Atrial fibrillation induced by gabapentin: a case report.

BACKGROUND: Gabapentin is commonly prescribed for the treatment of neuropathic pain, restless leg syndrome, and partial-onset seizures. Although the most frequent side effects of gabapentin are associated with the central nervous system, gabapentin can also affect the cardiovascular system. Case reports and observational studies have showed that gabapentin can be associated with increased risk of atrial fibrillation. However, all the evidence is concentrated in patients older than 65 years old with comorbidities that predispose them to the development of arrhythmias. CASE PRESENTATION: We describe a case of an African American male in his 20s that presented to our chronic pain clinic with lumbar radiculitis and developed atrial fibrillation 4 days after being started on gabapentin. Laboratory workup did not show significant abnormalities, including normal complete blood count, comprehensive metabolic panel, toxicology screen, and thyroid-stimulating hormone. Transthoracic and transesophageal echocardiography showed a patent foramen ovale with right-to-left shunt. The patient was initially treated with diltiazem for heart rate control and apixaban. Direct current cardioversion with successful conversion to sinus rhythm was performed 24 hours after admission. The patient was then discharged on apixaban and diltiazem. Apixaban was changed to low-dose aspirin 1 month after discharge. CONCLUSION: With rapidly increasing usage of gabapentin for approved and off-label indications, it is important to identify unintended adverse effects of this drug as they are considered safe alternatives to opioids. New-onset atrial fibrillation could be induced by gabapentin in young individuals.

Inferring medication adherence from time-varying health measures.

Medication adherence is a problem of widespread concern in clinical care. Poor adherence is a particular problem for patients with chronic diseases requiring long-term medication because poor adherence can result in less successful treatment outcomes and even preventable deaths. Existing methods to collect information about patient adherence are resource-intensive or do not successfully detect low-adherers with high accuracy. Acknowledging that health measures recorded at clinic visits are more reliably recorded than a patient's adherence, we have developed an approach to infer medication adherence rates based on longitudinally recorded health measures that are likely impacted by time-varying adherence behaviors. Our framework permits the inclusion of baseline health characteristics and socio-demographic data. We employ a modular inferential approach. First, we fit a two-component model on a training set of patients who have detailed adherence data obtained from electronic medication monitoring. One model component predicts adherence behaviors only from baseline health and socio-demographic information, and the other predicts longitudinal health measures given the adherence and baseline health measures. Posterior draws of relevant model parameters are simulated from this model using Markov chain Monte Carlo methods. Second, we develop an approach to infer medication adherence from the time-varying health measures using a sequential Monte Carlo algorithm applied to a new set of patients for whom no adherence data are available. We apply and evaluate the method on a cohort of hypertensive patients, using baseline health comorbidities, socio-demographic measures, and blood pressure measured over time to infer patients' adherence to antihypertensive medication.

Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models.

One of the most significant barriers to medication treatment is patients' non-adherence to a prescribed medication regimen. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. This article develops a modeling framework for longitudinally recorded health measures modeled as a function of time-varying medication adherence. Our framework, which relies on normal Bayesian dynamic linear models (DLMs), accounts for time-varying covariates such as adherence and non-dynamic covariates such as baseline health characteristics. Standard inferential procedures for DLMs are inefficient when faced with infrequent and irregularly recorded response data. We develop an approach that relies on factoring the posterior density into a product of two terms: a marginal posterior density for the non-dynamic parameters, and a multivariate normal posterior density of the dynamic parameters conditional on the non-dynamic ones. This factorization leads to a two-stage process for inference in which the non-dynamic parameters can be inferred separately from the time-varying parameters. We demonstrate the application of this model to the time-varying effect of antihypertensive medication on blood pressure levels for a cohort of patients diagnosed with hypertension. Our model results are compared to ones in which adherence is incorporated through non-dynamic summaries.

Dielectrophoretic capture and genetic analysis of single neuroblastoma tumor cells.

Our understanding of the diversity of cells that escape the primary tumor and seed micrometastases remains rudimentary, and approaches for studying circulating and disseminated tumor cells have been limited by low throughput and sensitivity, reliance on single parameter sorting, and a focus on enumeration rather than phenotypic and genetic characterization. Here, we utilize a highly sensitive microfluidic and dielectrophoretic approach for the isolation and genetic analysis of individual tumor cells. We employed fluorescence labeling to isolate 208 single cells from spiking experiments conducted with 11 cell lines, including 8 neuroblastoma cell lines, and achieved a capture sensitivity of 1 tumor cell per 10(6) white blood cells (WBCs). Sample fixation or freezing had no detectable effect on cell capture. Point mutations were accurately detected in the whole genome amplification product of captured single tumor cells but not in negative control WBCs. We applied this approach to capture 144 single tumor cells from 10 bone marrow samples of patients suffering from neuroblastoma. In this pediatric malignancy, high-risk patients often exhibit wide-spread hematogenous metastasis, but access to primary tumor can be difficult or impossible. Here, we used flow-based sorting to pre-enrich samples with tumor involvement below 0.02%. For all patients for whom a mutation in the Anaplastic Lymphoma Kinase gene had already been detected in their primary tumor, the same mutation was detected in single cells from their marrow. These findings demonstrate a novel, non-invasive, and adaptable method for the capture and genetic analysis of single tumor cells from cancer patients.