Ms. Wu is a PhD candidate, Dr. Courtney is Adjunct Professor, Dr. Finlayson is Senior Research Fellow, School of Nursing and Midwifery, Dr. Isenring is National Health and Medical Research Council Australian Clinical Training Fellow, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; and Dr. Shortridge-Baggett is Professor, Department of Graduate Studies, Lienhard School of Nursing, Pace University, New York, New York. In addition, Dr. Isenring is Clinical Academic Fellow Conjoint Senior Lecturer, Queensland Health and University of Queensland, Brisbane, Queensland, Australia; and Dr. Courtney is Head, School of Nursing, Midwifery, and Paramedicine, Australian Catholic University, Brisbane, Queensland, Australia.
The authors have disclosed no potential conflicts of interest, financial or otherwise. Funding for this study was provided by a Queensland Nursing Council scholarship and an Australian Postgraduate Award scholarship, and was conducted as part of a PhD study of Dr. Wu. The sponsors had no role in the study design, execution, data analysis and interpretation, writing of the manuscript, or decision to submit the paper for publication. The authors acknowledge the assistance and support provided by all the team members of the Rehabilitation in Older Adults Project 2 (RIO 2) and to the patients and staff in the study hospitals for their time and participation.
Address correspondence to Min-Lin (Winnie) Wu, MSN, School of Nursing and Midwifery, Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Brisbane, Queensland 4059, Australia; e-mail: firstname.lastname@example.org.
Malnutrition in older adults is a serious global problem (Kubrak & Jensen, 2007; Watterson et al., 2009) and is associated with undesirable clinical outcomes, including increased morbidity, mortality, length of hospital stay, and health care costs (Correia & Waitzberg, 2003; Middleton, Nazarenko, Nivison-Smith, & Smerdely, 2001; Neumann, Miller, Daniels, & Crotty, 2005). International studies have reported a malnutrition prevalence ranging from 13% to 78% in acute hospital settings (Kubrak & Jensen, 2007). For hospitalized older adults, it ranges between 12% to 72%, depending on the different patient populations, settings, and the different definitions of malnutrition used (Heersink, Brown, Dimaria-Ghalili, & Locher, 2010).
This significantly high malnutrition prevalence is of concern because older adults are more likely to experience nutritional status deterioration over the period of hospitalization due to eating difficulties, the side effects of medication, and severity of the disease (Westergren, Unosson, Ohlsson, Lorefält, & Hallberg, 2002). Studies have shown that hospitalized older adults who are malnourished at the time of admission are likely to have increased risk of experiencing adverse events while in the hospital and following discharge, as well as increased risk of not being able to recover from malnutrition (Heersink et al., 2010; Soini, Routasalo, & Lauri, 2006). This has brought to light the importance of early and routine identification of malnutrition for older adults in acute hospital settings.
One of the potential adverse events after discharge is the need for hospital readmission. Older adults are known to have higher rates of emergency hospital readmissions in comparison to the general population (Parker, 2005; Victor, Healy, Thomas, & Seargeant, 2000), indicating a need to identify risk factors and early interventions. Previously identified risk factors for hospital readmission include multiple comorbidities (Inouye et al., 2008), impaired functionality (Covinsky et al., 2003), age (Inouye et al., 2008), recent multiple admissions (Lanièce et al., 2008), poor social support (Strunin, Stone, & Jack, 2007), and a history of depression (Marcantonio et al., 1999; Mitchell et al., 2010). These known risk factors enable identification of a high-risk population, allowing potential early interventions to reduce readmissions.
Malnutrition is amenable to prevention by early identification and appropriate nutritional intervention (Watterson et al., 2009). Assessing nutritional status in older adults generally includes medical, nutritional, and medication history; physical examinations; anthropometric data; biochemical parameters; and body composition analysis (Dyck & Schumacher, 2011; Visvanathan, Newbury, & Chapman, 2004). A combination of measurements has been recommended in clinical practice to detect malnutrition (American Dietetic Association, 1994). The terms screening and assessment are used when evaluating nutritional status, and they are often used interchangeably in the literature (Green & Watson, 2005). Nutrition screening, however, is considered to be a simple process to identify malnutrition risk, whereas nutrition assessment refers to a more in-depth and comprehensive evaluation of nutritional status, including dietary and medical history, physical assessment, anthropometric measurements, and laboratory data to confirm a diagnosis of malnutrition (American Dietetic Association, 1994).
A number of nutrition screening and assessment tools have been developed and validated for use in older adults (Stratton et al., 2004; Vellas et al., 1999). However, a gold standard measurement has yet to be defined and, as a result, nutritional evaluation remains unrecognized and overlooked (Forster & Gariballa, 2005). Among the nutrition assessment tools, the Subjective Global Assessment (SGA, Detsky et al., 1987) is a valid and reliable tool for assessing nutritional status in older adults (Christenson, Unosson, & Ek, 2002). Nutrition assessment often requires administration by an appropriately trained clinician, such as a dietician or RN, and can be time consuming, which means it may not be feasible for use on all hospitalized older patients. Nutrition screening, on the other hand, serves to identify patients who may be at risk of malnutrition in a quick and simple way. In the hospital, nutrition screening is usually carried out by RNs within 24 to 72 hours of hospital admission, which is an essential first step in early determination of older adults at risk of malnutrition (Charlton, 2010).
A valid, simple, and easy-to-use nutritional screening tool is an important consideration for nursing staff in a busy clinical environment (Green & Watson, 2005). The Malnutrition Screening Tool (MST) is the simplest and most widely used nutritional screening tool in Australian hospitals. It has been tested for validity in inpatients and oncology outpatients in Australia (Ferguson, Capra, Bauer, & Banks, 1999; Isenring, Cross, Daniels, Kellett, & Koczwara, 2006; Neelemaat, Meijers, Kruizenga, van Ballegooijen, & van Bokhorst-de van der Schueren, 2011) but not validated specifically in frail and older adults at high risk of hospital readmission. It is important to validate a nutrition screening tool that can be used across different health care settings so that nurses can detect those who may be at nutritional risk and require appropriate nutrition intervention, particularly older adults at risk of hospital readmission. The purpose of this study, therefore, was to validate the MST by assessing the agreement and prevalence of malnutrition risk between the MST and SGA in older adults at high risk of hospital readmission.
A total of 157 hospitalized patients were recruited from September 2008 to March 2010. These patients were participants in a randomized controlled trial (RCT) (registration number ACTRN12608000202369) investigating the effectiveness of a multifaceted transitional care intervention, including hospital and home-based exercise and nursing care for older adults at risk of hospital readmission. All patients admitted to the medical wards of the participating hospitals who fitted the inclusion and exclusion criteria were approached and invited to participate in the study.
Inclusion criteria were based on previously identified risk factors for readmission in older adults as described above, including: age 65 and older, admitted with a medical diagnosis, and at least one of the following risk factors for readmission: age 75 and older, multiple admissions in the previous 6 months, multiple comorbidities, living alone, lacking social support, having poor self-rated health, experiencing moderate to severe functional impairment, and having a history of depression. Exclusion criteria were based on participants’ ability to participate safely and understand the interventions of the main study. Patients who required home oxygen, were dependent on a wheelchair or unable to walk independently for 3 meters (patients independently using walking aids were not excluded), lived in a nursing home, or had a cognitive deficit or progressive neurological disease were excluded. Ethical approval for the study was obtained from the Human Research and Ethics Committees of both the university and hospital.
Potential participants were identified through medical wards within 24 hours of their admission. An information package on the study was provided and explained to potential participants, and written informed consent was obtained from all participants. The eligible patients were recruited within 72 hours of their hospital admission.
Data Collection and Measures
Baseline data on demographics and health and medical history were collected from medical records. The nutritional tools were administered by two RNs who received training and interrater reliability testing conducted by an experienced dietician. The interrater reliability was examined (kappa = 0.82 on 9 cases), and the results showed substantial agreement between the two RNs. Two nutrition measures were conducted, described below.
Nutrition screening was performed using the MST. It consists of two components: (a) unintentional weight loss in the past 6 months and (b) eating poorly because of a decreased appetite. Scoring between 0 and 5 identifies whether participants are at risk of malnutrition (score ⩾2) or not at risk of malnutrition (score of 0 or 1) (Ferguson et al., 1999). Nutrition assessment data were collected by using the SGA, which is one of the few nutritional assessment tools that has established reliability and validity in older adults (Christenson et al., 2002). The SGA was tested in the assessment of older outpatients 70 and older and was reported to have high validity (82% sensitivity) and interrater reliability (77.8%) (Ek, Unosson, Larsson, Ganowiak, & Bjurulf, 1996). In addition, the SGA has been further tested for predictive validity by Duerksen et al. (2000) by comparing its ability to predict mortality from nutritional status with other measurements of nutritional status in hospitalized patients 70 and older, which was similar to the current study. The interrater reliability demonstrated moderately good agreement (unweighted kappa = 0.48 ± 0.17) between the observers, and the results also showed a significant correlation between severe malnourishment and mortality (Duerksen et al., 2000).
The SGA includes two main areas: (a) a medical history, which assesses participants’ weight change, dietary intake, gastrointestinal symptoms, and functional impairment; and (b) a physical examination, which consists of assessment for loss of subcutaneous fat, muscle wasting, edema, and ascites (Detsky et al., 1987). Participants are characterized as being well nourished, moderately malnourished, or severely malnourished (Detsky et al., 1987).
Data analysis was performed using SPSS version 17. Descriptive analyses were conducted for all demographic variables. The kappa statistic was used to determine the proportion of agreement between the MST and SGA. The value of kappa varies from 0 to 1, with a value of <0.20 = poor, 0.20 to 0.40 = fair, 0.41 to 0.60 = moderate, 0.60 to 0.80 = substantial, and >0.81 = almost perfect (Landis & Koch, 1977). A contingency table was used to examine sensitivity (percentage of malnourished correctly identified), specificity (percentage of well nourished correctly identified), and predictive value (likelihood that the tool correctly predicts the presence or absence of malnutrition) of the MST in detecting patients at risk of malnutrition, compared with the SGA (Gibson, 2005). Statistical significance was reported at the p < 0.05 level (two tailed).
One hundred fifty-seven hospitalized patients ages 65 to 93 (mean age = 77.6, SD = 6.4 years) participated in the study. Patient characteristics are shown in Table 1. The majority of participants were women (77.1%), had an annual income of less than $30,000 (79%), and had between 7 to 12 years of education (33.1%). The most common diagnoses on admission were respiratory (39.5%) and cardiac (19.9%) diseases. The average number of risk factors for readmission was three (median = 3, range = 1 to 8) with more than half of the participants age 75 and older (67.5%), living alone (52.2%), and having multiple comorbidities (95.5%). Two patients were also receiving palliative care treatment when they were admitted, in addition to treatment for their acute medical condition. The median number of comorbidities was three (range = 1 to 8), and the most commonly reported conditions were related to cardiac diseases (78.3%), respiratory diseases (53.5%), and gastrointestinal problems (44.6%).
Table 1: Demographic Characteristics of the Participants (N = 157)
A total of 157 participants completed the MST and 155 completed the SGA, as 2 participants were discharged before the data collection was completed. According to the SGA, 79.4% of participants were well nourished and 20.6% malnourished (n = 31, including 30 moderately and 1 severely malnourished). Based on the MST, 27.4% (n = 43) of participants screened positively, as they had MST scores ⩾2, and 72.2% (n = 114) of participants were not at risk of malnutrition (Figure).
Figure. Prevalence of malnutrition in older adults at high risk of hospital readmission.SGA = Subjective Global Assessment; MST = Malnutrition Screening Tool.
With regard to the validation of the MST, a total of 30 participants were correctly identified as being malnourished (true positives), and 110 were correctly classified as being well nourished (true negatives). Two of the 32 participants (1.3% of 157 participants) who were assessed as being malnourished by the SGA were not detected by the MST (false negatives). Thirteen of the 123 participants (8.4% of 155 participants) assessed as well nourished by the SGA were identified as at risk of malnutrition by the MST (false positives). Table 2 displays the contingency table of nutrition risk (measured by the MST) compared to nutritional status (measured by the SGA). Comparison of the MST and SGA using the kappa statistic revealed a substantial agreement—kappa = 0.74, p < 0.001, 95% confidence interval (CI) [0.62, 0.86]—between the two tools.
Table 2: Contingency Table of Nutrition Risk (MST) Compared With Nutritional Status (SGA)
Using the SGA as the benchmark for the assessment of malnutrition, the MST achieved a high sensitivity of 94% and a specificity of 89%. The positive predictive value was 0.70 (the proportion of participants who were at risk of malnutrition and were malnourished), and the negative predictive value was 0.98 (the proportion of participants who were not at risk of malnutrition and were well nourished). Table 3 describes the numerical definitions of sensitivity, specificity, predictive value, prevalence, and study results. These results indicate that the MST was a valid tool in screening for risk of malnutrition among the study population.
Table 3: Numerical Definitions of Sensitivity, Specificity, Predictive Value, Prevalence, and Study Results
The current study demonstrated the validity of the MST compared with a full nutrition assessment using the SGA in older adults at high risk of hospital readmission. The MST was shown to be effective for nurses in identifying patients at risk of malnutrition when compared with the SGA, with high sensitivity (94%), specificity (89%), positive predictive value (70%), and substantial negative predictive value (98%). Additionally, the kappa statistic shows a substantial agreement—kappa = 0.74, p < 0.001, 95% CI [0.62, 0.86] —between these two methods.
These results are similar to previous MST validation studies conducted in acute and oncology outpatients (Ferguson et al., 1999; Isenring et al., 2006; Neelemaat et al., 2011). The findings particularly supported the original development of the MST in 408 hospital inpatients with an average age of 57.7 (SD = 16.5 years, age range = 9 to 94) (sensitivity = 93%, specificity = 93%, positive predictive value = 0.98, and negative predictive value = 0.73), compared with a full nutrition assessment by the SGA (Ferguson et al., 1999). Jones (2004) suggested that assessment of a tool’s validity is an ongoing process, and use of the tool in a different population required new validity. There was concern whether the MST would be appropriate for older adults at high risk of readmission, as it was originally developed in a younger population (mean age = 57.7, SD = 16.5 years). The current study, however, found that it was also valid in an older frail population at risk of readmission. In a recent study comparing the MST with the SGA in 285 aged care residents, the MST was found to be highly sensitive (84%) but have a lower specificity (66%) (positive predictive value = 0.65 and negative predictive value = 0.84), compared with the present study (Isenring, Bauer, Banks, & Gaskill, 2009). With the strong predictive values, the current study provides clear evidence that the MST performs well in older adults in the acute setting.
Other studies have used similar methods to validate other nutrition screening tools compared with the SGA (Kyle, Kossovsky, Karsegard, & Pichard, 2006; Pablo, Izaga, & Alday, 2003). However, the current study results demonstrate higher sensitivity and specificity compared with those studies. A study comparing three nutritional screening tools (Nutritional Risk Indicator, Malnutrition Universal Screening Tool, and Nutrition Risk Screening) with the SGA in 995 hospital inpatients with medical or surgical conditions attending a Swiss hospital found that the sensitivity was in the range of 43% to 62%, and specificity was in the range of 76% to 93% (Kyle et al., 2006). These results showed higher specificity than sensitivity, which indicates that these screening tools performed better in correctly identifying patients who were not malnourished than those at risk of malnutrition (Kyle et al., 2006). Although a 100% sensitivity and specificity would be ideal for a screening tool, in reality, this is generally not achievable and hence the need to correctly classify all malnourished patients (sensitivity) takes priority over misclassifying patients who are well nourished (specificity) (Capra, 2007).
The malnutrition prevalence was 20.6% according to the SGA in the current study, indicating that one in five older hospitalized patients had malnutrition. However, this result was lower than other rates reported in the literature when the SGA was applied. In Turkey, a study of 251 inpatients with mixed diagnoses (mean age = 49.59, SD = 15.3 years) found that 30% of patients were malnourished (Sungurtekin, Sungurtekin, Hanci, & Erdem, 2004). A much higher rate of malnutrition was reported in an Argentinian study that used the SGA to determine a malnutrition rate of 47.6% in 412 patients (mean age = 65) who were admitted to the general medical units (Baccaro et al., 2007).
Similarly, a higher prevalence was reported in Australian studies, with prevalence of malnutrition ranging from 30% to 42% in acute hospital care settings (Banks, Ash, Bauer, & Gaskill, 2007; Lazarus & Hamlyn, 2005). The lower malnutrition prevalence of the current study may reflect that our target population was different from that of previous studies, as this is the first published study that has explored the nutrition status in older adults at risk of hospital readmission. The main study is an RCT, which targets the population of older patients who are identified as at risk of readmission yet relatively healthy with reasonable functional ability and potentially able to live independently (Courtney et al., 2009). This group would particularly benefit from primary and secondary prevention in terms of early detection and effective interventions for malnutrition, which, in turn, may prevent any nutrition-related clinical complications.
The purpose of nutrition screening is to identify those patients at risk for malnutrition (American Dietetic Association, 1994). Early detection of malnutrition risk allows for appropriate intervention; however, it relies on validated nutrition screening tools (Isenring et al., 2009). Although many nutrition screening tools have been developed, few have been solidly validated (Jones, 2004). Examples of validated and commonly used nutrition screening tools in the Australian older adult population include the Mini Nutritional Assessment-Short Form (Rubenstein, Harker, Salvà, Guigoz, & Vellas, 2001), the Malnutrition Universal Screening Tool (Stratton et al., 2004), and the MST (Ferguson et al., 1999). It has been suggested that simple, accurate, and highly sensitive and specific screening tools are best in clinical practice (Ferguson et al., 1999). The simplicity and accuracy of the MST suggests it is easier to use than the other two methods, as it does not require calculations such as body mass index. Additionally, a quick and easy-to-use tool is an important consideration for nursing staff, given the time constraints and work-related pressures they face (Green & Watson, 2005). Furthermore, using the same nutrition screening approach such as the MST for all patients admitted to the hospital may shed light on improving identification of malnutrition, as nursing staff would be familiar with the method regardless of different settings. The MST is widely used in Australian teaching hospitals and has been consistently investigated and validated in more diverse samples of patients and, hence, there is the further advantage of using the MST over other screening tools.
A limitation of the study was that the samples used in the current study cannot be generalized to the older hospitalized population as a whole. People who had dementia and severe functional impairments were excluded from the study, which would potentially contribute to a higher rate of malnutrition. However, this validation study has achieved 90% power to detect discrepancy rates of 6.5% or higher as statistically significant at the two-tailed, 5% level, indicating a sufficient sample for this study. Although results of the current study suggest that the MST is a valid nutrition screening tool, further research investigating the predictive value of the MST in terms of length of stay and readmissions is recommended.
The MST demonstrates substantial sensitivity, specificity, and agreement with the SGA, indicating it can be used as a valid tool to identify malnutrition risk. These findings are particularly meaningful for clinical practice, as nursing staff can use the MST for routine screening to identify patients at risk of malnutrition, and this may prevent hospital-acquired malnutrition for acute hospitalized older adults. Further studies are required to determine the predictive validity of the MST in terms of length of stay and readmission for acute hospitalized older adults.
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Demographic Characteristics of the Participants (N = 157)
|Number of risk factors
||3 (1 to 8)
|Number of comorbidities
||3 (1 to 8)
| $30,000 to $60,000
| 1 to <7 years
| Completed primary school
| 7 to 12 years
| Completed high school
| Post-secondary school
| Tertiary education
| Respiratory disease
| Cardiac disease
| Orthopedic problem
| Gastrointestinal disorder
| Renal disease
| Skin problem
|Risk factors for readmission
| Multiple comorbidities
| Age >75
| Lives alone
| Poor self-rated health
| Multiple recent admissions
| Poor social support
| Functional impairment
| History of depression
Contingency Table of Nutrition Risk (MST) Compared With Nutritional Status (SGA)
|Positive (at risk)
||30 (true positive)
||13 (false positive)
|Negative (not at risk)
||2 (false negative)
||110 (true negative)
Numerical Definitions of Sensitivity, Specificity, Predictive Value, Prevalence, and Study Results
|Sensitivity = TP/(TP + FN)
||30/(30 + 2) = 0.94
|Specificity = TN/(FP + TN)
||110/(13 + 110) = 0.89
|Predictive value = (TP + TN)/(TP + FP + TN + FN)
||(30 + 110)/(30 + 13 + 110 + 2) = 0.90
|Positive predictive value = TP/(TP + FP)
||30/(30 + 13) = 0.70
|Negative predictive value = TN/(TN + FN)
||110/(110 + 2) = 0.98
|Prevalence = (TP + FN)/(TP + FP + TN + FN)
||(30 + 2)/(30 + 13 + 110 + 2) = 0.206