Background/Aims: We examined the ischemia-modified albumin (IMA) level during exercise in patients with coronary artery disease (CAD).Methods: Forty patients with a history of chest pain underwent both symptom-limited treadmill exercise stress testing and coronary angiography within one week. During the treadmill tests, blood samples were obtained at baseline and 5 min after exercise to measure the serum IMA level.Results: Of the 40 patients, fourteen (35%, CAD group) had significant coronary artery stenosis, while the other 26 (65%, non-CAD group) did not. The baseline and post-exercise IMA levels in the two groups did not differ significantly (105.2±7.2 vs. 107.7±6.7 U/mL at baseline and 93.1±10.1 vs. 94.8±5.7 U/mL at post-exercise in the CAD and non-CAD groups, p=0.29 and 0.57, respectively). The changes in IMA after exercise did not differ either (-10.4±7.5 vs. -14.0±7.6 U/mL in the CAD and non-CAD groups, respectively, p=0.10). Similarly, the change in IMA between the exercise ECG test positive (TMT positive, n=9) and negative (TMT negative, n=20) groups did not differ (-14.63±5.19, vs -8.50±9.01 U/mL, p=0.15, in the TMT positive and negative groups, respectively).Conclusions: Our results suggest that IMA has limitation in detecting myocardial ischemia during symptom-limited exercise stress tests.
Study Objectives We conducted a prospective study to quantify motor activity during sleep measured by actigraphy before and after 3 months of treatment with clonazepam in patients with video-polysomnography (vPSG) confirmed isolated rapid eye movement (REM) sleep behavior disorder (iRBD). Methods The motor activity amount (MAA) and the motor activity block (MAB) during sleep were obtained from actigraphy. Then, we compared quantitative actigraphic measures with the results of the REM sleep behavior disorder questionnaire for the previous 3-month period (RBDQ-3M) and of the Clinical Global Impression-Improvement scale (CGI-I), and analyzed correlations between baseline vPSG measures and actigraphic measures. Results Twenty-three iRBD patients were included in the study. After medication treatment, large activity MAA dropped in 39% of patients, and the number of MABs decreased in 30% of patients when applying 50% reduction criteria. 52% of patients showed more than 50% improvement in either one. On the other hand, 43% of patients answered “much or very much improved” on the CGI-I, and RBDQ-3M was reduced by more than half in 35% of patients. However, there was no significant association between the subjective and objective measures. Phasic submental muscle activity during REM sleep was highly correlated with small activity MAA (Spearman’s rho=0.78, p<0.001) while proximal and axial movements during REM sleep correlated with large activity MAA (rho=0.47, p=0.030 for proximal movements, rho=0.47, p=0.032 for axial movements). Conclusions Our findings imply that quantifying motor activity during sleep using actigraphy can objectively assess therapeutic response in drug trials in patients with iRBD.
Nilotinib is a Bcr-Abl tyrosine kinase inhibitor used to treat chronic myelogenous leukemia. There have been case reports of nilotinib-related vasculopathy. However, most cases present peripheral artery disease, whilst reports of nilotinib-related cerebrovascular disease are quite uncommon. Herein, we report a case of nilotinib-induced intracranial stenosis in a patient with recurrent transient ischemic attacks and discuss the results of serial vessel wall magnetic resonance imaging.
Introduction Idiopathic Rapid eye movement (REM) sleep behavior disorder is a condition that can be an early sign of alpha-synuclein-mediated neurodegenerative diseases, and the course of the disease can vary greatly from patient to patient. It is important to identify patients who are at risk of developing neurodegenerative diseases in the future for the purpose of future clinical trials and for patients to plan their lives accordingly. Previous research has identified various risk factors for phenoconversion in RBD patients, but these studies are not practical for use in clinical settings due to resource availability or the rarity of certain features. Additionally, most of these studies have been conducted on non-Asian populations, which may have different genetic backgrounds than Asian populations. This study aimed to develop a machine learning model to predict survival in RBD patients using clinical features commonly available in routine clinical settings. Methods This study recruited patients diagnosed with RBD based on polysomnography results and collected 34 features for each patient. Missing data were imputed and various models were applied to the data to improve performance. The model's predictive performance was evaluated using an integrated Brier score and the concordance index. Mean performance indicators were calculated from 5-fold cross-validation results. A web application hosting the final prediction model was developed and deployed on a server for use by physicians or patients. Results 173 patients were included in the study. We used the likelihood ratio test to calculate the p-values of all variables and selected the following 8 variables with p-values less than 0.1: UPDRS part III, age, history of antidepressant use, history of alcohol use, MoCA (Montreal Cognitive Assessment), PSQI-TST (Pittsburgh Sleep Quality Index - total sleep time), AHI-REM (apnea-hypopnea index - REM sleep), and education level. The random survival forest model had the best mean IBS of 0.07 and the best C-index of 0.93 Conclusion We showed that it is possible for a machine learning model to predict phenoconversion in patients with RBD using features that are commonly available in routine clinical settings Support (if any)
Introduction More than 80% of patients of isolated rapid eye movement (REM) sleep behavior disorder (iRBD), a prodromal disease of α-synucleinopathies, progress to neurological disease like Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Resting-state EEG measurements taken at baseline have been related to the phenoconversion. The timing of the conversion and the disease to which it will convert are crucial issues in iRBD. This work used baseline EEG in iRBD to create a prediction model for the phenoconversion time and subtype of α-synucleinopathy. Methods Resting-state EEG and neurological assessments were performed at baseline on patients with iRBD. EEG spectral power, Shannon entropy and weighted phase lag index were employed as features. Four models were used to predict subtypes for the PD-MSA and DLB groups, and three models were used to predict survival. External validation was also performed. Results 29 patients out of 143 who were followed up to nine years (mean 3.4 years) later developed α-synucleinopathies (14 PD, 9 DLB, 6 MSA). With a concordance index of 0.8130 and an integrated Brier score of 0.0921, the random survival forest was the best model for predicting survival. For the subtype prediction analysis, the model with the highest accuracy, extreme gradient boosting, had an accuracy of 86.52%. Both models indicated a high importance on EEG slowing related features. Conclusion It is possible to predict the timing and subtype of phenoconversion in iRBD using machine learning models of using EEG biomarkers. To confirm our findings, further study is required, including large sample data from various countries. Support (if any)
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