Sarcopenia and intrinsic capacity (IC) declines pose significant challenges to healthy aging, particularly in the rapidly growing octogenarian population. This study aimed to elucidate the relationship between sarcopenia and declines in IC across multiple cohorts of community-dwelling older adults. Data from four Taiwanese cohorts were analyzed. Sarcopenia was diagnosed based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria (algorithm 1: categorized as either having possible sarcopenia or not (robust); algorithm 2: categorized as robust, possible sarcopenia or sarcopenia). IC was operationalized using the World Health Organization's Integrated Care for Older People (ICOPE) framework (step 1 and step 2), encompassing six domains: locomotion, vitality, vision, hearing, cognition, and psychological well-being. Multivariable logistic regression models were adopted to assess the association between sarcopenia and IC decline. Among 599 octogenarians (median age 82.2 years, 54.8% male), the prevalence of possible sarcopenia (algorithm 1) was 64.6%. When adopting algorithm 2, the prevalence of possible sarcopenia and sarcopenia was 46,2% and 32.1%, respectively. After adjusting for covariates, participants with possible sarcopenia or sarcopenia (algorithm 2) were more likely to exhibit declines in vitality (ICOPE Step 1: possible sarcopenia aOR 3.65, sarcopenia aOR 4.74; ICOPE Step 2: possible sarcopenia aOR 5.11, sarcopenia aOR 14.77) and cognition (ICOPE Step 1: possible sarcopenia aOR 2.40, sarcopenia aOR 2.12; ICOPE Step 2: possible sarcopenia aOR 2.02, sarcopenia aOR 2.51) compared to robust individuals. This study underscores the robust association between sarcopenia and declines in vitality and cognition among octogenarians, highlighting the importance of sarcopenia screening and management in promoting healthy longevity in this vulnerable population.

(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.

Early integration of palliative care for terminally ill cancer and non-cancer patients improves quality of life. However, there are sparse data on results of palliative care consultation services (PCCS) between cancer and non-cancer patients. In this 9-year observational study, data were collected from the Hospice-Palliative Clinical Database (HPCD) of Taichung Veterans General Hospital (TCVGH). Terminally ill cancer and non-cancer patients who received PCCS during 2011 to 2019 were enrolled. Trend analysis was performed to evaluate differences in outcomes of PCCS, including duration of PCCS, the awareness of disease of patients and families before and after PCCS, status of PCCS termination, and DNR declaration before and after PCCS among cancer and non-cancer patients throughout study period. In total, 5223 cancer patients and 536 non-cancer patients received PCCS from 2011 to 2019. The number of people who received PCCS increased stably over the decade, both for cancer and non-cancer patients. The average duration of PCCS for cancer and non-cancer patients was 21.4 days and 18.4 days, respectively. Compared with non-cancer patients, cancer patients had longer duration of PCCS, less DNR declaration (82% vs. 98%, respectively), and more transfers to the palliative care unit (17% vs. 11%, respectively), or for palliative home care (12% vs.8%, respectively). Determinants of late referral to PCCS includes age (OR 0.992, 95% CI 0.987–0.996), DNR declaration after PCCS (OR 1.967, 95% CI 1.574–2.458), patients’ awareness after PCCS (OR 0.754, 95% CI 0.635–0.895), and status of PCCS termination. This 9-year observational study showed that the trend of PCCS among cancer and non-cancer patients had changed over the duration of the study, and early integration of PCCS to all patients is essential for both cancer and non-cancer patients.

Background: The prevalence of diabetes is increasing worldwide, particularly in the Pacific Ocean island nations. Although machine learning (ML) models and data mining approaches have been applied to diabetes research, there was no study utilizing ML models to predict diabetes incidence in Taiwan. We aimed to predict the onset of diabetes in order to raise health awareness, thereby promoting any necessary lifestyle modifications and help mitigate disease burden. Methods: The research dataset used in the study was retrieved from the Clinical Data Center of Taichung Veterans General Hospital. We collected data from the available electronic health records with a total of 33 items being employed for model construction. Individuals with diabetes and those with missing data were excluded. Ultimately, 6687 adults were included in the final analysis, where we implemented three different ML algorithms, including logistic regression (LR), random forest (RF) and extreme gradient boosting (XGBoost) in order to predict diabetes. Results: The top five important factors involved in the prediction model were glycated hemoglobin (HbA1c), fasting blood glucose, weight, free thyroxine (fT4), and triglycerides (TG). Notably, random forest, logistic regression, and XGBoost reached 99%, 99%, and 98% accuracy, respectively. fT4 seems to be one of the significant features in predicting the onset of diabetes. Moreover, this would be the first study using machine learning models to predict diabetes that has demonstrated the importance of thyroid hormone. Conclusions: A total of 33 items were able to be put into the machine learning model in order to predict diabetes with promising accuracy. In comparison to prior studies on machine learning models, this study not only identified similar key factors for predicting diabetes but also highlighted the significance of thyroid hormones, a factor that was previously overlooked. Moreover, it highlighted the relevance of predicting type 2 diabetes using more affordable methods, which would be useful for clinical healthcare professionals and endocrinologists who apply the models to clinical practice.

Several dimensional impairments regarding Comprehensive Geriatric Assessment (CGA) have been shown to be associated with the prognosis of older patients. The purpose of this study is to investigate mortality prediction factors based upon clinical characteristics and test in CGA, and then subsequently develop a prediction model to classify both short- and long-term mortality risk in hospitalized older patients after discharge. A total of 1565 older patients with a median age of 81 years (74.0–86.0) were consecutively enrolled. The CGA, which included assessment of clinical, cognitive, functional, nutritional, and social parameters during hospitalization, as well as clinical information on each patient was recorded. Within the one-year follow up period, 110 patients (7.0%) had died. Using simple Cox regression analysis, it was shown that a patient’s Length of Stay (LOS), previous hospitalization history, admission Barthel Index (BI) score, Instrumental Activity of Daily Living (IADL) score, Mini Nutritional Assessment (MNA) score, and Charlson’s Comorbidity Index (CCI) score were all associated with one-year mortality after discharge. When these parameters were dichotomized, we discovered that those who were aged ≥90 years, had a LOS ≥ 12 days, an MNA score < 17, a CCI ≥ 2, and a previous admission history were all independently associated with one-year mortality using multiple cox regression analyses. By applying individual scores to these risk factors, the area under the receiver operating characteristics curve (AUC) was 0.691 with a cut-off value score ≧ 3 for one year mortality, 0.801 for within 30-day mortality, and 0.748 for within 90-day mortality. It is suggested that older hospitalized patients with varying risks of mortality may be stratified by a prediction model, with tailored planning being subsequently implemented.

We assessed the characteristics and perception of telephone appointments among outpatients and medical staff during the COVID-19 pandemic in Taiwan. Our survey was performed by giving self-administered questionnaires to the enrollees. Basic socioeconomic status data were collected. We used a valid and reliable telehealth usability questionnaire (TUQ) to assess the telemedicine experience among outpatients and medical staff. Only outpatients with chronic illness and who had regular visits before the pandemic were enrolled. We delivered the questionnaire survey to participants who used telephone appointments from 20 May 2021 to 31 July 2021 in Taichung Veterans General Hospital. A total of 471 outpatients and 203 medical staff completed the survey. Most of the respondents were aged 30–69, college-educated, women, and married. Outpatients have higher scores in all dimensions of TUQ than medical staff, especially in the dimensions of ease of use and effectiveness. Age, gender, education, and marriage have no significant associations in the medical staff group. In the outpatient group, gender is the only significant factor in the six dimensions of TUQ. We found a significant disparity in the perception gap of telemedicine among outpatient and medical staff. Outpatients are satisfied with telephone appointments during the COVID-19 pandemic, but medical staff are concerned about the ease of use and effectiveness.