Purpose: The purpose of this study was to explore the experiences of caregivers in long-term care facilities as they implement palliative care. Although palliative care has been available in Taiwan for more than 30 years, it is often provided in hospitals, few models in the long-term care facilities. Methods: Semi-structured interviews using grounded theory methodology and purposive sampling. Two small long-term care facilities that had performed well in palliative care were selected from eastern Taiwan. A total of 12 caregivers participated in in-depth semi-structured face-to-face interviews. Results: Four major stages in the implementation of palliative care were identified: (1) feeling insecure, (2) clarifying challenges, (3) adapting to and overcoming the challenges, and (4) comprehending the meaning of palliative care. The core category of these caregivers as “the guardians at the end of life” reflects the spirit of palliative care. Conclusion This study demonstrates that successful palliative care implementation would benefit from three conditions. First, the institution requires a manager who is enthusiastic about nursing care and who sincerely promotes a palliative care model. Second, the institution should own caregivers who possess personality traits reflective of enthusiasm for excellence, unusual ambition, and a true sense of mission. Third, early in the implementation phase of the hospice program, the institution must have the consistent support of a high-quality hospice team.

Purpose: The purpose of this study was to explore the experiences of caregivers in long-term care facilities as they implement palliative care. Although palliative care has been available in Taiwan for more than 30 years, it is often provided in hospitals, few models in the long-term care facilities. Methods: Semi-structured interviews using grounded theory methodology and purposive sampling. Two small long-term care facilities that had performed well in palliative care were selected from eastern Taiwan. A total of 12 caregivers participated in in-depth semi-structured face-to-face interviews. Results: Four major stages in the implementation of palliative care were identified: (1) feeling insecure, (2) clarifying challenges, (3) adapting to and overcoming the challenges, and (4) comprehending the meaning of palliative care. The core category of these caregivers as “the guardians at the end of life” reflects the spirit of palliative care. Conclusion This study demonstrates that successful palliative care implementation would benefit from three conditions. First, the institution requires a manager who is enthusiastic about nursing care and who sincerely promotes a palliative care model. Second, the institution should own caregivers who possess personality traits reflective of enthusiasm for excellence, unusual ambition, and a true sense of mission. Third, early in the implementation phase of the hospice program, the institution must have the consistent support of a high-quality hospice team.

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.

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.