Influence of the MedDRA® hierarchy on pharmacovigilance data mining results

Pearson, Ronald K.; Hauben, Manfred; Goldsmith, David I.; Gould, A. Lawrence; Madigan, David; O’Hara, Donald J.; Reisinger, Stephanie J.; Hochberg, Alan M.

doi:10.1016/j.ijmedinf.2009.01.001

« Previous Next »International Journal of Medical Informatics
Volume 78, Issue 12 , Pages e97-e103, December 2009

Influence of the MedDRA^® hierarchy on pharmacovigilance data mining results

Ronald K. Pearson
- ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102, USA
Manfred Hauben
- Pfizer Corporation, New York University School of Medicine, New York, NY, USA
- New York Medical College, Valhalla, NY, USA
- Brunel University, West London, UK
David I. Goldsmith
- Goldsmith Pharmacovigilance and Systems, New York, NY, USA
A. Lawrence Gould
- Merck Research Laboratories, West Point, PA, USA
David Madigan
- Columbia University, New York, NY, USA
Donald J. O’Hara
- ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102, USA
Stephanie J. Reisinger
- ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102, USA
Alan M. Hochberg
- ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102, USA
- Corresponding author at: ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102, USA. Tel.: +1 717 635 2124.

Received 17 November 2008; accepted 13 January 2009. published online 19 February 2009.

Abstract

Purpose

To compare the results of drug safety data mining with three different algorithms, when adverse events are identified using MedDRA¹ Preferred Terms (PT) vs. High Level Terms (HLT) vs. Standardised MedDRA Queries (SMQ).

Methods

For a representative set of 26 drugs, data from the FDA Adverse Event Reporting System (AERS) database from 2001 through 2005 was mined for signals of disproportionate reporting (SDRs) using three different data mining algorithms (DMAs): the Gamma Poisson Shrinker (GPS), the urn-model algorithm (URN), and the proportional reporting rate (PRR) algorithm. Results were evaluated using a previously described Reference Event Database (RED) which contains documented drug–event associations for the 26 drugs. Analysis emphasized the percentage of SDRs in the “unlabeled supported” category, corresponding to those adverse events that were not described in the U.S. prescribing information for the drug at the time of its approval, but which were supported by some published evidence for an association with the drug.

Results

Based on a logistic regression analysis, the percentage of unlabeled supported SDRs was smallest at the PT level, intermediate at the HLT level, and largest at the SMQ level, for all three algorithms. The GPS and URN methods detected comparable percentages of unlabeled supported SDRs while the PRR method detected a smaller percentage, at all three MedDRA levels. No evidence of a method/level interaction was seen.

Conclusions

Use of HLT and SMQ groupings can improve the percentage of unlabeled supported SDRs in data mining results. The trade-off for this gain is the medically less-specific language of HLTs and SMQs compared to PTs, and the need for the added step in data mining of examining the component PTs of each HLT or SMQ that results in a signal of disproportionate reporting.

Keywords: MedDRA, Data mining, Terminology, Proportional reporting rate, Urn model, Gamma Poisson Shrinker