Aging is associated with increased risk of frailty and malnutrition. However, food insecurity has rarely been highlighted in the elderly population, especially among the low income group. Thus, a cross-sectional study was conducted to determine the association between nutritional status, food insecurity and frailty among elderly in low income residences in Klang Valley. A total of 72 elderly individuals aged 60 years and above was selected (mean age 66 ± 6 years) through convenient sampling. Participants were interviewed to obtain information on socio-demographic, health status, food insecurity and cognitive status. Anthropometrics parameters and frailty assessments was measured using standard criteria. Results showed that 75.0% of the participants had abdominal obesity. Nearly half of the participants were overweight (41.7%), followed by normal (43.0%) and underweight (15.3%). With respect to food insecurity, most of them reported that they had enough food (93.1%). There were significant correlation (p < 0.05) between food insecurity with height (r = -0.263, p = 0.026). Most of the participants were pre-frail (58.3%), frail (27.8%) and followed by non-frail (13.9%). Calcium intake is inversely associated with frailty (t = -2.62, p = 0.011). In conclusion, food insecurity was not a problem, however, half of the subjects were overweight and pre-frail. Three out four subjects had abdominal obesity. There is a need to investigate further the pathogenesis of fat frail in this low income elderly population and formulate effective intervention strategies.
Aged ; Income

OBJECTIVE: The purpose of this study was to quantify lesions on chest radiographs in patients with severe acute respiratory syndrome (SARS) and analyze the severity of the lesions with clinical parameters. MATERIALS AND METHODS: Two experienced radiologists reviewed chest radiographs of 28 patients with SARS. Each lung was divided into upper, middle, and lower zones. A SARS-related lesion in each zone was scored using a four-point scale: zero to three. The mean and maximal radiographic scores were analyzed statistically to determine if the scorings were related to the laboratory data and clinical course. RESULTS: Forward stepwise multiple linear regression showed that the mean radiographic score correlated most significantly with the number of hospitalized days (p < 0.001). The second most significant factor was the absolute lymphocyte count (p < 0.001) and the third most significant factor was the number of days of intubation (p = 0.025). The maximal radiographic score correlated best with the percentage of lymphocytes in a leukocyte count (p < 0.001), while the second most significant factor was the number of hospitalized days (p < 0.001) and the third most significant factor was the absolute lymphocyte count (p = 0.013). The mean radiographic scores of the patients who died, with comorbidities and without a comorbidity were 11.1, 6.3 and 2.9, respectively (p = 0.032). The corresponding value for maximal radiographic scores were 17.7, 9.7 and 6.0, respectively (p = 0.033). CONCLUSION: The severity of abnormalities quantified on chest radiographs in patients with SARS correlates with the clinical parameters.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Biological Markers/blood ; Blood Gas Analysis/statistics & numerical data ; Female ; Humans ; Intubation, Intratracheal/statistics & numerical data ; Length of Stay ; Lung/*radiography ; Lymphocyte Count/statistics & numerical data ; Male ; Middle Aged ; Observer Variation ; Predictive Value of Tests ; Prognosis ; Retrospective Studies ; Severe Acute Respiratory Syndrome/blood/*diagnosis/mortality ; Severity of Illness Index ; Survival Analysis

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.