Active Safety Monitoring of Newly Marketed Medications in a Distributed Data Network: Application of a Semi-Automated Monitoring System

Abstract

We developed a semi-automated active monitoring system that uses sequential matched-cohort analyses to assess drug safety across a distributed network of longitudinal electronic healthcare data. In a retrospective analysis, we showed that the system would have identified cerivastatin-induced rhabdomyolysis. In this study, we evaluated whether the system would generate alerts for three drug-outcome pairs: rosuvastatin and rhabdomyolysis (known null association), rosuvastatin and diabetes mellitus, and telithromycin and hepatotoxicity (two examples for which alerting would be questionable). During >5 years of monitoring, rate differences (RDs) comparing rosuvastatin to atorvastatin were -0.1 cases of rhabdomyolysis per 1,000 person-years (95% CI, -0.4, 0.1) and -2.2 diabetes cases per 1,000 person-years (95% CI, -6.0, 1.6). The RD for hepatotoxicity comparing telithromycin to azithromycin was 0.3 cases per 1,000 person-years (95% CI, -0.5, 1.0). In a setting in which false positivity is a major concern, the system did not generate alerts for three drug-outcome pairs.

Cardiovascular safety of linagliptin compared with other oral glucose‐lowering agents in patients with type 2 diabetes: A sequential monitoring programme in routine care

“…The monitoring system was set up to inform the medical community in a timely manner of the best available effect estimate as the data accumulated over time. Unlike a clinical trial, non‐experimental studies based on large utilization databases cannot prevent people from receiving substandard treatment once the treatment effects in routine care have been established; thus, the periodic analyses in database studies are not performed to evaluate potential earlier study termination, nor any other time‐dependent actions, such as regulatory decisions, but rather to provide the most timely and accurate information when treating individual patients …”

Using Real‐World Data to Extrapolate Evidence From Randomized Controlled Trials

“…Large, administrative healthcare databases, which prospectively record information on healthcare utilization, reflect real‐world treatment patterns among diverse populations . RCTs and healthcare databases are often used to address different aspects of drug effects, with the former typically focused on determining efficacy and the latter on evaluating “real‐world” effectiveness and safety .…”

Development and Application of Two Semi-Automated Tools for Targeted Medical Product Surveillance in a Distributed Data Network

“…One of the concerns regarding automated safety surveillance systems in multiple databases is the opportunity for many false positives or false negatives [38]. As a first step in evaluating these concerns, a PS-based semi-automated safety monitoring approach was utilized to assess the safety of three medications across three claims databases [39]. Data from each database were divided into three-month intervals to mimic prospective accumulation of data, and new users of the three study drugs were 1:1 PS matched to new users of comparator products within each interval.…”