PURPOSE: Diabetes mellitus has been either the fourth or fifth leading cause of death among Taiwanese adults during 1995–2015. Older adults with diabetes are at higher risk of developing diabetic macrovascular and micro-vascular complications. The purpose of this study explored the self-management experiences of middle-aged and older adults with diabetes through a focus group. METHODS: Purposive sampling was used to recruit patients with diabetes from the metabolic outpatient clinics of medical centers and regional hospitals in Taiwan. Two focus groups, comprising a total of 23 participants, were employed to collect data, and group discussions were held a total of four times in an education room that was distant from clinical areas. RESULTS: Three themes were generated from analysis of the collected data: (1) “listening to the voice of the body and observing physical changes,” (2) “re-recognizing diabetes and challenges,” and (3) “self-management implementation dilemmas.” This study provided new insights into the experiences of middle-aged and older adults in Taiwan regarding their self-management of diabetes. CONCLUSION: Healthcare teams should be involved in the self-management education of patients with diabetes as early as possible to reduce patients' anxiety and to develop more patient-centered, culture-sensitive clinical skills. In addition to monitoring patients' self-management, healthcare professor should pay more attention to patients' successful adaptation to and coexistence with the disease.
Adult ; Ambulatory Care Facilities ; Anxiety ; Cause of Death ; Clinical Competence ; Delivery of Health Care ; Diabetes Mellitus ; Education ; Focus Groups ; Humans ; Middle Aged ; Patient Care Team ; Patient Education as Topic ; Self Care ; Taiwan ; Voice

Background: Shared decision making using patient decision aids (PtDAs) was established over a decade ago, but few studies have evaluated its efficacy in Asian countries. We therefore evaluated the application of PtDAs in a decision conflict between two muscle relaxant reversal agents, neostigmine and sugammadex, and sequentially analyzed the regional differences and operating room turnover rates. Methods: This multicenter, outcome-assessor-blind, randomized controlled trial included 3,132 surgical patients from two medical centers admitted between March 2020 and August 2020. The patients were randomly divided into the classical and PtDA groups for pre-anesthesia consultations. Their clinicodemographic characteristics were analyzed to identify variables influencing the choice of reversal agent. On the day of the pre-anesthesia consultation, the patients completed the four SURE scale (sure of myself, understand information, risk-benefit ratio, encouragement) screening items. The operating turnover rates were also evaluated using anesthesia records. Results: Compared with the classical group, the PtDA group felt more confident about receiving sufficient medical information (P < 0.001), felt better informed about the advantages and disadvantages of the medications (P < 0.001), exhibited a superior understanding of the benefits and risks of their options (P < 0.001), and felt surer about their choice (P < 0.001). Moreover, the PtDA group had a significantly greater tendency to choose sugammadex over neostigmine (P < 0.001). Conclusions PtDA interventions in pre-anesthesia consultations provided surgical patients with clear knowledge and better support. PtDAs should be made available in other medical fields to enhance shared clinical decision-making.

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.