Recurrence of atrial fibrillation after switching from brand to generic atenolol

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients.

Flecainide is well-tolerated and effective in patient with atrial fibrillation at 12 months: a retrospective study

Introduction:

Current atrial fibrillation (AF) guidelines recommend flecainide as a first-line rhythm control option in patients without structural heart disease. While there is proven efficacy in clinical trials and guideline support, it is hypothesized that flecainide may be underutilized due to negative outcomes in the CAST trial and that adverse effects are less common than previously perceived.

Methods:

This retrospective chart review evaluated patients ⩾18 years initiated on flecainide for AF from August 2011 to October 2016 by a cardiology provider at the study site. Exclusion criteria included: <5 days of flecainide therapy, AF due to a reversible cause, and inadequate documentation. The primary outcome was efficacy of flecainide at maintaining symptomatic control at 6 and 12 months. Secondary outcomes included characterization of alterations in rhythm control strategies and documented normal sinus rhythm per electrocardiogram at 6 and 12 months.

Results:

Of the 326 patients identified, 144 patients were included. After 6 and 12 months, 102 patients (70.8%) and 89 patients (61.8%) of the 144 were symptomatically controlled. Atenolol use (p = 0.024), female sex (p = 0.006), hypertension (p = 0.040), and dronedarone failure (p = 0.012) were associated with flecainide discontinuation at 6 months. At 12 months, only previous propafenone failure (p = 0.032) was significant. Of the 144 patients, 16 (11.1%) reported adverse effects with dizziness, hot flashes, bradycardia, and headache (1.4% each) being the most common.

Conclusion:

Flecainide is a well-tolerated medication, even at 12 months, with very minor adverse effects. These results support the utility of flecainide in guideline recommended patient populations.