Safety and effectiveness of rasagiline in patients with Parkinson's disease in Japan: a post-marketing surveillance study

BACKGROUND

Rasagiline is a monoamine oxidase B inhibitor for the treatment of Parkinson's disease (PD). This study assessed the safety and effectiveness of rasagiline in patients with PD in routine clinical practice in Japan.

RESEARCH DESIGN AND METHODS

This multicenter, prospective, observational study (148 sites) enrolled patients (1 November 2018-31 October 2020) with PD. Patients received rasagiline orally 1 mg once daily; maximum observation period was 24 months. The incidence of adverse drug reactions (ADRs) was evaluated; effectiveness was assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III total score.

RESULTS

The safety analysis set comprised 961 patients (mean age, 72.50 years; 53.80% female; mean duration of PD, 6.82 years). Mean treatment duration was 14.74 months, with 42.25% receiving rasagiline for ≥ 19 months; 189 (19.67%) had ≥ 1 ADR. Common ADRs were dyskinesia (4.06%), orthostatic hypotension (2.29%), hallucination (1.87%), visual hallucination, nausea, fall (1.56% each), dizziness (1.35%), and somnolence (1.25%). Mean (standard deviation) UPDRS Part III total score was 28.5 (14.35) at baseline and 25.5 (14.98) at the final assessment.

CONCLUSIONS

No new concerns in safety and effectiveness regarding rasagiline in Japanese patients with PD were raised.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03727139; Japan Pharmaceutical Information Center Clinical Trials Information: JapicCTI-184181.

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

BACKGROUND

Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome ("attributed") or state the simple treatment and outcome without an association ("unattributed"). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, "transfusion" and "time-based." Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians' documentation of attributed AEs.

OBJECTIVE

We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs.

METHODS

We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs.

RESULTS

Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination.

CONCLUSIONS

The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

Torsades de Pointes after Ondansetron Infusion in 2 Patients

Drugs that prolong the electrocardiographic QT interval increase the risk of ventricular arrhythmias, particularly torsades de pointes. Ondansetron, a 5-hydroxytryptamine type 3 receptor antagonist antiemetic, is one such drug. We present the cases of 2 patients who were given intravenous ondansetron and subsequently developed torsades de pointes. Both had normal QT intervals at baseline but were discovered to have risk factors that predisposed them to drug-induced QT prolongation and ventricular arrhythmias. We briefly review the mechanisms for torsades de pointes caused by QT-prolonging medications, describe characteristics that increase patients' susceptibility to drug-induced QT prolongation, and call attention to the risk of ventricular arrhythmias in patients who are given ondansetron.

A Synthesis of Current Surveillance Planning Methods for the Sequential Monitoring of Drug and Vaccine Adverse Effects Using Electronic Health Care Data

INTRODUCTION

The large-scale assembly of electronic health care data combined with the use of sequential monitoring has made proactive postmarket drug- and vaccine-safety surveillance possible. Although sequential designs have been used extensively in randomized trials, less attention has been given to methods for applying them in observational electronic health care database settings.

EXISTING METHODS

We review current sequential-surveillance planning methods from randomized trials, and the Vaccine Safety Datalink (VSD) and Mini-Sentinel Pilot projects-two national observational electronic health care database safety monitoring programs.

FUTURE SURVEILLANCE PLANNING

Based on this examination, we suggest three steps for future surveillance planning in health care databases: (1) prespecify the sequential design and analysis plan, using available feasibility data to reduce assumptions and minimize later changes to initial plans; (2) assess existing drug or vaccine uptake, to determine if there is adequate information to proceed with surveillance, before conducting more resource-intensive planning; and (3) statistically evaluate and clearly communicate the sequential design with all those designing and interpreting the safety-surveillance results prior to implementation. Plans should also be flexible enough to accommodate dynamic and often unpredictable changes to the database information made by the health plans for administrative purposes.

CONCLUSIONS

This paper is intended to encourage dialogue about establishing a more systematic, scalable, and transparent sequential design-planning process for medical-product safety-surveillance systems utilizing observational electronic health care databases. Creating such a framework could yield improvements over existing practices, such as designs with increased power to assess serious adverse events.