The Study of Signal Detection from National Reporting System of Adverse Drug Reaction Database in Taiwan
Date Issued
2006
Date
2006
Author(s)
Su, Chun-Hui
DOI
zh-TW
Abstract
Purpose
Spontaneous reporting systems (SRS) for adverse drug reactions (ADRs) remain a cornerstone of the Pharmacovigilance to early detect new ADRs and changes of the frequency of ADRs that are already known. In this study, we aimed to apply two simple data-mining algorithms developed in literature to the safety signal detection for the ADR database built in Taiwan. The feasibility of the two different methods was compared as well.
Methods
Reported cases collecting from 2003 to 2005 in the database of National reporting System of Adverse Drug Reactions in Taiwan were reviewed and coded with ATC code for suspected drugs and with MedDRA code for the reported adverse drug reactions. Preferred term (PT) of the MedDRA coding was used to represent the ADR. The priciples of proportional reporting ratio (PRR) and reporting odds ratio (ROR) were applied to ATC-PT pairs generated from the reports for signal detection.
Results
A total of 8439 reports over the 3-year period were included. The male/female ratio of patients was 0.97:1 and the average age of patients was 52.1±21.7 years old. The largest patients group was in the age range of 70-80 yrs (17.2%). The most frequently reported suspected drugs were the drugs used in nervous system (30.3%), followed by antiinfectives (27.2%), and cardiovascular- renal drugs (22.9%). According to MedDRA’s system organ classification, the most often reported ADRs were skin and subscutaneous tissue disorders (32.2%), followed by nervous system disorders (10.5%) and gastrointestinal disorders (10.0%). Type B ADRs are the main type of ADRs (67%). The causality of suspected drugs and ADRs was assessed as certain, probable, and possible for 91.3% of the cases.
A total of 14,072 ATC-PT pairs were generated from the reported cases, which belong to 7,364 different ATC-PT pairs. Among them, 974 (13.4%) ATC-PT pairs have the frequencies at least 3. After calculation, 648 signals (8.9%) were generated by PRR method and 722 signals (9.9%) were from ROR method. It is interesting to see that 647 different ATC-PT pairs (8.9%) were detected by both methods. Only 251 different pairs were not considered as signal by either method. If we took the 723 ATC-PT pairs to do the calculations with respect to the same drug class (based on ATC classification), a total of 244 signals (3.4%) were detected by both PRR and ROR methods.
Discussion
After searching, the relationship between suspected drug and ADR for 17 ATC-PT pairs (2.6% out of the 647 pairs) was not found in literatures. They may be a good candidate for further investigation. Moreover, antihyperlipidemia drug such as statins and fibrates related rhabdomyolysis and contrast medias related anaphylaxic shock are recommended for further carefully monitoring.
Conclusion
There is no single standard for signal detection method so far. The PRR method and ROR method may be good enough for safety signal detection for the ADR database of Taiwan. However, statistic approach signal detection method should be considered as a potential supplement to, and not substitute for, traditional pharmacovigilance strategies. Further clinical/pharmacological knowledge- based evaluation and research are required to confirm the casuality between the drug and the adverse drug reaction.
Subjects
藥物不良反應
藥物不良反應資料庫訊號偵測
資料探勘
Adverse drug reaction
ADR database
signal detection
data mining
Type
text
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