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.

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.