Statistical Analysis System of Spontaneous Adverse Drug Reaction Reports

Sira Kim; Boram Wang; Jungsun Lee; Bori Kim; Hyeno La; Young Min Park; Inyoung Choi

doi:10.0000/jkscpt.2012.20.2.155

Journal List > J Korean Soc Clin Pharmacol Ther > v.20(2) > 1055106

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Kim, Wang, Lee, Kim, La, Park, and Choi: Statistical Analysis System of Spontaneous Adverse Drug Reaction Reports

Original Article

Journal of Korean Society for Clinical Pharmacology and Therapeutics

Journal of Korean Society for Clinical Pharmacology and Therapeutics 2012; 20(2): 155-164.

Published online: 31 December 2012

DOI: https://doi.org/10.0000/jkscpt.2012.20.2.155

Statistical Analysis System of Spontaneous Adverse Drug Reaction Reports

Sira Kim¹, Boram Wang¹, Jungsun Lee², Bori Kim², Hyeno La³, Young Min Park⁴, Inyoung Choi¹

¹Graduate School of Healthcare Management and Policy, The Catholic of the University, Catholic Medical Center, Seoul, Korea.

²Seoul St' Mary Hospital, The Catholic of the University, Catholic Medical Center, Seoul, Korea.

³Department of Pharmacology, The Catholic of the University, Catholic Medical Center, Seoul, Korea.

⁴Department of Dermatology, The Catholic of the University, Catholic Medical Center, Seoul, Korea.

Corresponding author (iychoi@catholic.ac.kr)

Received 19 October 2012 Revised 16 November 2012 Accepted 20 November 2012

Abstract

Background

Spontaneous adverse drug reaction (ADR) reporting data has been used for safety of post-market drug surveillance. A system has been required that is able to detect signals associated with drugs by analyzing the collected ADR data.

Methods

We developed the web-based automated analysis system (ADR-detector). We used the data which reported ADR spontaneously between March 2009 and December 2010 to Korean Food and Drug Administration. We used 3 statistical indicators for evaluating ADR signals: proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The ADR reports which were detected as significant signals based on the indicators have been reviewed.

Results

Among 153,774 reports, 9,955 cases were related to 4 analgesics which were most frequently reported analgesic drugs during the study period. The numbers of ADR reports associated with each drug are as follow: 5,623 reports in tramadol (56.5 %), 1,720 reports in fentanyl (17.3 %), 1,463 reports in tramadol-combination (14.7 %), and 1,149 reports in ketorolac (11.5 %). Top 5 ADR were nausea (3,351 reports - 33.7 %), vomiting (1,755 reports - 17.6 %), dizziness (1,130 - 11.4 %), rash (412 reports - 4.1 %), and pruritus (354 reports - 3.6 %). 6,674 ADR reports were significant based on PRR and ROR, and 336 reports were significant based on IC.

Conclusion

By using the automated analysis system, not only statisticians but also general researchers are able to analyze ADR signals in real-time. Also ADR-detector would provide rapid review and cross-check of ADR.

Keywords: Automated analysis system, Signal, Data mining, Spontaneous adverse drug reaction reporting

Figures and Tables

Figure 1

Database for ADR-Detector.

Figure 2

Process of ADR (Adverse Drug Reaction)-Detector.

Figure 3

Programming code of ADR-Detector system.

Figure 4

Web main page of ADR-Detector system.

Figure 5

Example of signal search with tramadol containing products using ADR-Detector system.

Figure 6

Example of single drug adverse event signal using ADR-Detector system (case of tramadol).

Figure 7

Example of multi drug (4 types of an algesics) signal research using ADR-Detector.

Table 1

Definition and signal detection criteria of implemented data mining indices

^*PRR: proportional reporting ratio, ROR: reporting odds ratio, IC: information component, AE: adverse event.

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