Pharmacoepidemiology and drug safety
-
Pharmacoepidemiol Drug Saf · Nov 2013
Validation of anaphylaxis in the Food and Drug Administration's Mini-Sentinel.
We aim to develop and validate the positive predictive value (PPV) of an algorithm to identify anaphylaxis using health plan administrative and claims data. Previously published PPVs for anaphylaxis using International Classification of Diseases, ninth revision, Clinical Modification (ICD-9-CM) codes range from 52% to 57%. ⋯ The PPV for the ICD-9-CM-based algorithm for anaphylaxis was slightly higher than PPV estimates reported in prior studies, but remained low. We were able to identify an algorithm that optimized the PPV but demonstrated lower sensitivity for anaphylactic events.
-
Pharmacoepidemiol Drug Saf · Nov 2013
Determining the predictive value of Read codes to identify congenital cardiac malformations in the UK Clinical Practice Research Datalink.
The purposes of this study were to determine (i) the positive predictive value (PPV) of multiple Read codes used to identify congenital cardiac malformation (CCM) cases in the UK Clinical Practice Research Datalink (CPRD); (ii) the accuracy of the diagnosis date; and (iii) the source of information that the general practitioners (GPs) use for validating the diagnosis suggested by the code. ⋯ Clinical Practice Research Datalink Read codes for CCMs have 93% PPV and most likely point to true cases. However, the accuracy of diagnosis dates and the age at diagnosis may not be as reliable. The findings of this study indicate that GPs use information beyond what is available for researchers in the EMR to confirm clinical diagnoses when responding to validation questionnaires. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
-
Pharmacoepidemiol Drug Saf · Nov 2013
Automatic detection of adverse events to predict drug label changes using text and data mining techniques.
The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. ⋯ Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks.