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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 31  |  Issue : 3  |  Page : 326-331

High prevalence of malnutrition among hospitalized patients in a tertiary care hospital by using malnutrition universal screening tool


1 Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
2 Department of Public Health and Community Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt

Date of Submission30-Dec-2018
Date of Acceptance14-Feb-2019
Date of Web Publication27-Aug-2019

Correspondence Address:
MD of Internal Medicine Mervat E Behiry
Department of Internal Medicine, Faculty of Medicine, Cairo University, 71 El Kasr El Aini, Cairo, 11562
Egypt
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ejim.ejim_126_18

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  Abstract 


Background Estimating the prevalence of malnutrition among the hospitalized patients is challenging. Malnutrition is associated with a variety of poor outcomes including long hospital stay and mortality as well as increased hospital costs.
Aim The study aims to assess the risk of malnutrition in hospitalized patients and to identify the main factors and clinical parameters associated with the risk of malnutrition.
Materials and methods The researchers conducted a cohort study to screen for the risk of malnutrition following hospital admission in a population of adult patients recently admitted to a tertiary care hospital in Egypt using the malnutrition universal screening tool (MUST) and simplified nutritional appetite questionnaire and to assess the effect of malnutrition on duration of hospital stay.
Results The study included 1000 patients with a mean age of 49±13.7 years. The median duration of hospitalization was 5 (4–7) days, with a range of 2–30 days. All those included in the study presented a risk for malnutrition (1.0% medium risk and 99.0% high risk). High risk malnutrition was most common among those with diabetes (P=0.001) and renal problems (P=0.03), and in patients who had been admitted for longer hospital stay (P=0.037). A statistically positive correlation was seen between the age of the patients (P=0.031, r=−0.031), length of stay (P=0.353, r<0.001), and MUST score. However, there was a statistically significant negative weak correlation between simplified nutritional appetite questionnaire (appetite) (P=0.003, r=−0.094), BMI (P<0.001, r=−0.120), albumin (P<0.001, r=−0.117), and MUST score. Malnourished patients had a longer hospital stay than those who are well nourished (P=0.002).
Conclusion We have identified an overall malnutrition risk of 100% among the hospitalized patients and ascertained that malnutrition is a risk of prolonged length of hospital stay. MUST questionnaire should be implemented to screen and early recognize the malnourished hospitalized subjects for better intervention.

Keywords: malnutrition, malnutrition universal screening tool, nutrition screening, prolonged hospital stay, tertiary care hospital


How to cite this article:
Behiry ME, Salem MR. High prevalence of malnutrition among hospitalized patients in a tertiary care hospital by using malnutrition universal screening tool. Egypt J Intern Med 2019;31:326-31

How to cite this URL:
Behiry ME, Salem MR. High prevalence of malnutrition among hospitalized patients in a tertiary care hospital by using malnutrition universal screening tool. Egypt J Intern Med [serial online] 2019 [cited 2019 Sep 17];31:326-31. Available from: http://www.esim.eg.net/text.asp?2019/31/3/326/265430




  Introduction Top


Disease-related malnutrition is a crucial public health problem in health-care settings as stated by the European Society for Clinical Nutrition and Metabolism and the European Nutrition for Health Alliance [1]. The prevalence of malnutrition among hospitalized patients ranges from 10 to 60%, depending on the definition used and the way of assessment [2],[3].

The adverse impact of malnutrition on the patient’s outcome is well established; malnutrition is an independent risk factor for late recovery, mortality, and morbidities, delayed wound healing, hospitalization, and increased health-care costs [4]. Many studies have found that the length of hospital stay is significantly longer in malnourished patients, with an increase of 40–70% in undernourished patients [5]. Therefore, the European Society for Clinical Nutrition and Metabolism has recommended the implementation of nutritional screening programs in hospitals, and further nutritional assessment of patients identified to be at risk of malnutrition for the sake of both patients and health-care systems [6].

Malnutrition in hospitalized patients is an underdiagnosed and undertreated problem [7]. Also, few studies have been conducted in Egypt to explore the current research topic. Therefore, the primary objective of the study was to assess the risk of malnutrition in patients recently admitted to a tertiary care level hospital and to identify the main factors and clinical parameters associated with the malnutrition risk.


  Materials and methods Top


Study design and population

A prospective cohort of hospitalized patients of internal medicine department, which has a capacity of 350 beds. All consecutive patients who had chronic illness aged greater than or equal to 18 years admitted to internal medicine wards at Cairo University Hospital during the study period were screened for malnutrition and were followed up to measure the duration of hospital stay. Pregnant women, clinically unstable and mentally noncooperative (unconscious) patients, not able to give informed consent, could not be weighed, and those who had an expected length of hospital stay of fewer than 3 days were excluded.

Sample size and the sampling technique

A consecutive sampling technique was used to recruit patients accepted to participate in the study. The researchers interviewed a total of 1000 adults during the study period starting from October 2016 through July 2017.

Data collection tools

A pretested, anonymous, structured interview questionnaire was used to collect the data. All questions were close ended and were precoded before data collection to facilitate data entry and analysis. The questionnaire included questions about the following data.

Sociodemographic characteristics

Age, sex, education, occupation, and residence.

Anthropometric measures

Anthropometric measurements (namely weight, height, and accordingly the BMI) were conducted for the enrolled patients.

Nutrition screening

The nutrition screening was carried out using a validated clinical screening tool: the malnutrition universal screening tool (MUST). MUST is a five-step screening tool to identify adults, who are malnourished, at risk of malnutrition, or obese. It also includes management guidelines which can be used to develop a management plan. Three independent criteria are used: (a) current weight status using BMI, (b) unintentional weight loss, and (c) acute disease with no nutritional intake for greater than 5 days. Each criterion can be rated as 0, 1, or 2. The overall risk for malnutrition is established as low (score=0), medium (score=1) or high (score ≥2) [8]. The number of MUST scores was undertaken by experienced clinical nurses on patients within 6 h of admission.

Simplified nutritional appetite questionnaire

Simplified nutritional appetite questionnaire (SNAQ) is a four-item single-domain questionnaire that is used to assess the appetite among the enrolled participants as poor appetite is common among chronic patients. Responses are scored by using a 5-point scale that is based on the following numerical scale: (a) 1, (b) 2, (c) 3, (d) 4, (e) 5 points verbally labeled. The total SNAQ (appetite) score is the sum of scores on the four items, with less than or equal to 14 indicating poor appetite and a significant risk of at least 5% weight loss within 6 months [9].

Patient’s files

A separate sheet was generated to collect the required data from the patient’s medical record:
  1. Medical history: length of hospital stay duration and outcomes (mortality or discharge). Length of stay (LOS) was categorized as prolonged based on a median of 9 days as the cutoff point [10].
  2. Laboratory data: hemoglobin (g/l) and serum albumin (g/l).


Hypoalbuminemia in an adult is defined if albumin is less than 3.5 g/dl [11].

Hemoglobin levels to diagnose anemia at sea level if hemoglobin is less than 12 g/dl [12].

Statistical analysis

The data were coded, entered, and analyzed using the Statistical Package SPSS version 21 (SPSS Inc., Chicago, Illinois, USA) [13]. Data were cleaned and double checked. Number and percentage were used to summarize qualitative variables. Mean, SD, median, and interquartile range were used to synthesize quantitative variables. Mann-Whitney test was used to compare the differences between two independent groups. Chi square test for qualitative variables. Spearman’s ρ nonparametric correlation was used to test the association between variables. A P value less than or equal to 0.05 was considered as statistically significant.

Ethical consideration

The Medical Research Committee in the Internal Medicine Department at the Faculty of Medicine Cairo University revised and approved the study protocol. Informed consent from each participant was obtained after proper orientation of them regarding the study objectives. Only those who agreed were included, and those who refused were excluded from the study. All procedures for data collection were treated with confidentiality according to the Helsinki Declaration of Biomedical Ethics [14].


  Results Top


The researchers screened 1000 patients with a mean age of 49±13.7 and the range was 18–89 years, with more than half being men (52.2%). About three-quarters of them (74.4%) were uneducated. The median (interquartile range) duration of hospital stay was 5 (4–7) days, with a range of 2–30 days. The mean BMI was 26.5±5.2. The more prevalent comorbidities were renal disease 321 (32.1%), followed by hepatic disease 214 (21.4%), as shown in [Table 1]. Malnutrition high risk was more frequent among those with diabetes (P=0.001), followed by renal disease (P=0.03), and in patients who had been admitted for a longer time (P=0.037) as shown in [Table 2].
Table 1 Background and clinical characteristics of the enrolled chronic patients

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Table 2 Distribution of the enrolled patients by malnutrition risk as revealed by malnutrition universal screening tool and different backgrounds and clinical characteristics

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Patients with MUST score ≥2 had a longer hospital stay than thosewhose MUST score <2 (6.5 ± 3.8 vs. 4.4 ± 0.8 days; P=0.03). Astatistically positive correlation was seen between the age of the patients (P=0.031, r=−0.031), LOS (P=0.353 , r<0.001), and MUST score. However, there was a statistically significant negative weak correlation between SNAQ (appetite) (P=0.003*, r=−0.094), BMI (P<0.001*, r=−0.120), albumin (P<0.001*, r=−0.117), and MUST score [Table 3].
Table 3 Correlations between malnutrition risk as revealed by malnutrition universal screening tool and different background and clinical characteristics

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  Discussion Top


The current study revealed that the malnutrition risk among the enrolled patients admitted to Kasr Al Aini Internal Medicine Department was 100.0% as defined by the MUST screening tool. The study ascertained that the main factors associated with a higher risk of malnutrition are age, prolonged length of hospital stay, and the presence of comorbidities. The high malnutrition risk prevalence might be attributed to overall poor health, poor appetite as revealed in the current study by the SNAQ appetite assessment tool among about three-quarters of the enrolled participants, or closely linked comorbidities hindering adequate nutritional intake and absorption, increased the requirements [15]. In addition, malnutrition high risk was more frequent among those with diabetes (P=0.001), followed by renal diseases (P=0.03), and all the hospitalized patients under study were suffering from one or more chronic illnesses.

The revealed prevalence was higher than the reported rates of earlier studies conducted among hospitalized patients [7],[16]. The variation between the current study figures and those revealed from other hospitals as previously mentioned could be explained by the difference between various hospital settings, different ethnicity, types, the severity of the illness, and different instruments used for the classification of malnutrition as well as different disease spectra. Also, the studied hospital is a university hospital that functions as a referral hospital and generally admits patients requiring extensive care.

Malnutrition risk is significantly associated with comorbidities such as diabetes and renal problems. This finding could be explained by the catabolic processes, generalized inflammation, and higher nutrient demand due to the underlying comorbidity [17]. Major comorbidity and critical states were found to be the main contributors to disease-related malnutrition. Thus, in these populations the evaluation of nutritional status must be accurate in order to optimize their clinical outcome [18], where increased energy and protein requirements, increased losses together with inflammation may play a central role. In fact, the interaction of disease and nutrition is bilateral: while disease may cause secondary malnutrition, malnutrition may adversely influence the underlying disease [19].

The negative impact of malnutrition on patient’s outcome is well demonstrated. Many studies have found that the LOS of malnourished, screened patients was longer than that for well-nourished patients when assessed by MUST [20]. We also confirmed that those patients classified as at high risk of malnutrition have a 2-day longer hospital stay than those classified moderate, supporting the importance of detecting this problem on admission to avoid important health-care-associated costs.

Unsurprisingly, the present study revealed that age was positively correlated with malnutrition risk (r=0.068, P=0.031); the increase in patient age was significantly associated with the higher nutritional risk in the sample studied here. This is broadly consistent with other studies that demonstrated the association between an increase in age and malnutrition risk [21]. The possible explanation is the physiological dysfunction occurring with an increase in age, coexistence of conditions such as dementia and dysphagia, polypharmacy, and the social denial. Also, the length of hospital stay was significantly associated with MUST score (r=0.03, P<0.001). The current finding is in line with an earlier study conducted by Lim et al. [5] who concluded that poor nutrition was associated with poor outcomes affecting the survival of patients, hospital costs, and length of stay.In the current study, there is a significant inverse correlation between albumin and malnutrition risk as revealed by the MUST score (r=−0.117 and P<0.001). The previous finding is logic as the mean level of albumin has been demonstrated to more reliably reflect protein–energy malnutrition among the participant’s as suppression of serum albumin levels usually occur in patients with chronic illness and is attributed to many causes such as renal loss or hepatic disease or protein-losing enteropathy [22].

One of the strengths of this study resides in the fact that MUST is a validated instrument, and its use in different geographical and clinical settings can ensure a high degree of comparability of results, as well as the study addresses an area not well explored in Egypt especially in a tertiary health-care hospital. This study also has a set of limitations. First, the cross-sectional nature of the study did not allow the assessment of associations between the presence of malnutrition risk and long-term outcomes such as the length of hospital stay. However, the current study was conducted to explore the situation in this new area of inquiry and to generate hypotheses as no information is available regarding the nutritional screening of hospitalized patients in Egypt. It was not used to infer causal relationships. Data from this study may not be generalized to other tertiary care hospitals in Egypt. The mix of patients attending the tertiary care hospital is different from the patient mix in the general population.

In conclusion, we have identified an overall malnutrition risk of 100.0% among the hospitalized, and we have ascertained the main factors associated with a higher risk of malnutrition; age, the prolonged length of hospital stay, and comorbidities. The findings of the study can serve to orient and substantiate future studies on the importance of implementing routine screening measures for the hospitalized patients.

Acknowledgements

The authors acknowledge all participants of the study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
The Prague Declaration: stop disease-related malnutrition. Available at: http://www.european-nutrition.org/index.php/topics/prague. [Accessed 4 March 2018].  Back to cited text no. 1
    
2.
Korfalii G, Gündogdu H, Aydintg S, Bahar M, Besler T, Moral AR et al. Nutritional risk of hospitalized patients in Turkey. Clin Nutr 2009; 28:533–537.  Back to cited text no. 2
    
3.
Kruizenga H, Van Keeken S, Weijs P, Luc B, Sandra B, Getty H et al. Undernutrition screening survey in 564,063 patients: patients with a positive undernutrition screening score stay in hospital 1.4 d longer. Am J Clin Nutr 2016; 103:1026–1032.  Back to cited text no. 3
    
4.
Budzyński J, Tojek K, Czerniak B, Zbigniew B. Scores of nutritional risk and parameters of nutritional status assessment as predictors of in-hospital mortality and readmissions in the general hospital population. Clin Nutr 2016; 35:1464–1471.  Back to cited text no. 4
    
5.
Lim SL, Ong KC, Chan YH, Oke WC, Ferguson M, Daniels L. Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality. Clin Nutr 2012; 31:345–350.  Back to cited text no. 5
    
6.
Löser C. Malnutrition in hospital: the clinical and economic implications. Dtsch Arztebl Int 2010; 107:911–917.  Back to cited text no. 6
    
7.
Corkins MR, Guenter P, DiMaria-Ghalili RA, Gordon L, Ainsley M, Sarah M et al. Malnutrition diagnoses in hospitalized patients. J Parenter Enteral Nutr 2014; 38:186–195.  Back to cited text no. 7
    
8.
Todorovic V, Russell C, Stratton R, Ward J, Elia M. The ‘MUST’ explanatory booklet. 1st ed. Redditch: BAPEN; 2003.  Back to cited text no. 8
    
9.
Wilson MM, Thomas DR, Rubenstein LZ, Chibnall JT, Anderson S, Baxi A et al. Appetite assessment: simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. Am J Clin Nutr 2005; 82:1074–1081.  Back to cited text no. 9
    
10.
Rabito EI, Marcadenti A, da Silva Fink J, Figueira L, Silva FM. Nutritional Risk Screening 2002, Short Nutritional Assessment Questionnaire Malnutrition Screening Tool, and Malnutrition Universal Screening Tool Are Good Predictors of Nutrition Risk in an Emergency Service. Nutr Clin Pract 2017; 23:526–532.  Back to cited text no. 10
    
11.
Vashi PG, Gupta D, Lammersfeld C, Braun D, Popiel B, Misra S et al. The relationship between baseline nutritional status with subsequent parenteral nutrition and clinical outcomes in cancer patients undergoing hyperthermic intraperitoneal chemotherapy. Nutr J 2013; 12:118.  Back to cited text no. 11
    
12.
Smith RE. The clinical and economic burden of anemia. Am J Manag Care 2010; 16:S59–S66.  Back to cited text no. 12
    
13.
IBM. Corp. IBM SPSS Statistics for Windows (Version 21. 0 .). Armonk, NY: IBM Corp; 2012.  Back to cited text no. 13
    
14.
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013; 310:2191–2194  Back to cited text no. 14
    
15.
Starke J, Schneider H, Alteheld B, Stehle P, Meier R. Short-term individual nutritional care as part of routine clinical setting improves outcome and quality of life in malnourished medical patients. Clin Nutr 2011; 30:194–201.  Back to cited text no. 15
    
16.
Álvarez-Hernández J, Planas Vila M, León-Sanz M, García de Lorenzo A, Celaya-Pérez S, García-Lorda P et al.; PREDyCES researchers. Prevalence and costs of malnutrition in hospitalized patients; the PREDyCES Study. Nutr Hosp 2012; 27:1049–1059.  Back to cited text no. 16
    
17.
Kubrack C, Jensen L. Malnutrition in acute care patients. Int J Nurs 2007; 44:1036–1054.  Back to cited text no. 17
    
18.
Raslan M, Gonzalez MC, Dias MC, Nascimento M, Castro M, Margues P et al. Comparison of nutritional risk screening tools for predicting clinical outcomes in hospitalized patients. Nutrition 2010; 26:721–726.  Back to cited text no. 18
    
19.
Zhang L, Wang X, Huang Y, Gao Y, Peng N, Zhu W et al. Nutrition Day 2010 audit in Jinling hospital of China. Asia Pac J Clin Nutr 2013; 22:206–213.  Back to cited text no. 19
    
20.
Philipson T, Thornton Sinder J, Lakdawalla D, Stryckman B, Goldman D. Impact of oral nutritional supplementation on hospital outcomes. Am J Manag Care 2017; 19:121–128.  Back to cited text no. 20
    
21.
Nițescu M, Furtunescu FL, Pițigoi D, Streinu-Cercel A, Săndulescu O, Oțelea MR et al. Screening for malnutrition risk at hospital admission in patients with infectious diseases. J Contemp Clin Pract 2017; 3:57–63.  Back to cited text no. 21
    
22.
Agarwal E, Ferguson M, Banks M, Batterham M, Bauer J, Capra S et al. Nutrition care practises in hospital wards: results from the Nutrition Care Day Survey 201. Clin Nutr 2012; 31:995–1001.  Back to cited text no. 22
    



 
 
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  [Table 1], [Table 2], [Table 3]



 

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