Kuo-Hsuan Chang

Neuropsychiatric symptoms are prevalent in Parkinson's disease (PD) but remain incompletely characterized, particularly concerning sex-specific differences and the comparative performance of diagnostic tools. This study aimed to provide a comprehensive neuropsychiatric profile of a Taiwanese PD cohort, evaluate the diagnostic sensitivity of various assessment tools, and explore correlations with clinical features. We enrolled 92 PD patients and 57 healthy controls. Participants underwent a comprehensive assessment battery, including motor scales, cognitive measures, and mood inventories. Diagnoses of dementia and depression were established using standard criteria. Diagnostic tool performance was evaluated by calculating the sensitivity and specificity, and relationships between variables were assessed using Spearman correlation. Dementia was identified in 34.8% and depression in 28.3% of PD patients. Female patients had lower scores on the Mini-Mental State Examination (MMSE) (25.18 vs. 28.08, p = 0.003) and Montreal Cognitive Assessment (MoCA) (21.18 vs. 25.79, p = 0.001), and higher scores on the Hamilton Depression Rating Scale (HAM-D) (6.68 vs. 5.02, p = 0.046) and Neuropsychiatric Inventory (NPI) (3.95 vs. 1.79, p = 0.018) compared with male patients. For dementia diagnosis, the clinician-rated MMSE and MoCA demonstrated sensitivities exceeding 90%, whereas the caregiver-rated Clinical Dementia Rating (CDR) had a sensitivity of only 25%. For depression, the clinician-rated HAM-D (88.5% sensitivity) outperformed the self-reported Beck's Depression Inventory II (BDI-II) (65.4% sensitivity). Most neuropsychiatric assessments were significantly correlated with age, disease duration, and motor severity. These findings highlight a considerable neuropsychiatric burden in PD, with a distinct female-predominant vulnerability in cognitive and affective domains. The superior performance of clinician-administered assessments emphasizes their value in improving diagnostic certainty. These results support the routine, comprehensive, and sex-specific neuropsychiatric evaluations in the clinical management of PD.

Neuromyelitis optica spectrum disorder is a rare autoimmune inflammatory demyelinating disease that must be differentiated from multiple sclerosis. The impact of misclassification on these patients in Taiwan remains unclear. We conducted a hospital-based retrospective cohort study of neuromyelitis optica spectrum disorder patients using the Chang Gung Research Database from 2005 to 2021. This cohort included diagnostic serostatus data from anti-Aquaporin 4 antibody testing, providing insights into incidence, demographics, annual relapse rate, and initial misclassification as multiple sclerosis. To extend these findings, we applied the same methodology to a nationwide neuromyelitis optica spectrum disorder cohort (2006–2020) using Taiwan’s National Health Insurance Research Database, which does not include aquaporin 4 serostatus information; thus, the data made it impossible to distinguish seropositive from seronegative nationwide neuromyelitis optica spectrum disorder cases. Misclassification was calculated as the ratio of nationwide neuromyelitis optica spectrum disorder patients initially diagnosed with multiple sclerosis (MS) to the total nationwide neuromyelitis optica spectrum disorder cases in each cohort. In the Chang Gung Research Database cohort, we identified 193 seropositive nationwide neuromyelitis optica spectrum disorder patients, including four (2.1%) paediatric cases. Hospital incidence increased from 0.08 to 1.19 per 100 000 persons (2006–2021). Comorbidities were present in 32% of adults, with Sjögren’s syndrome as the most common (21.2%). Median annualized relapse rate in adults was 0.49 (range, 0.26–0.85) and was higher in the first 3 years post-diagnosis (0.67 versus 0.00; P < 0.01). The National Health Insurance Research Database cohort included 1892 neuromyelitis optica spectrum disorder patients, 92 (4.9%) paediatric. Incidence in adults rose from 0.25 to 0.84 per 100 000 persons, with a prevalence of 8.01 per 100 000 in 2020. Median annualized relapse rate was 0.36 (range, 0.07–60.83) for adults and 0.44 (range, 0.07–6.40) for paediatric patients. Misclassification as multiple sclerosis occurred in 55.0% Chang Gung Research Database and 63.5% National Health Insurance Research Database cohort. Misclassified patients were more likely to be younger, female, experience delayed antibody testing, have frequent hospitalizations, and suffer more relapses. Both hospital-based and nationwide cohorts revealed increasing neuromyelitis optica spectrum disorder incidence and prevalence in Taiwan. This early-phase elevation in relapse activity highlights the critical therapeutic window where intervention may most effectively prevent long-term disability. Greater clinical awareness and close follow-up may improve neuromyelitis optica spectrum disorder diagnosis and management.

Hospital-acquired infections (HAIs) are serious complication for patients with acute ischemic stroke (AIS), often resulting in poor functional outcomes. However, no existing model can specifically predict HAI in AIS patients. Therefore, we employed the Gradient Boosting matching learning algorithm to establish predictive models for HAI occurrence in AIS patients and poor 30-day functional outcomes (modified Rankin Scale > 2) in AIS patients with HAI by analyzing electronic health records from 6560 AIS patients. Model performance was evaluated through internal cross-validation and external validation using an independent cohort of 3521 AIS patients. The established models demonstrated robust predictive performance for HAI in AIS patients, achieving area under the receiver operating characteristic curves (AUROCs) of 0.857 ± 0.008 during internal validation and 0.825 ± 0.002 during external validation. For AIS patients with HAI, the second model effectively predict poor 30-day functional outcomes, with AUROCs of 0.905 ± 0.009 during internal validation and 0.907 ± 0.002 during external validation. In conclusion, machine learning models effectively identify the HAI occurrence and predict poor 30-day functional outcomes in AIS patients with HAI. Future prospective studies are crucial for validating and refining these models for clinical application, as well as for developing an accessible flowchart or scoring system to enhance clinical practices.

Background: Patients with Parkinson's disease (PD) present progressive deterioration in both motor and non‐motor manifestations. However, the absence of clinical biomarkers for disease progression hinders clinicians from tailoring treatment strategies effectively.Objectives: To identify electroencephalography (EEG) biomarker that can track disease progression in PD.Methods: A total of 116 patients with PD were initially enrolled, whereas 63 completed 2‐year follow‐up evaluation. Fifty‐eight age‐ and sex‐matched healthy individuals were recruited as the control group. All participants underwent EEG and clinical assessments. Long‐range temporal correlations (LRTC) of EEG data were analyzed using the detrended fluctuation analysis.Results: Patients with PD exhibited higher LRTC in left parietal θ oscillations (P = 0.0175) and lower LRTC in centro‐parietal γ oscillations (P = 0.0258) compared to controls. LRTC in parietal γ oscillations inversely correlated with changes in Unified Parkinson's Disease Rating Scale (UPDRS) part III scores over 2 years (Spearman ρ = −0.34, P = 0.0082). Increased LRTC in left parietal θ oscillations were associated with rapid motor progression (P = 0.0107), defined as an annual increase in UPDRS part III score ≥3. In cognitive assessments, LRTC in parieto‐occipital α oscillations exhibited a positive correlation with changes in Mini‐Mental State Examination and Montreal Cognitive Assessment scores over 2 years (Spearman ρ = 0.27–0.38, P = 0.0037–0.0452).Conclusions: LRTC patterns in EEG potentially predict rapid progression of both motor and non‐motor manifestations in PD patients, enhancing clinical assessment and understanding of the disease. © 2024 International Parkinson and Movement Disorder Society.