Risk Factors and Predictive Model for Mortality of Hospitalized COVID-19 Elderly Patients from a Tertiary Care Hospital in Thailand.

Background: Early detection of elderly patients with COVID-19 who are at high risk of mortality is vital for appropriate clinical decisions. We aimed to evaluate the risk factors associated with all-cause in-hospital mortality among elderly patients with COVID-19. Methods: In this retrospective study, the medical records of elderly patients aged over 60 who were hospitalized with COVID-19 at Thammasat University Hospital from 1 July to 30 September 2021 were reviewed. Multivariate logistic regression was used to identify independent predictors of mortality. The sum of weighted integers was used as a total risk score for each patient. Results: In total, 138 medical records of patients were reviewed. Four identified variables based on the odds ratio (age, respiratory rate, glomerular filtration rate and history of stroke) were assigned a weighted integer and were developed to predict mortality risk in hospitalized elderly patients. The AUROC of the scoring system were 0.9415 (95% confidence interval, 0.9033-0.9716). The optimized scoring system was developed and a risk score over 213 was considered a cut-off point for high mortality risk. Conclusions: A simple predictive risk score provides an initial assessment of mortality risk at the time of admission with a high degree of accuracy among hospitalized elderly patients with COVID-19.

Association between Gut Microbiota with Mild Cognitive Impairment and Alzheimer's Disease in a Thai Population.

BACKGROUND: Mild cognitive impairment (MCI) and Alzheimer's disease (AD) are common in older adults. Much recent work has implicated the connection between the gut and the brain via bidirectional communication of the gastrointestinal tract and the central nervous system through biochemical signaling. Altered gut microbiota composition has shown controversial results based on geographic location, age, diet, physical activity, psychological status, underlying diseases, medication, and drug use. OBJECTIVES: This study aimed to investigate the relationships of gut microbiota with MCI and AD. METHODS: 16S metagenome profiles from stool collection of participant groups (normal; n = 20, MCI; n = 12, AD; n = 20) were analyzed. The diagnosis of cognitive conditions was made by standard criteria consisting of clinical interviews, physical examinations, cognitive assessments, laboratory examinations, and neuroimaging by both structural neuroimaging and amyloid positron emission tomography scans. Correlations between medical factors with food frequency and the fecal microbiome were elucidated. RESULTS: A significant difference at the operational taxonomic unit level was observed. The significantly higher abundance of bacteria in nondementia patients belonged to the Clostridiales order, including Clostridium sensu stricto 1 (p < 0.0001), Fusicatenibacter (p = 0.0007), Lachnospiraceae (p = 0.001), Agathobacter (p = 0.021), and Fecalibacterium (p < 0.0001). In contrast, Escherichia-Shigella (p = 0.0002), Bacteroides (p = 0.0014), Holdemanella (p < 0.0001), Romboutsia (p = 0.001), and Megamonas (p = 0.047) were the dominant genera in the AD group. Left and right hippocampus and right amygdala volumes were significantly decreased in the AD group (p < 0.001) and significantly correlated with the groups of bacteria that were significantly different between groups. CONCLUSION: There was a relationship between the composition of the gut microbiome and neurodegenerative disorders, including MCI and AD. Reduction of Clostridiaceae and increases in Enterobacteriaceae and Bacteroides were associated with persons with MCI and AD, consistent with previous studies. The altered gut microbiome could be potentially targeted for the early diagnosis of dementia and the reduction of AD risk.

Prevalence and risk for malnutrition in older Thai people: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVES: Malnutrition is potentially preventable in older people, but with varied reported prevalence. We assessed its prevalence, assessment methods, and risk factors in older Thai people. METHODS AND STUDY DESIGN: Studies published from January 1, 2000, to September 30, 2020 were searched in Medline, EMBASE, Google Scholar, and local databases. A random-effects model was used to calculate pooled prevalence with subgroups analysis (setting of the patient, region). Forest plots displayed sensitivity and specificity for all nutritional screening tools validated against Mini Nutritional Assessment (MNA) with tests for heterogeneity. Publication bias was tested by funnel plot and Egger's test. RESULTS: 71 studies (total 23,788 subjects) were included where mean age was 65.5 to 78.3 years. The pooled prevalences of malnutrition were 10.4%, 6.1%, and 5.7% by body mass index (BMI), MNA, and MNA-Short Form (MNA-SF), respectively. At-risk of malnutrition prevalence was 42.6% using the MNA and 37.8% using the MNA-SF. The pooled prevalence of malnutrition by BMI <18.5 kg/m2 was 10.4% (95% CI 8.7-12.4). The pooled prevalence of malnutrition based on MNA was 6.1% (95% CI 3.8-9.4). It was highest among hospitalized patients and lowest in community-dwelling elders by both measures. Factors associated with malnutrition were female sex, advanced age, low education, living alone, living in rural areas, comorbidities, eating problems, and geriatric conditions. CONCLUSIONS: The pooled prevalence of elder malnutrition was 6-10%, depending on assessment method and study setting. Hospitalized older people were at increased risk of malnutrition. It might be ameliorated through community directed food systems.