Abstract Objective There are numerous distinctive benign electroencephalographic (EEG) patterns which are morphologically epileptiform but are non-epileptic. The aim of this study was to determine ...the prevalence of different benign epileptiform variants (BEVs) among subjects who underwent routine EEG recordings in a large EEG laboratory over 35 years. Methods We retrospectively studied the prevalence of BEVs among 35,249 individuals who underwent outpatient EEG recordings at London Health Sciences Centre in London, Ontario, Canada between January 1, 1972 and December 31, 2007. The definitions of the Committee on Terminology of the International Federation of Societies for EEG and Clinical Neurophysiology (IFSECN) were used to delineate epileptiform patterns (Chatrian et al. A glossary of terms most commonly used by clinical electroencephlographers. Electroenceph Clin Neurophysiol 1974;37:538–48) and the descriptions of Klass and Westmoreland Klass DW, Westmoreland BF. Nonepileptogenic epileptiform electroenephalographic activity. Ann Neurol 1985;18:627–35 were used to categorize the BEVs. Results BEVs were identified in 1183 out of 35,249 subjects (3.4%). The distribution of individual BEVs were as follows: benign sporadic sleep spikes 1.85%, wicket waves 0.03%, 14 and 6 Hz positive spikes 0.52%, 6 Hz spike-and-waves 1.02%, rhythmic temporal theta bursts of drowsiness 0.12%, and subclinical rhythmic electrographic discharge of adults in 0.07%. Conclusion The prevalence of six types of BEVs was relatively low among the Canadian subjects when compared to the reports from other countries. Significance BEVs are relatively uncommon incidental EEG findings. Unlike focal epileptic spikes and generalized spike-and-waves, BEVs do not predict the occurrence of epilepsy. Accurate identification of the BEVs can avoid misdiagnosis and unnecessary investigations.
Computerized decision support systems (CDSS) are believed to have the potential to improve the quality of health care delivery, although results from high quality studies have been mixed. We ...conducted a systematic review to evaluate whether certain features of prescribing decision support systems (RxCDSS) predict successful implementation, change in provider behaviour, and change in patient outcomes.
A literature search of Medline, EMBASE, CINAHL and INSPEC databases (earliest entry to June 2008) was conducted to identify randomized controlled trials involving RxCDSS. Each citation was independently assessed by two reviewers for outcomes and 28 predefined system features. Statistical analysis of associations between system features and success of outcomes was planned.
Of 4534 citations returned by the search, 41 met the inclusion criteria. Of these, 37 reported successful system implementations, 25 reported success at changing health care provider behaviour, and 5 noted improvements in patient outcomes. A mean of 17 features per study were mentioned. The statistical analysis could not be completed due primarily to the small number of studies and lack of diversity of outcomes. Descriptive analysis did not confirm any feature to be more prevalent in successful trials relative to unsuccessful ones for implementation, provider behaviour or patient outcomes.
While RxCDSSs have the potential to change health care provider behaviour, very few high quality studies show improvement in patient outcomes. Furthermore, the features of the RxCDSS associated with success (or failure) are poorly described, thus making it difficult for system design and implementation to improve.
Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, ...increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.
L'intelligence artificielle progresse rapidement de la phase expérimentale à la phase de mise en œuvre dans de nombreux domaines, notamment la médecine. L'accès à de grands ensembles de données, la puissance croissante des ordinateurs et les avancées en matière d'algorithmes d'apprentissage ont permis de faire des pas de géant au chapitre du développement des applications d'intelligence artificielle. Au cours des cinq dernières années, des techniques comme l'apprentissage profond ont permis d'améliorer rapidement les capacités de reconnaissance d'images, de production de légendes d'images et de reconnaissance vocale. La radiologie est un domaine tout indiqué pour l'adoption précoce de ces techniques. L'intégration d'applications d'intelligence artificielle en radiologie au cours de la prochaine décennie devrait grandement améliorer la qualité, la valeur et la portée de la contribution de la radiologie aux soins des patients et à la santé de la population, en plus de révolutionner le travail des radiologistes. En sa qualité de porte-parole de la profession au Canada, l’Association canadienne des radiologistes (CAR) défend des normes de pratique élevées en imagerie centrée sur les patients, en apprentissage continu et en recherche. La CAR a mis sur pied un groupe de travail sur l'intelligence artificielle qui a pour mandat de discuter des enjeux liés à la pratique, aux politiques et à la prestation de soins relativement à l'introduction et à la mise en œuvre d'outils d'intelligence artificielle en radiologie. Le présent livre blanc formule à l'intention de la CAR des recommandations issues des délibérations des membres du groupe de travail. Il renseigne les membres de la CAR et les responsables de l’élaboration des politiques sur la terminologie à employer, les besoins en matière de formation, la recherche-développement, les partenariats, les applications cliniques potentielles, la mise en œuvre, la structure et la gouvernance, le rôle des radiologistes et sur les retombées potentielles de l'intelligence artificielle en radiologie au Canada.
Summary Background Many patients with an oligodendroglioma (OD) experience seizures, some of which become refractory to anti-epileptic drugs (AEDs). This study aims (1) to quantify the rate of ...seizures and medically refractory epilepsy in patients with ODs; and (2) to determine if there is any association between short-term and long-term survival, and the presence and drug-responsiveness of seizures. Methods A retrospective review was conducted of the medical records of patients who had been pathologically identified as having an OD at the London Health Sciences Centre or the London Regional Cancer Program in London, Ontario from January 1996 to July 2008. Deaths were ascertained by reviewing all hospital records. Survival analysis was performed. Results One-hundred sixty-six patients met inclusion criteria. Epileptic seizures were the presenting feature or occurred as part of the initial manifestation of the OD in 75.3% of patients, with 90.4% ( n = 150) experiencing at least one seizure and 76.5% developing epilepsy over the course of observation. Of the 150 patients with seizures, 23 experienced a single seizure (13.9% of the 166), whereas 127 patients experienced multiple seizures (76.5%). In those with multiple seizures, the epilepsy was refractory to drug treatment slightly more than half the time (54.3%). Survival analysis demonstrated consistently superior survival among those with a single seizure. Those without seizures had the worst survival rates over the first few years post-diagnosis; but then no further deaths occurred. Survival among those with refractory seizures tended to be better than among those whose seizures were drug responsive, over the first 10 years post-diagnosis. Conclusions Seizures are common and may influence survival in patients with oligodendogliomas. Those who experience just one seizure appear to do best.
PDF
