METHODS AND ANALYSIS: NPC patients will be required to complete a risk factor questionnaire after obtaining their informed consent. The risk factor questionnaire will be used to collect potential risk factors for malnutrition. Univariate and multivariate logistic regression analyses will be used to identify risk factors for malnutrition. A new nutritional assessment tool will be developed based on risk factors. The new tool's performance will be assessed by calibration and discrimination. The bootstrapping will be used for internal validation of the new tool. In addition, external validation will be performed by recruiting NPC patients from another hospital.
DISCUSSION: If the new tool is validated to be effective, it will potentially save medical staff time in assessing malnutrition and improve their work efficiency. Additionally, it may reduce the incidence of malnutrition and its adverse consequences.
STRENGTHS AND LIMITATIONS OF THIS STUDY: The study will comprehensively analyze demographic data, disease status, physical examination, and blood sampling to identify risk factors for malnutrition. Furthermore, the new tool will be systematically evaluated, and validated to determine their effectiveness. However, the restricted geographical range may limit the generalizability of the results to other ethnicities. Additionally, the study does not analyze subjective indicators such as psychology.
ETHICS AND DISSEMINATION: The ethical approval was granted by the Ethical Committee of the First Affiliated Hospital of Guangxi Medical University (NO. 2022-KT-GUI WEI-005) and the Second Affiliated Hospital of Guangxi Medical University (NO. 2022-KY-0752).
CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2300071550.
METHODS: NPC patients were recruited in this cross-sectional study, and they were divided into well-nourished and malnourished groups according to the Global Leadership Initiative on Malnutrition (GLIM). Potential risk factors were initially screened using univariate analysis (p
METHODS: A cross-sectional study was conducted among 260 children admitted to general medical wards. SGNA and anthropometric measurements were used as references. Kappa agreement, diagnostic values, and area under the curve (AUC) were analyzed to evaluate the diagnostic ability of the AND/ASPEN malnutrition diagnosis tool. Logistic binary regression was performed to determine the predictive ability of each malnutrition diagnosis tool on the length of hospital stay.
RESULTS: The AND/ASPEN diagnosis tool detected the highest malnutrition rate (41%) among the hospitalized children in comparison with the reference methods. This tool demonstrated fair specificity of 74% and sensitivity of 70% compared with the SGNA. It obtained a weak agreement in determining the presence of malnutrition by kappa (0.06-0.42) and receiver operating characteristic curve analysis (AUC = 0.54-0.72). The use of the AND/ASPEN tool obtained an odds ratio of 0.84 (95% CI, 0.44-1.61; P = 0.59) in predicting the length of hospital stay.
CONCLUSIONS: The AND/ASPEN malnutrition tool is an acceptable nutrition assessment tool for hospitalized children in general medical wards.
DESIGN: Scoping review.
SETTING: Systematic search using PubMed and Web of Science.
RESULTS: We identified twelve tools from seventy-four eligible publications. They were developed for Koreans (n 4), Bangladeshis (n 2), Iranians (n 1), Indians/Malays/Chinese (n 1), Japanese (n 3) and Chinese Americans (n 1). Most tools (10/12) were composed of a dish-based FFQ. Although the development process of a dish list varied among the tools, six studies classified mixed dishes based on the similarity of their characteristics such as food ingredients and cooking methods. Tools were validated against self-reported dietary information (n 9) and concentration biomarkers (n 1). In the eight studies assessing the differences between the tool and a reference, the mean (or median) intake of energy significantly differed in five studies, and 26-83 % of nutrients significantly differed in eight studies. Correlation coefficients for energy ranged from 0·15 to 0·87 across the thirteen studies, and the median correlation coefficients for nutrients ranged from 0·12 to 0·77. Dish-based dietary assessment tools were used in fifty-nine studies mainly to assess diet-disease relationships in target populations.
CONCLUSIONS: Dish-based dietary assessment tools have exclusively been developed and used for Asian-origin populations. Further validation studies, particularly biomarker-based studies, are needed to assess the applicability of tools.
Methods: Data from the National Health and Morbidity Survey (NHMS) 2018 was analysed. This survey applied a multistage stratified cluster sampling design to ensure national representativeness. Malnutrition was identified using a validated Mini Nutrition Assessment-Short Form (MNA-SF). Variables on sociodemographic, health status, and dietary practices were also obtained. The complex sampling analysis was used to determine the prevalence and associated factors of at-risk or malnutrition among the elderly.
Result: A total of 3,977 elderly completed the MNA-SF. The prevalence of malnutrition and at-risk of malnutrition was 7.3% and 23.5%, respectively. Complex sample multiple logistic regression found that the elderly who lived in a rural area, with no formal or primary level of education, had depression, Instrumental Activity of Daily Living (IADL) dependency, and low quality of life (QoL), were underweight, and had food insecurity and inadequate plain water intake were at a significant risk of malnutrition (malnutrition and at-risk), while Chinese, Bumiputra Sarawak, and BMI more than 25 kgm-2 were found to be protective.
Conclusions: Currently, three out of ten elderly in Malaysia were at-risk or malnutrition. The elderly in a rural area, low education level, depression, IADL dependency, low QoL, underweight, food insecurity, and inadequate plain water intake were at risk of malnutrition in Malaysia. The multiagency approach is needed to tackle the issue of malnutrition among the elderly by considering all predictors identified from this study.
METHODS: This cross-sectional study recruited children below 18 years old admitting into general paediatric ward in a public hospital. The PNST and Subjective Global Nutritional Assessment (SGNA) were performed on 100 children (64 boys and 36 girls). The objective measurements include anthropometry (z-scores for weight, height and body mass index), dietary history and biochemical markers were measured. These were used to classify malnutrition as per Academy of Nutrition and Dietetics/American Society of Parental and Enteral Nutrition (AND/ASPEN) Consensus Statement for identification of paediatric malnutrition and WHO growth standards for children. Cohen's kappa was computed to report the level of agreement.
RESULTS: The PNST identified 57% of hospitalized children as being at risk of malnutrition. In this study, there was a stronger agreement between PNST with AND/ASPEN malnutrition classification (k = 0.602) as when PNST was compared with WHO (k = 0.225) and SGNA (k = 0.431). The PNST shows higher specificity (85.29%) and sensitivity (78.79%) when compared with AND/ASPEN than with WHO malnutrition criteria (55.81% specificity and 66.67% sensitivity).
CONCLUSION: This study showed the usefulness of routine use of PNST for screening the malnutrition risk of hospitalized children in Malaysian tertiary hospital settings.
Methods: Post-stroke patients who attended the outpatient clinics in three hospitals of Peninsular Malaysia were enrolled in the study. The risk of malnutrition was assessed using the Malnutrition Risk Screening Tool-Hospital. Data including demographic characteristics, clinical profiles, dietary nutrients intake, body mass index (BMI) and hand grip strength were collected during the survey. The crude odds ratio (OR) and adjusted odds ratio (AOR) were reported for univariate and multivariate logistic regression analyses, respectively.
Results: Among 398 patients included in the study, 40% were classified as high-risk for malnutrition. In the multivariable logistic regression, tube feeding (AOR: 13.16, 95% confidence interval [CI]: 3.22-53.77), loss of appetite (AOR: 8.15, 95% CI: 4.71-14.12), unemployment (AOR: 4.26, 95% CI: 1.64-11.12), wheelchair-bound (AOR: 2.23, 95% CI: 1.22-4.09) and BMI (AOR: 0.87, 95% CI: 0.82-0.93) were found to be significant predictors of malnutrition risk among stroke patients.
Conclusion: The risk of malnutrition is highly prevalent among post-stroke patients. Routine nutritional screening, identification of risk factors, and continuous monitoring of dietary intake and nutritional status are highly recommended even after the stroke patient is discharged.