PURPOSE: Whether tamoxifen affects the risk of neurodegenerative disease is controversial. This nationwide population-based study investigated the risk of Parkinson's disease (PD) associated with tamoxifen treatment in female patients with breast cancer using Taiwan's National Health Insurance Research Database. METHODS: A total of 5,185 and 5,592 female patients with breast cancer who did and did not, respectively, receive tamoxifen treatment between 2000 and 2009 were included in the study. Patients who subsequently developed PD were identified. A Cox proportional hazards model was used to compare the risk of PD between the aforementioned groups. RESULTS: Tamoxifen did not significantly increase the crude rate of developing PD in female patients with breast cancer (tamoxifen group, 16/5,169; non-tamoxifen group, 11/5,581; p=0.246). Tamoxifen did not significantly increase the adjusted hazard ratio (aHR) for subsequently developing PD (aHR, 1.310; 95% confidence interval [CI], 0.605–2.837; p=0.494). However, tamoxifen significantly increased the risk of PD among patients followed up for more than 6 years (aHR, 2.435; 95% CI, 1.008–5.882; p=0.048). CONCLUSION: Tamoxifen treatment may increase the risk of PD in Taiwanese female patients with breast cancer more than 6 years after the initiation of treatment.
Asian Continental Ancestry Group* ; Breast Neoplasms* ; Breast* ; Female* ; Humans ; National Health Programs ; Neurodegenerative Diseases ; Parkinson Disease* ; Proportional Hazards Models ; Tamoxifen*

Recombinant tissue plasminogen activator (rt-PA) is the most effective treatment for acute ischemic stroke and the exclusion criteria of rt-PA has been revised to extend its application. However, in Taiwan, National Health Insurance (NHI) did not follow the latest international consensus due to safety concerns. The present study investigated whether extending the application of rt-PA in Taiwan was safe and effective. The medical records from the Shuang Ho hospital stroke registry between August 2009 and December 2016 were retrospectively reviewed. Post rt-PA intracranial hemorrhage (ICH) and modified Rankin Scale (mRS) score at 3-month after stroke were the primary and secondary outcomes, respectively. Differences were analyzed through Fisher’s exact test and Student’s t test. A p-value of <0.05 was considered statistically significant. Overall, there were 243 patients categorized into two groups: NHI exclusion criteria adherence (n = 160) and non-adherence (n = 83). There was no significant difference in the risk of post rt-PA ICH (12.50% in adherence group, 4.82% in non-adherence group, p=0.07). Among the non-adherence group, 10 patients breached the latest international exclusion criteria and none of them experienced post rt-PA ICH. However, among patients with moderately severe stroke, the odds of mRS < 2 at 3-month were significantly lower in non-adherence group. This study demonstrated that extending administration of rt-PA in Taiwan was safe but the functional outcome after moderate stroke was not as favorable as adherence group. Old age, long onset-to-treatment time and less efficacy of lower dose of rt-PA were the possible factors for the difference in outcome.

OBJECTIVES: The aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion. METHODS: A data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1. RESULTS: Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. CONCLUSIONS: Our study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.
Area Under Curve ; Costs and Cost Analysis ; Dataset ; Decision Trees ; Diagnosis-Related Groups ; Financial Management ; Forests ; Humans ; Length of Stay ; Logistic Models ; Machine Learning ; Methods ; Sensitivity and Specificity ; Spinal Fusion ; Support Vector Machine ; Taiwan

Background: The risk and benefit of tissue plasminogen activator (tPA) for aged>80 years with acute ischemic stroke (AIS) are controversial. In this study, we investigated the safety and efficacy of tPA in this population and utilized the artificial neural network (ANN) to established outcome predictive models. Methods: We retrospectively reviewed the stroke registry data of patients with AIS, aged >80 years who arrived at the hospital within 3 hours from the onset of symptoms. The characteristics and the outcomes, presented as modified Rankin Scale (mRS), and mortality rate at 3 months between the tPA-treated and non-tPA groups were analyzed. An ANN algorithm was applied to establish predictive models. Results: A total of 80 patients aged>80 years with AIS were identified, and 49 of them received tPA. After adequate training, our ANN models accurately predicted the outcomes with the area under the receiver operating characteristic curves of 0.974, and a low error to predict the mRS score at 3 months. After applying our prediction model to those in the non-tPA group, we demonstrated the potential benefits in those patients if they had undergone tPA therapy. Conclusions: Our results show that ANN can be a potentially useful tool for predicting the treatment outcomes of tPA. Such novel machine learning-based models may help with therapeutic decision making in clinical settings.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.