Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.

Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, and benzodiazepine intoxication. While altered functional connectivity (FC) plays a pivotal role in the pathophysiology of LOC, there has been a lack of efforts to develop differential diagnosis artificial intelligence (AI) models that feature the distinctive FC change patterns specific to each LOC cause. Three approaches were applied for extracting features for the AI models: three-dimensional FC adjacency matrices, vectorized FC values, and graph theoretical measurements. Deep learning using convolutional neural networks (CNN) and various machine learning algorithms were implemented to compare classification accuracy using electroencephalography (EEG) data with different epoch sizes. The CNN model using FC adjacency matrices achieved the highest accuracy with an AUC of 0.905, with 20-s epoch data being optimal for classifying the different LOC causes. The high accuracy of the CNN model was maintained in a prospective cohort. Key distinguishing features among the LOC causes were found in the delta and theta brain wave bands. This research advances the understanding of LOC's underlying mechanisms and shows promise for enhancing diagnosis and treatment selection. Moreover, the AI models can provide accurate LOC differentiation with a relatively small amount of EEG data in 20-s epochs, which may be clinically useful.

Long-Term Safety and Efficacy of Extended Dual Antiplatelet Therapy After Drug-Eluting Stent Implantation in Real-World Practice.

BACKGROUND: Extended dual antiplatelet therapy (DAPT) after drug-eluting stent (DES) implantation is frequently used for high-risk patients in real-world practice. However, there are limited data about the long-term efficacy of extended DAPT after percutaneous coronary intervention (PCI).Methods and Results:This study investigated 1,470 patients who underwent PCI. The study population was divided into 2 groups based on DAPT duration: guideline-based DAPT (G-DAPT; DAPT ≤12 months after PCI; n=747) and extended DAPT (E-DAPT; DAPT >12 months after PCI; n=723). The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCEs), defined as cardiac death, myocardial infarction (MI), repeat target vessel revascularization, or stroke. The median follow-up duration was 80.8 months (interquartile range 60.6-97.1 months). The incidence of MACCE was similar in the G-DAPT and E-DAPT groups (21.0% vs. 18.3%, respectively; P=0.111). However, the E-DAPT group had a lower incidence of non-fatal MI (hazard ratio [HR] 0.535; 95% confidence interval [CI] 0.329-0.869; P=0.011), and target lesion revascularization (HR 0.490; 95% CI 0.304-0.792; P=0.004), and stent thrombosis (HR 0.291; 95% CI 0.123-0.688; P=0.005). The incidence of bleeding complications, including major bleeding, was similar between the 2 groups (5.2% vs. 6.3%, respectively; P=0.471). CONCLUSIONS: Although E-DAPT after DES implantation was not associated with a reduced rate of MACCE, it was associated with a significantly lower incidence of non-fatal MI, TLR, and stent thrombosis.

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy.

Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation.