 months) 10 2 Many clinical conditions such as dehydration and oedema may cause a failure to identify significant unintentional weight loss STEP 3: Establish acute disease effect and assign a score For example, If patient is acutely ill and there is no nutrition or likely to be no nutrition for >5 days, put a score of 2. The BAPEN team has derived this value by considering the fact that there is an increase in protein oxidation and nitrogen loss, therefore weight and lean body mass loss occurs faster in acute disease. The percentage weight loss in acute disease combined with no or almost no food intake for more than 5 days is comparable to > 10% over 3 –6 months. Hence a score of 2 has been assigned (BAPEN, 2008). ,. STEP 4: Add scores from steps 1, 2 and 3 above to find overall risk of malnutrition as given below: Score 0 no risk; Routine care Score 1 medium risk; Observe Score 2 or more high risk; Treat STEP 5: Care plan according to management guidelines of BAPEN 1. For Score 0 (low risk): routine clinical care. Repeat screenings weekly if patient is in the hospital. 2. For Score 1 (medium risk). Observe 1. Dietary intake for three days if subject is in hospital. 2. Little clinical concern if improved or taken adequate nutrition; if no improvement clinical concern repeat screening weekly in hospital and follow local guidelines for intervention. . 3. Score 2 or more (high risk) Treat by 1. Referring the matter to dietician, nutritional support team or as provided in the local policy or the hospital policy. 2 increase overall nutritional intake Monitor and review care plan weekly in the hospital Besides for all risk categories: Treat the cause of malnutrition, help in food choices for eating and drinking. Record malnutrition category and need for special diets. Follow local policy. For obesity control the probable causes before treating obesity. Alternative measurements and considerations: If height can not be measured: take recently measured self-reported height provided it is realistic and reliable. If the subject can not tell or is unable to tell, take one of the alternative measurement viz. ulna, knee height or demispan. If height and weight both are unknown, estimate BMI category by mid upper arm circumference (MUAC). The alternative BMI measurement, the MUAC has to be interpreted in the light of age, gender and ethnicity. Furthermore, some outcomes are unreliable in conditions that cause limb oedema (Harris & Haboubi, 2005). Wilson (2007) found anthropometric measurements of fat stores very easy to perform. However, amount of fat in healthy subjects vary widely. For example the athlete needs very little fat storage since he obtains his energy from food and has large muscle mass but low fat is not suitable for ill person who is fighting infections and stress of major surgery with barely some energy intake. Burden et al (2005) report though MUAA could be reliably measured, it has poor validity and is thus unlikely to be a good predictor of clinical outcome. Percentiles based on healthy populations do not apply well to the individuals with clinical condition If recent unplanned weight loss can not be calculated use the self reported but reliable and realistic weight loss. If the height, weight and BMI can not be measured following subjective ideas may be used to professionally assign the subject a nutritional risk category. However, these can not be used to assign a score: For BMI: clinical impression: thin, acceptable weight, overweight. Obviously wasting (very thin) or obesity (very overweight) Unplanned Weight Loss: Clothes and /or jewelry became loose fitting (weight loss,) Decreased food intake, reduced appetite or swallowing problems in past 3-6 months. Underlying disease or psychosocial or physical disability resulting in weight loss. Acute Disease Effect: No Food intake or likely to be no food intake for > 5 days Factors affecting Validity and Reliability of the Tool: Chapelhow et al, (2005 p103) state that a tool is valid if it measures what it should measure. For e. g. MUST measures malnutrition not personal preferences for food (validity) while reliability means the tool measures observations/items being studies in same way every time it is used. For example 10 nurses would use MUST in 10 different wards on 10 different patients. They would ask same questions and measure BMI and calculate scores on same factors. Thus validity and reliability need to be assured through research. The measurement for three crucial parameters are made by clinically acceptable methods so the tool has validity and differences between measurements with different patient would be as expected within limits, it is reliable also. The validity and reliability of the tool however may be affected if measurements are not made accurately. Problems may be there when nurse professional is not experienced and fully competent as she might overlook some medical conditions. Stratton et al (2004) found that the incidence of malnutrition risk using MUST was 19 to 60%, while from the other tools it ranged from 19 to 65%. MUST shows ‘excellent’ conformity with the MEREC Bulletin tool and good-fair agreement with the HH tool (kappa 0.825, kappa 0.647, respectively) in gastroenterology outpatients. MUST had good to excellent agreement with the NRS and the MST (kappa 0.775, kappa 0.707, respectively) in hospital inpatients. Fair-good agreement was found between MUST and MNA-SF in elderly medical and surgical patients (kappa 0.551, kappa 0.605, respectively);. There was excellent conformity between MUST and SGA in hospital inpatients below 65 years of age (kappa 0.783). Agreement between MUST and URS was poor in general surgical inpatients (kappa 0.255).. Sensitivity and specificity of MUST with varied from 60-97%. The Mini Nutritional Assessment-Short Form was used as a ‘gold standard’ in the study. There are differences in sensitivity and specificity of MUST towards different patient, however since all tools reflect the differences so it can be assigned a cause related to respective patient’s medical condition. Researcher found ‘MUST’ and MST as the simplest and fastest tools that could complete screening in 3–5 min. In yet another study, Kyle et al (2006) found that the sensitivities of the NRI, MUST, and NRS-2002 were 43%, 61% and 62%, respectively. Specificities of the NRI, MUST and NRS-2002 were 89%, 76% and 93%, respectively. NRS-2002 had greater positive and negative predictive values (85% and 79%) than the other two tools. All four instruments found a statistically significant association between nutritional risk and LOS. Agreement was moderate between SGA and NRS-2002 (kappa 0.48, P Read More