White matter hyperintensity determines ischemic stroke severity in symptomatic carotid artery stenosis.

INTRODUCTION: The aim of this study is to investigate the influence of white matter hyperintensity (WMH) on stroke severity and prognosis in patients with symptomatic carotid artery stenosis. METHODS: Patients with symptomatic carotid artery stenosis were retrieved from the Samsung Medical Center stroke registry from January 2011 to December 2016. Stroke severity was categorized into three levels according to National Institutes of Health Stroke Scale (NIHSS): transient ischemic attack (TIA) or transient symptoms with infarction (TSI), mild stroke, and moderate to severe stroke. WMH volume was measured with medical image processing and visualization. The clinical outcome was assessed using the modified Rankin scale on the 90th day from which the latest onset of the neurological symptom. Logistic regression was used to predict stroke severity, and ordinal regression was used to compare the clinical outcome. RESULTS: Among 158 patients, the numbers of patients with TIA or TSI, mild stroke, and moderate to severe stroke were 48 (30.4%), 59 (37.3%), and 51 (32.3%), respectively. The larger WMH volume was associated with moderate to severe strokes (TIA/TSI vs. moderate to severe strokes, odds ratio (OR) 2.318, 95% confidence interval (CI) 1.194-4.502, p = 0.007; mild vs. moderate to severe strokes, OR 1.972, 95% CI 1.118-3.479, p = 0.013). Patients with larger volume of WMH showed poorer clinical outcome (cutoff value: 9.71 cm3, OR 2.099, 95% CI 1.030-4.311, p = 0.042). CONCLUSION: Our study showed that larger WMH volume is associated with more severe stroke and poorer prognosis in patients with symptomatic carotid artery stenosis.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes.

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

Daily Exercise Patterns and Their Differences between Parkinson's Disease Patients with and without Postural Instability.

Background: Due to the clinical impact of exercise in patients with Parkinson's disease (PD), management should include personalized and effective exercises according to patient's PD stage. We investigated the detailed exercise behaviors of patients with mild to advanced PD and compared their patterns between PD with and without postural instability (PI). Methods: We enrolled PD patients from September to December 2019. Clinical data on parkinsonism, exercise behaviors, and Physical Activity Scale of the Elderly (PASE) scores were collected and compared between mild PD without PI (Hoehn-Yahr (HY) stages 1 and 2) and advanced PD with PI (HY stages 3 and 4). Results: In total, 263 PD patients were recruited. The mean exercise frequency was 4.7 ± 2.1 times/week, and the average duration was 7.8 ± 6.7 hours/week. The most common exercise was an aerobic exercise (71.9%) of mild-to-moderate intensity, with active walking being the most common (49.0%). The mild PD patients demonstrated a higher duration and intensity of exercise and more physical activity than the advanced PD patients. However, the frequency of exercise was not significantly different between the two groups. The PASE score was significantly higher in mild PD patients than in advanced PD patients (p < 0.001). Conclusion: PD patients focused mostly on aerobic exercises, especially active walking. With the disease progression, the amount and intensity of exercise decreased while frequency remained. Higher intensity of exercise is needed in the mild PD group, while the advanced PD group requires the increment of duration for each exercise session.

10-Year Retrospective Review of the Etiologies for Meningitis With Elevated Adenosine Deaminase in Cerebrospinal Fluid: Etiologies Other Than TB.

Purpose: An elevated adenosine deaminase (ADA) level in the cerebrospinal fluid (CSF) is considered a reliable marker of tuberculous meningitis (TBM). However, CSF-ADA levels can also be elevated in other diseases. We aimed to find the most common diagnosis of patients with elevated CSF-ADA levels for the last 10 years. Methods: We retrospectively investigated the diagnoses of all patients with elevated CSF-ADA (ADA ≥ 10 IU/L) levels between 2010 and 2019 at the Samsung Medical Center. Definite TBM was defined based on microbiological evidence. Clinical TBM was defined based on the brain imaging and response to the standard TB treatment. We compared the laboratory characteristics of the three most common diagnoses. Results: CSF-ADA levels were elevated in 137 (5.6%) of 2,600 patients. The most common diagnoses included hematologic malignancy (HM; n = 36, 26.2%), TBM (n = 26, 19.0%), and viral meningitis (VM; n = 25, 18.2%). CSF-ADA levels did not differ significantly between TBM [median (interquartile range (IQR)), 20.2 IU/L (13.8-29.3)] and HM [16.5 (12.8-24.0)]. However, CSF-ADA levels were lower in VM [14.0 (11.0-16.1)] than in TBM (p = 0.027). Lymphocyte-dominant pleocytosis was more common in VM [77.0% (70.8-81.5)] than in TBM [16.0 (3.0-51.0), p = 0.015] or HM [36.0 (10.0-72.0); p = 0.032]. Interestingly, the CSF characteristics of clinical TBM were similar to those of VM but not definite TBM. Conclusion: The most common diagnoses with elevated CSF-ADA levels were HM, followed by TBM and VM. Clinicians should carefully consider the differential diagnoses in patients with elevated CSF-ADA levels, especially those in the early stage of meningitis without microbiological evidence for TBM.

Differential diagnosis between Parkinson's disease and atypical parkinsonism based on gait and postural instability: Artificial intelligence using an enhanced weight voting ensemble model.

BACKGROUND: Parkinsonian diseases and cerebellar ataxia among movement disorders, are representative diseases which present with distinct pathological gaits. We proposed a machine learning system that can differentiate Parkinson's disease (PD), cerebellar ataxia and progressive supranuclear palsy Richardson syndrome (PSP-RS) based on postural instability and gait analysis. METHODS: We screened 1467 gait (GAITRite) and postural instability (Pedoscan) analyses performed in Samsung Medical Center from January 2019 to December 2020. PD, probable PSP-RS, and cerebellar ataxia (i.e., probable MSA-C, hereditary ataxia, and sporadic adult-onset ataxia) were included in the study. The gated recurrent units for GaitRite and the deep neural network for Pedoscan were applied. The enhanced weight voting ensemble (EWVE) method was applied to incorporate the two modalities. RESULTS: We included 551 PD, 38 PSP-RS, 113 cerebellar ataxia and among them, 71 were MSA-C. Pedoscan-based and Gait-based model showed high sensitivity but low specificity in differentiating atypical parkinsonism from PD. The EWVE showed significantly improved specificity and reliable performance in differentiation between PD vs. ataxia patients (AUC 0.974 ± 0.036, sensitivity 0.829 ± 0.217, specificity 0.969 ± 0.038), PD vs. MSA-C (AUC 0.975 ± 0.020, sensitivity 0.823 ± 0.162, specificity 0.932 ± 0.030) and PD vs. PSP-RS (AUC 0.963 ± 0.028, sensitivity 0.555 ± 0.157, specificity 0.936 ± 0.031). CONCLUSION: We proposed reliable Pedoscan-based, Gait-based and EWVE model in differentiating gait disorders by integrating information from gait and postural instability. This model can provide diagnosis guidelines to primary caregivers and assist in differential diagnosis of PD from atypical parkinsonism for neurologists.

Detection and differentiation of ataxic and hypokinetic dysarthria in cerebellar ataxia and parkinsonian disorders via wave splitting and integrating neural networks.

Dysarthria may present during the natural course of many degenerative neurological conditions. Hypokinetic and ataxic dysarthria are common in movement disorders and represent the underlying neuropathology. We developed an artificial intelligence (AI) model to distinguish ataxic dysarthria and hypokinetic dysarthria from normal speech and differentiate ataxic and hypokinetic speech in parkinsonian diseases and cerebellar ataxia. We screened 804 perceptual speech analyses performed in the Samsung Medical Center Neurology Department between January 2017 and December 2020. The data of patients diagnosed with parkinsonian disorders or cerebellar ataxia were included. Two speech tasks (numbering from 1 to 50 and reading nine sentences) were analyzed. We adopted convolutional neural networks and developed a patch-wise wave splitting and integrating AI system for audio classification (PWSI-AI-AC) to differentiate between ataxic and hypokinetic speech. Of the 395 speech recordings for the reading task, 76, 112, and 207 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. Of the 409 recordings of the numbering task, 82, 111, and 216 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. The reading and numbering task recordings were classified with 5-fold cross-validation using PWSI-AI-AC as follows: hypokinetic dysarthria vs. others (area under the curve: 0.92 ± 0.01 and 0.92 ± 0.02), ataxia vs. others (0.93 ± 0.04 and 0.89 ± 0.02), hypokinetic dysarthria vs. ataxia (0.96 ± 0.02 and 0.95 ± 0.01), hypokinetic dysarthria vs. none (0.86 ± 0.03 and 0.87 ± 0.05), and ataxia vs. none (0.87 ± 0.07 and 0.87 ± 0.09), respectively. PWSI-AI-AC showed reliable performance in differentiating ataxic and hypokinetic dysarthria and effectively augmented data to classify the types even with limited training samples. The proposed fully automatic AI system outperforms neurology residents. Our model can provide effective guidelines for screening related diseases and differential diagnosis of neurodegenerative diseases.

Validation of Blood Pressure Measurement Using a Smartwatch in Patients With Parkinson's Disease.

Objectives: We aimed to validate the accuracy of blood pressure (BP) measurement using a smartwatch in patients with Parkinson's disease (PD). Materials and Methods: We compared 168 pairs of BP (n = 56) measurements acquired by a smartwatch (SM-R850) with those measured by a sphygmomanometer (reference device). Results: Differences between the smartwatch BP and reference BP measurements were compared. The mean and standard deviation of the differences systolic BP (SBP) and diastolic BP (DBP), measured by smartwatch and reference device, fulfilled both criterion 1 (0.4 ± 4.6 and 1.1 ± 4.5 mm Hg for DBP and SBP, respectively) and criterion 2 (0.2 ± 2.5 and 0.9 ± 2.4 mm Hg for DBP and SBP, respectively) of the BP validation criterion of the International Organization for Standardization. Conclusion: BP measurement using a smartwatch with a photoplethysmography sensor is an accurate and reliable method in patients with PD.

Understanding fatigue in progressive supranuclear palsy.

Fatigue is a common and disabling non-motor symptom (NMS) of Parkinson's disease (PD); however, it has been poorly understood in patients with progressive supranuclear palsy (PSP). We investigated the association between fatigue, clinical features, and other NMS in patients with probable PSP. In 72 probable PSP patients, fatigue was investigated using the Parkinson Fatigue Scale (PFS). Further, all patients were evaluated using the PSP rating scale (PSPRS), Beck Depression Inventory (BDI), Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), PD Sleep Scale (PDSS), NMS scale (NMSS), PD Questionnaire-39 summary index (PDQ-39 SI), and Scale for outcomes in PD-Autonomic (SCOPA-AUT). The prevalence of fatigue assessed by PFS was 38.9% (28/72) in patients with PSP. The secondary fatigue was defined as fatigued patients with depression and/or sleep disturbances. We divided the patients into primary (n = 15), secondary (n = 13), and non-fatigue groups. There were no differences in age, sex, disease duration, and PSPRS, PDSS, MMSE, and FAB scores among the three groups. The primary fatigue group had higher scores in PDQ-39 SI compared to the non-fatigue group. The secondary fatigue group showed higher scores in NMSS, PDQ-39 SI, and SCOPA-AUT compared to the non-fatigue group. PFS was positively correlated with NMSS and PDQ-39 SI and SCOPA-AUT. Fatigue is common in patients with PSP and is associated with the NMS and the quality of life in these patients. The present study provides meaningful insight into fatigue in patients with PSP.

Risk factors and prognosis of adult-onset post-pump chorea.

OBJECTIVES: Post-pump chorea (PPC), defined as the development of chorea after major cardiac surgery utilizing cardiopulmonary bypass (CPB), has been rarely reported in adults. METHODS: We compared 17 patients with adult-onset PPC to controls who did not develop chorea after cardiac surgery with CPB. Two patients were enrolled using hospital based data and 15 were collected by a systematic literature review. The controls without chorea after CPB (n = 4208) were collected using hospital based data. We described the clinical and surgical features of adult-onset PPC and compared them with those of the controls. RESULTS: Ten of 17 PPC patients were male, the mean age was 46.8 years, and the mean onset latency was 6.0 days. The adult-onset PPC patients were younger (46.8 ± 16.7 vs. 59.1 ± 15.0, p = 0.001), had a lower minimum body temperature (23.3 ± 5.5 vs. 29.7 ± 3.7, p < 0.001) and a longer total circulatory arrest time (63.7 ± 7.5 vs. 21.0 ± 14.6, p < 0.001) than controls. Forty-three percentage of patients with adult-onset PPC had persistent chorea on follow-up, and these patients showed a higher rate of abnormal initial brain MRI compared with the patients with good clinical outcomes (p = 0.041). CONCLUSIONS: The onset age, onset latency, underlying disease, treatment response, and surgical features were variable among PPC patients, while abnormal initial brain MRI was associated with persistent chorea. Pooling more cases through multicenter efforts will hopefully provide more knowledge on the underlying pathophysiology, prevention, and management of PPC.

Motor and Non-motor Symptoms Associated With Exercise Behavior in Parkinson's Disease Patients: Factors Differ Between Patients With and Without Postural Instability.

Background: Exercise is an important treatment for Parkinson's disease (PD). Therefore, recognizing determinants of exercise behavior for PD based on disease stage is essential. We sought to find whether the determinants differ based on presence of postural instability (PI), which is indicative of disease stage in PD. Methods: We enrolled patients at Samsung Medical Center from September 2019 to November 2020, who had the ability to perform exercise [modified Hoehn and Yahr (HY) stage ≤ 3]. All the motor and non-motor symptoms were investigated. The exercise of the PD patients was evaluated using the Physical Activity Scale of the Elderly (PASE)-leisure score. We classified patients into PD without PI (HY stage 1 - 2) and PD with PI (HY stage 2.5 - 3) groups. Multivariate linear regression was performed using backward elimination in each group to determine factors associated with PASE-leisure score. Results: A total of 233 patients were enrolled. In the PD without PI group (n = 177), the positive determinant of exercise was Activities-Specific Balance Confidence (ABC) score (ß = 0.142, p = 0.032), and the negative determinants were fatigue score (ß = -0.228, p = 0.018), female (ß = -6.900, p = 0.016) and currently employed status (ß = -6.072, p = 0.046). In the PD with PI group (n = 56), the positive determinant was non-motor symptom scale (NMSS) score (ß = 0.221, p = 0.017) and disease duration (ß = 1.001, p = 0.036), while the negative determinants were UPDRS part 3 score (ß = -0.974, p < 0.001), UPDRS part 4 score (ß = -2.192, p = 0.002), and age (ß = -1.052, p < 0.001). Conclusion: Different motor and non-motor symptoms were associated with the exercise in PD patients with and without PI. When encouraging PD patients to exercise, personalized and different strategies should be applied based on the presence of PI.

Validation of the Korean version of the composite autonomic symptom scale 31 in patients with Parkinson's disease.

PURPOSE: The composite autonomic symptom scale-31 (COMPASS-31) is a self-rated questionnaire that evaluates diverse autonomic symptoms. In the present study, we developed the Korean version of the COMPASS-31 (K-COMPASS-31) with appropriate translation, and verified its reliability and internal and external validity in patients with Parkinson's disease (PD). METHODS: The original COMPASS-31 was translated independently into Korean by two bilingual neurologists. Test-retest reliability was evaluated at a 2-week interval. We investigated the correlations between the K-COMPASS-31, the scale for outcomes in PD-autonomic (SCOPA-AUT), and the results of an autonomic function test (AFT), respectively. RESULTS: A total of 90 patients with PD (47 females; mean age, 63.4 ± 10.8 years) were enrolled. The K-COMPASS-31 showed excellent test-retest reliability (intra-class correlation coefficient = 0.874, p < 0.001) and internal validity (Cronbach's α-coefficient = 0.878). The COMPASS-31 was positively correlated with SCOPA-AUT (r = 0.609, p < 0.001) and the results of the AFT. CONCLUSIONS: In conclusion, the K-COMPASS-31 showed excellent reliability and validity for the assessment of autonomic symptoms in PD patients. The K-COMPASS-31 is an easy-to-repeat and widely used tool for investigating autonomic dysfunction in various neurologic disorders and enables comparison of autonomic dysfunction among neurologic disorders. We recommend the K-COMPASS-31 as a valid instrument for use in clinical practice for patients with PD.

The changes of exercise pattern and clinical symptoms in patients with Parkinson's disease in the era of COVID-19 pandemic.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic has disrupted everyday life of Parkinson's disease (PD) patients, but its clinical impact has not been illustrated. In this study, we investigated the change in physical activity and subsequently clinical symptoms of PD during the COVID-19 pandemic. METHODS: We enrolled PD patients who were able to ambulate independently and had visited our clinic at Samsung Medical Centre from December 2019 to January 2020 (baseline) and in May 2020 (follow-up during the COVID-19 crisis), and divided them into either 'the sustained exercise group' or 'the reduced exercise group'. Then, we assessed the change in the exercise and clinical features between these two groups over the study period. RESULTS: A total of 100 subjects were recruited. During the COVID-19 pandemic, the amount, duration and frequency of exercise were reduced. There was decrease in number of patients who do indoor-solo exercise and increase in that of patients who do not exercise. One third reported subjective worsening of both motor and non-motor features, although Unified PD Rating Scale (UPDRS) part 3 score was similar. Additionally, the reduced exercise group reported more motor and non-motor aggravation than the sustained exercise group, despite lack of significant difference in the UPDRS part 3 score. CONCLUSION: The COVID-19 pandemic had a clear impact on exercise and subjective symptoms in PD patients, with reduced exercise being related to a subjective increase in both motor and non-motor symptoms of PD. Maintaining exercise should therefore be emphasized even in situations like the COVID-19 pandemic.