Journal of the Formosan Medical Association最新文献

Background: With global aging, neurodegenerative disorders, particularly Alzheimer's disease (AD) and Parkinson's disease (PD), have become major public health concerns. However, the evaluation of their burden trends in Taiwan is lacking. We assessed temporal and geographic trends in the burden of AD with other dementias and PD in Taiwan from 2000 to 2021, stratified by age, sex, and region.

Methods: Data from Taiwan's National Health Insurance (NHI) and National Death Registry were analyzed for 2000-2021. Case definitions for Alzheimer's disease with other dementias (ADODs) and Parkinson's disease (PD) followed Global Burden of Disease 2021 criteria and were identified using ICD codes. Fatal burden was assessed using the National Death Registry. Disease burden was quantified by years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs), with additional geographic mapping to visualize regional variations.

Results: Between 2000 and 2021, age-standardized prevalence rose sharply for ADODs (+885 %) and PD (+394 %), while mortality increased by 116 % and 31 %, respectively; incidence remained stable. DALY rates grew by 258 % for ADODs and 86 % for PD, mainly driven by rising YLDs. The greatest burden was among those aged ≥80 years, with ADODs and PD DALYs increasing by 357 % and 147 %, respectively. Geographically, the highest age-standardized DALYs for ADODs and PD were found in the Southern Taiwan.

Conclusion: The prevalence and health burden of ADODs and PD have increased substantially in Taiwan despite declining incidence, posing growing challenges to the aging population.

Background: Obstructive sleep apnea (OSA) is a prevalent sleep disorder, Drug-induced sleep endoscopy (DISE) offers direct visualization of upper airway obstruction and collapse. Unlike awake fiberoptic examinations, DISE allows for direct visualization of airway collapse during sleep, simulating natural sleep conditions under intravenous anesthesia. This is crucial for identifying obstruction sites and patterns, especially before surgical interventions or after failed CPAP treatment. While the VOTE classification system is commonly used to assess collapse severity at different anatomical sites. It relies on manual, time-consuming and subjective scoring. This study aims to develop an automated learning-based analysis system to identify the velum and epiglottis and their obstruction ratios from continuous DISE images in the supine position. This approach could provide a more efficient and objective assessment of upper airway collapse during DISE.

Methods: We developed an AI-assisted system that performs anatomical region classification, continuous obstruction rate estimation, and velum collapse pattern recognition across all four VOTE regions. A total of 165 DISE videos were collected, with 157 used for model training and 8 for clinical validation. From these videos, 2295 frames were extracted and annotated by physicians with region labels and obstruction grades.To enhance performance in anatomically ambiguous regions, such as the tongue base and oropharynx, the system incorporates depth-aware compensation and distance-based frame selection.

Results: The model achieved high prediction accuracy in anatomical classification (Velum 82.47 %, Epiglottis: 85.48 %) significantly improved obstruction grading in complex regions, increasing accuracy from 51.78 % to 78.32 % in the tongue base and from 48.38 % to 77.66 % in the oropharynx. Velum collapse patterns were identified with 81.07 % accuracy. The use of continuous obstruction rates provided more detailed and consistent assessments than conventional grading.

Background: Blood donation volumes fluctuate sharply with pandemics, disasters, holidays, and promotional campaigns, creating risks of shortages and waste. Traditional statistical models often fail to capture such event-driven shocks. This study proposes a semantics-enhanced forecasting framework that integrates structured donation data with unstructured news and social-media text.

Methods: We benchmark multiple topic-modeling methods-Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Correlated Topic Model (CTM), and BERTopic-and select BERTopic for its superior coherence-diversity tradeoff. Document-level topic distributions are aggregated into daily vectors, compressed using Principal Component Analysis (PCA), and transformed into lagged predictors. These semantic features are combined with structured donation data and used to train several forecasting models, including Gradient Boosting, XGBoost, LightGBM, CatBoost, Random Forest, generalized additive models (GAM), and Seasonal ARIMA with exogenous regressors (SARIMAX).

Results: Using data from Taiwan's four regional blood centers (2024-2025), semantic topic features substantially improved predictive accuracy. Gradient Boosting achieved the best performance, yielding a 28 % reduction in root mean square error (RMSE) and a 0.33 increase in coefficient of determination (R²). Robustness was confirmed using Diebold-Mariano tests, paired t-tests, Wilcoxon tests, and sign tests. SHapley Additive exPlanations (SHAP) analysis revealed stable influential predictors, including campaign indicators, mobile donation sites, donor age groups, and semantic topics related to epidemics, disasters, and blood-shortage appeals. Embedding forecasts into a perishable-inventory Mixed-Integer Linear Programming (MILP) model-using predicted daily capacities as constraints-achieved a 96.9 % service level, compared with 98.9 % under an Oracle benchmark with perfect foresight.

Conclusions: The proposed "predict-then-optimize" pipeline enhances blood-supply resilience and can generalize to other healthcare or perishable-supply contexts characterized by event-driven volatility.

Background: Purpose: Brivaracetam (BRV), an analog of levetiracetam (LEV), is designed with a higher affinity and selectivity for synaptic vesicle protein 2A (SV2A) than LEV. This study aimed to assess the efficacy and tolerability of shifting anti-seizure medication (ASM) from LEV to BRV in pediatric patients with epilepsy. Factors following the switch were analyzed.

Methods: A retrospective study included epilepsy patients under the age of 20 who transitioned from LEV to BRV between April 2022 and April 2023. Data at baseline (T0) and 6 months post-BRV (T1) were evaluated. Responders (seizure reduction ≥50 %) and non-responders were compared for factors linked to outcomes.

Results: Among 121 patients with epilepsy (mean age 10.8 ± 4.9 years), 59.5 % were responders at T1. EEG features with focal epileptiform discharge (p = 0.037) and lower average ASM amounts (p = 0.014) significantly related to better outcomes. There was no significant difference between responders and non-responders in age, gender and etiology of seizure. Importantly, in non-seizure adverse effects, 60 % reported improved tolerability, particularly in mood, attention, and irritability. Nevertheless, 19 % experienced worsened adverse effects during T1 and switch back to LEV.

Conclusion: BRV is a promising alternative ASM for pediatric patients intolerant to LEV, especially with focal and non-drug-resistant seizures. Beyond seizure control, the improved tolerability profile, especially regarding behavioral side effects, may enhance developmental functioning and quality of life. These findings guide effective switching from LEV to BRV to improve outcomes.