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Related Concept Videos

Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
Pharmaceutical Poisoning: Potential Scenarios01:26

Pharmaceutical Poisoning: Potential Scenarios

Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...

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Pilot evaluation of an automated method to decrease false-positive signals induced by co-prescriptions in spontaneous

Paul Avillach1, Francesco Salvo, Frantz Thiessard

  • 1Université de Bordeaux, Bordeaux, France.

Pharmacoepidemiology and Drug Safety
|May 15, 2013
PubMed
Summary
This summary is machine-generated.

An automated method effectively reduced false-positive signals of disproportionate reporting (SDRs) caused by drug co-prescription. This technique shows promise for improving pharmacovigilance data accuracy by filtering out non-genuine safety signals.

Keywords:
adverse reactionbiasmethodspharmacoepidemiologypharmacovigilancesignal detectionspontaneous reporting

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Area of Science:

  • Pharmacovigilance
  • Drug Safety
  • Data Mining

Background:

  • Spontaneous reporting systems are crucial for drug safety surveillance.
  • Co-prescription can lead to false-positive signals of disproportionate reporting (SDRs), complicating safety signal detection.
  • Accurate identification of genuine safety signals is vital for patient protection.

Purpose of the Study:

  • To evaluate an automated backward stepwise removal method for decreasing false-positive SDRs arising from co-prescription.
  • To assess the efficacy of this automated approach in a real-world pharmacovigilance database.

Main Methods:

  • The study utilized the French spontaneous reporting database.
  • An automated backward stepwise removal technique was applied to SDRs for gastric and oesophageal haemorrhages (GOH), central nervous system haemorrhages and cerebrovascular accidents (CNSH), ischaemic coronary artery disorders (ACI), and muscle pains (MP).
  • The method iteratively removed reports associated with the highest-ranked SDR, assessing initially eliminated SDRs for their false-positive nature.

Main Results:

  • The automated method successfully eliminated numerous SDRs across the studied adverse events.
  • The number of eliminated SDRs varied by event: 17 for GOH, 37 for CNSH, 15 for ACI, and 36 for MP.
  • The positive predictive value for identifying false-positive SDRs ranged from 0% for MP to 78.4% for CNSH.

Conclusions:

  • The automated backward stepwise removal method demonstrates potential for eliminating numerous false-positive signals caused by co-prescription bias.
  • Further refinement of the method is necessary to enhance its accuracy and applicability in pharmacovigilance.
  • This approach could significantly improve the efficiency and reliability of drug safety signal detection.