The New Sentinel Network — Improving the Evidence of Medical-Product Safety

Platt, Richard; Wilson, Marcus D.; Chan, K. Arnold; Benner, Joshua S.; Marchibroda, Janet; McClellan, Mark

doi:10.1056/nejmp0905338

Cited by 200 publications

(129 citation statements)

References 1 publication

Supporting

Mentioning

128

Contrasting

Order By: Relevance

“…Aiming to evaluate possible medical product safety issues quickly and securely, the FDA has recently initiated the Sentinel Network 41 and the Safe Use Initiative. 47 Additionally, large and well-designed randomized control trials can contribute information about medication-related harms, 42 but reporting and availability of harms information needs to be improved.…”

Section: Discussionmentioning

confidence: 99%

Different Black Box Warning Labeling for Same-Class Drugs

Panagiotou

Contopoulos‐Ioannidis

Papanikolaou

et al. 2011

J GEN INTERN MED

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Different Black Box Warning Labeling for Same-Class Drugs

Panagiotou

Contopoulos‐Ioannidis

Papanikolaou

et al. 2011

J GEN INTERN MED

View full text Add to dashboard Cite

“…Interestingly, the recent IMEDS research agenda (http://imeds.reaganudall.org/) promises comparisons of different common data models. A study that maps the same raw data set as used by Matcho et al [3] to the Mini-Sentinel common data model [8] would be an interesting paper.…”

mentioning

confidence: 99%

Converting to a Common Data Model: What is Lost in Translation?

Rijnbeek

2014

Drug Saf

View full text Add to dashboard Cite

“…The US Food and Drug Administration began its Sentinel initiative in May 2008 and announced that they will construct a novel postmarketing monitoring system employing a database of 100 million patients by 2012 24) . In Europe, the EU-ADR project has begun to allow signal detection using multinational clinical information databases 25) .…”

Section: Discussionmentioning

confidence: 99%

Integration of Drug Use-Results Survey (DUS) Data of a Pharmaceutical Manufacturer

Matsushita

Sugihara

Kawasugi

2010

Japanese Journal of Pharmacoepidemiology/Yakuzai ekigaku

View full text Add to dashboard Cite

Objective: Pharmaceutical manufacturers conduct drug use-results surveys（DUSs）for drug reexamination applications in Japan. However, most DUSs are single-cohort studies to confirm drug safety; therefore little information is obtained from a DUS for safety comparison with other drugs. Integrating DUS data is a novel method to compare drug safety profiles among drugs. This study examined the usefulness of integrating DUS data. Design: Active surveillance Methods: We integrated DUS data obtained from two DUSs of antihypertensive drugs-a calcium channel blocker（CCB), azelnidipine（Calblock® ), and an angiotensin-receptor blocker（ARB), olmesartan medoxomil（Olmetec® )-with similar study protocols. Both studies were conducted by the same pharmaceutical company. Matching patients using propensity scores, we examined whether the DUS results could be applied to pharmacoepidemiology data resources for hypothesis strengthening. Safety outcomes included vasodilation-related events, which are typical adverse drug reactions（ADRs）to CCB. The incidence of safety outcomes was compared by conditional logistic regression models. Two definitions for safety outcomes were employed: definition 1, physician-reported adverse events; and definition 2, physicianreported ADRs. Results: In a total of 7196 patients included in the analysis, the propensity-matched patients were well balanced. Most safety outcomes were detected in the CCB group including a significant increase in vasodilation-related events（odds ratio for definition 1 versus ARB group, 1.75; 95％ confidence interval, 1.01-3.03). Conclusion: Integrated DUS data collated by pharmaceutical manufacturers following clear criteria are potentially useful to commence pharmacoepidemiology studies designed to strengthen hypotheses.

show abstract

The New Sentinel Network — Improving the Evidence of Medical-Product Safety

Cited by 200 publications

References 1 publication

Different Black Box Warning Labeling for Same-Class Drugs

Different Black Box Warning Labeling for Same-Class Drugs

Converting to a Common Data Model: What is Lost in Translation?

Integration of Drug Use-Results Survey (DUS) Data of a Pharmaceutical Manufacturer

Contact Info

Product

Resources

About