Comparison of clinical performance of four gastrointestinal bleeding risk scores in Chinese patients with atrial fibrillation receiving oral anticoagulants.

Gastrointestinal bleeding is the most common bleeding complication during anticoagulant therapy. A reliable bleeding risk score can help the clinician assess risk of bleeding in individual patients and select the anticoagulant regimen. This study retrospectively analyzed the data of patients with atrial fibrillation who received anticoagulant therapy from July 2015 to December 2018 at two centers-the Fujian Medical University Union Hospital and Fuzhou Second Hospital Affiliated to Xiamen University. Demographic data, clinical findings, and laboratory results were collected from the hospital records. Patients were followed up for 6 months. The performance of four bleeding risk scores (New Score, RIETE Score, Cuschieri et al. Score, de Groot et al. Score) for prediction of gastrointestinal bleeding was assessed using the area under the curve. A total of 3462 patients (mean age, 66.3 ± 11.5 years; 59.6% males; 1055 direct oral anticoagulants users and 2407 warfarin users) were followed up for 6 months. While 99/3462 (2.9%) patients had gastrointestinal bleeding. The area under the curves for the New, RIETE, Cuschieri et al., de Groot et al. scores were 0.652 (95% CI 0.576-0.728), 0.862 (95% CI 0.809-0.914), 0.606 (95% CI 0.527-0.685), and 0.873 (95% CI 0.816-0.929), respectively. Among the four BRSs evaluated, the RIETE score and the de Groot et al. score appear to have the good predictive value, while the NEW score and the Cuschieri et al. score did not sufficiently predict gastrointestinal bleeding risk within the study Chinese population.

Electromyography Biomarkers for Quantifying the Intraoperative Efficacy of Deep Brain Stimulation in Parkinson's Patients With Resting Tremor.

Introduction: Deep brain stimulation (DBS) is an effective therapy for resting tremor in Parkinson's disease (PD). However, quick and objective biomarkers for quantifying the efficacy of DBS intraoperatively are lacking. Therefore, we aimed to study how DBS modulates the intraoperative neuromuscular pattern of resting tremor in PD patients and to find predictive surface electromyography (sEMG) biomarkers for quantifying the intraoperative efficacy of DBS. Methods: Intraoperative sEMG of 39 PD patients with resting tremor was measured with the DBS on and off, respectively, during the intraoperative DBS testing stage. Twelve signal features (time and frequency domains) were extracted from the intraoperative sEMG data. These sEMG features were associated with the clinical outcome to evaluate the efficacy of intraoperative DBS. Also, an sEMG-based prediction model was established to predict the clinical improvement rate (IR) of resting tremor with DBS therapy. Results: A typical resting tremor with a peak frequency of 4.93 ± 0.98 Hz (mean ± SD) was measured. Compared to the baseline, DBS modulated significant neuromuscular pattern changes in most features except for the peak frequency, by decreasing the motor unit firing rate, amplitude, or power and by changing the regularity pattern. Three sEMG features were detected with significant associations with the clinical improvement rate (IR) of the tremor scale: peak frequency power (R = 0.37, p = 0.03), weighted root mean square (R = 0.42, p = 0.01), and modified mean amplitude power (R = 0.48, p = 0.003). These were adopted to train a Gaussian process regression model with a leave-one-out cross-validation procedure. The prediction values from the trained sEMG prediction model (1,000 permutations, p = 0.003) showed a good correlation (r = 0.47, p = 0.0043) with the true IR of the tremor scale. Conclusion: DBS acutely modulated the intraoperative resting tremor, mainly by suppressing the amplitude and motor unit firing rate and by changing the regularity pattern, but not by modifying the frequency pattern. Three features showed strong robustness and could be used as quick intraoperative biomarkers to quantify and predict the efficacy of DBS in PD patients with resting tremor.

Metabolic covariance networks combining graph theory measuring aberrant topological patterns in mesial temporal lobe epilepsy.

OBJECTIVE: We aimed to study the networks' mechanism of metabolic covariance networks in mesial temporal lobe epilepsy (mTLE), through examining the brain value of fluorine-18-fluorodeoxyglucose positron emission tomography (18 F-FDG-PET). METHODS: 18 F-FDG-PET images from 16 patients with mTLE were analyzed using local and global metabolic covariance network (MCN) approaches, including whole metabolic pattern analysis (WMPA), hippocampus-based (h-) MCN, whole brain (w-) MCN, and edge-based connectivity analysis (EBCA). RESULTS: WMPA showed a typical ipsilateral hypometabolism and contralateral hypermetabolism pattern to epileptic zones in mTLE. h-MCN revealed decreased hippocampus-based synchronization in contralateral regions. w-MCN exhibited a disrupted metabolic network with globally increased small-world properties and regionally decreased nodal metrics in the ipsilateral hemisphere. Hippocampus (h)-EBCA and whole brain EBCA (w-EBCA) both detected a reduced-connectivity dominated metabolic covariant network. Moreover, the reduced interhemisphere connectivity seemingly played a major role in the aberrant epileptic topological pattern. CONCLUSION: From a metabolic point of view, we demonstrated the damaging effects with reduced contralateral intranetwork metrics properties and the compensatory effects in contralateral intranetworks with increased network properties. However, the import role of significant reduced interhemisphere connection has rarely been reported in other mTLE studies. Taken together, 18 F-FDG-PET MCN analysis provides new evidence that the mTLE is a system neurological disorder with disrupted networks.