Assessing the Safety of Early Repatriation for Stable ST-Segment Elevation Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention.

Background: Repatriation of ST-segment elevation myocardial infarction (STEMI) patients after primary percutaneous coronary intervention (PPCI) is common in regional health care programs. We examined the short- and long-term safety of early repatriation after PPCI in stable STEMI patients.

Methods: Consecutive stable STEMI patients undergoing PPCI between 2016 to 2018 in the Fraser Health Authority were included.

Influence of Monitoring Strategy on Assessment of Ablation Success and Postablation Atrial Fibrillation Burden Assessment: Implications for Practice and Clinical Trial Design.

Background: Various noninvasive intermittent rhythm monitoring strategies have been used to assess arrhythmia recurrences in trials evaluating pharmacological and invasive therapeutic interventions for atrial fibrillation (AF). We determined whether a frequency and duration of noninvasive rhythm monitoring could be identified that accurately detects arrhythmia recurrences and approximates the AF burden derived from continuous monitoring using an implantable cardiac monitor (ICM).

Methods: The rhythm history of 346 patients enrolled in the CIRCA-DOSE trial (Cryoballoon Versus Contact-Force Irrigated Radiofrequency Catheter Ablation) was reconstructed.

Clinical assessment of AF pattern is poorly correlated with AF burden and post ablation outcomes: A CIRCA-DOSE sub-study.

Background: Contemporary guidelines recommend that atrial fibrillation (AF) be classified into paroxysmal and persistent AF based on clinical assessment, with these categorizations forming the basis of therapeutic recommendations. While pragmatic, clinical assessment may introduce misclassification errors, which may impact treatment decisions. We sought to determine the relationship between AF classification, baseline AF burden, and post-ablation arrhythmia outcomes.

Artificial intelligence as an emerging technology in the current care of neurological disorders.

Background: Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e.

Usage of EpiFinder clinical decision support in the assessment of epilepsy.

Background: The diagnosis of epilepsy is at times elusive for both neurologists and nonneurologists, resulting in delays in diagnosis and therapy. The development of screening methods has been identified as a priority in response to this diagnostic and therapeutic gap. EpiFinder is a novel clinical decision support tool designed to enhance the process of information gathering and integration of patient/proxy respondent data.

Automatic gene prioritization in support of the inflammatory contribution to Alzheimer's disease.

This research seeks to extend the process of novel therapeutic gene target discovery for the treatment of Alzheimer's disease (AD). Gene-gene and gene-pathway annotation tools as well as human analysis are used to explore likely connections between potential gene targets and biochemical mechanisms of AD and associated genes. Rule-based annotation systems, such as GeneRanker, can be applied to the continuously growing volume of literature to extract relevant gene lists.

Text Classification towards Detecting Misdiagnosis of an Epilepsy Syndrome in a Pediatric Population.

When attempting to identify a specific epilepsy syndrome, physicians are often unable to make or agree upon a diagnosis. This is further complicated by the fact that the current classification and diagnosis of epilepsy requires specialized training and the use of resources not typically available to the average clinician, such as training to recognize specific seizure types and electroencephalography (EEG). Even when training and resources are available, expert epileptologists often find it challenging to identify seizure types and to distinguish between specific epilepsy syndromes.