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INTRODUCTION
A signal in pharmacovigilance was defined by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) as a “Reported information on a possible causal relationship between an adverse event (AE) and a drug, the relationship being unknown or incompletely documented previously.”1
Data mining, a signal detection method, uses a spontaneous reporting system to detect AE signals and has had an important role in pharmacovigilance for the last four decades.2 Physicians, pharmacists and consumers are recommended to report AEs that are suspected to be AEs to a spontaneous reporting system.3 Quantitative analysis methods, such as the reporting odds ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) analysis, could reveal important signals by analyzing disproportionately collected reports about drugs and clinical events.4
Imipenem is a member of the carbapenem class of antibiotics. It is known to have a wide spectrum of activity, and therefore, has commonly been used for Gram-positive and -negative, aerobic, and anaerobic bacteria.5 In US Food and Drug Administration (FDA) drug label information, imipenem presents severe adverse drug reactions (ADRs), such as potential seizure (0.2%). It also presents common ADRs, such as nausea (2.0%), vomiting (1.5%), diarrhea (1.8%), rash (0.9%), and so on.6 The drug label information regarding imipenem ADRs from the Ministry of Food and Drug Safety (Korea) is similar to that of the FDA.7 In Korea, however, signal detection associated with imipenem using the Korean spontaneous AEs reporting system database has never been conducted. Therefore, we first analyzed the Korean spontaneous reporting system database regarding imipenem and compared it with the drug label information of 8 other countries to detect signals that are not currently listed in the labels.
MATERIALS AND METHODS
Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database
The Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD) includes AE data from December 1988 to June 2014. The data were screened for detection of input error, logical error and assigned code by drug information and AEs before performing statistical analysis. This database is managed by KIDS, and anyone authorized by KIDS can use the data, and it is composed of patient information, drug information, AE information, seriousness of AE, reporter information, primary causality assessment, and disease history. These data have information about suspected AEs coded with the WHO Adverse Reaction Terminology (WHO-ART) codes-Preferred Term level (PT).8 These codes were developed 30 years ago by the WHO and have been widely used.9 We use the combination of drug ingredients and WHO-ART PT codes (PT) to find signals. It should be noted that information from the KIDS-KD is reported regardless of causal relationship. Thus, AE information from other countries and assessment of patient's underlying disease or medical records are needed.8
Disproportionality measurement for signal detection
Disproportionality measurement is one of the techniques used to detect adverse drug event signals, and Netherlands Pharmacovigilance Foundation, the UK Yellow Card database, and WHO-UMC use this method to find AE signals.10 To make a 2×2 table, we classified rows with imipenem and other drugs, except imipenem, and categorized columns with specific AEs and all other AEs (Table 1).
We calculated the PRR, ROR, and information component (IC) of BCPNN to detect signals. The criteria of signal detection was a PRR and ROR of at least 2, chi-squared value of at least 4 and 3 or more cases,11 and that for IC was a larger than zero lower limit of the 95% confidence interval of IC (Table 2).12
Statistical analysis
We used three methods for signal detection. Nevertheless, there is no ‘gold standard’ method for signal detection.13 We applied three methods to target imipenem and all other antibiotics and drugs. In general, the disproportionality calculating measure compares imipenem with all other drugs.1415
A signal was defined when it satisfied all three methods (PRR, ROR, and IC). We also analyzed the number of AEs reported from imipenem use, all drugs and all antibiotics per year and the characteristics of imipenem AE reports. Finally, we compared the drug label informations from South Korea, Canada, the USA, the UK, Japan, Germany, Switzerland, Italy, and France. All signals have the possibility for disease spill-over (code of treated disease as ADRs), therefore, we considered the clinical aspects.16 All statistical analyses were performed using SAS (Release 9.4, SAS Institute, Inc., Cary, NC, USA) and Microsoft Excel 2010.
RESULTS
The total number of suspected AE reports to the KIDS-KD from December 1988 to June 2014 was 807582. During the same period, the number of AE reports with all antibiotics was 192510 and with imipenem was 3382. The number of reports continued to increase year after year (Table 3).
The characteristics of reports in KIDS-KD are presented in Table 4. They are presented by gender of the patient, suspected AEs, type of reports, identification of reporter, and reporting institution (Table 4). All of the characteristics are different between imipenem and all of the antibiotics, and between imipenem and all of the drugs (p value<0.001). Drug eruption accounted for highest number of AEs reported with imipenem, 353 (10.4%), followed by nausea (207 reports, 6.1%), sepsis (156 reports, 4.6%), and pneumonia (145 reports, 4.3%) (Supplementary Table 1, only online). We calculated imipenem AEs signals compared with all antibiotics and drugs. Compared with all antibiotics, the number of signals detected by the PRR method was 58, 58 by ROR method, 154, and in more than 3 qualified cases by IC method, and 76 by IC method. Compared to all of the other drugs, the number of signals detected by the PRR method was 53, 53 by ROR method, 143, and in more than 3 qualified cases by IC method, and 71 by IC method. Finally, 58 and 53 signals satisfied all three methods.
DISCUSSION
This study describes signal detections after treatment with imipenem, based on KIDS-KD data. A great deal of research to detect individual adverse effects, such as seizure,517 digestive side effects, electrolyte abnormality, and hematopoietic abnormality, including anemia, have been reported.1819 Babinchak, et al.20 indicated imipenem-associated cardiovascular system AEs. In their study, one each case of angina pectoris and left cardiac failure were reported among 12 imipenem AE cases. Torres found that cardiac arrest occurred in one patient among 34 patients using imipenem.21 However, cardiac arrest, cardiac failure, myocardial infarction, Parkinson's syndrome and prostate enlargement were detected for the first time when the signal detection methods were used. The exact mechanism of heart disease by imipenem is not clear, but electrolyte exchange, a known ADR of imipenem, is a significant adjuvant marker of acute myocardial infarction.22 Furthermore, seizure, a serious ADR of imipenem, is associated with an increased risk of sudden cardiac death,23 and bradycardia or tachycardia is included in the drug label information in some countries. Therefore, the ADRs associated with cardiac problems are thought to be worthy of further study. In the present study, Parkinson's syndrome and prostate enlargement, which have never been reported, were detected as signals, therefore, further AE studies are needed to evaluate the causality of this signal.
The number of detected signals by IC was more than detected by the ROR or PRR criteria. This difference mostly results from the minimum number of cases included in the criteria. Lindquist, et al.24 developed the BCPNN method, which detected signals with 44% positive predictive values and 85% negative predictive values, indicating that BCPNN is a valuable tool to find early signals. ADR information was derived from data obtained from preclinical studies and clinical trials, however, it may not include rare ADRs, which are caused by interactions with other existing drugs or ADRs observed after a long exposure.25 Therefore, observational studies using a large computerized database for a long time period after marketing should be encouraged.
Our present study employed disproportionality methods not only to compare imipenem with all other drugs, but also to compare it with all other antibiotics. We found that the results were different when using disproportionality methods were used to compare impenem with the same drug class and all other drugs. Compared with all other antibiotics, signals detected were more than when compared to all other drugs. However, since there is the possibility of a false positive, a validity evaluation is required.
This study has the following strengths. First, this study is focused on imipenem, which has been used in more than 26 million patients26 and is a part of the carbapenem family, whose worldwide usage increased by 40 percent between 2000 and 2010. This is a large increase compared to all other antibiotics used, which increased by 30 percent during the same period.27 For many complicated bacterial infections, the use of the carbapenem class of antibiotics is considered to be a “last resort”. 28 Furthermore, this study is important, since the imipenem-associated adverse effects will likely continue to increase in the future. Second, this is the first study to analyze imipenem signal detection using the KIDS-KD: this database contains all of the AEs spontaneously reported from December 1988 to June 2014 in Korea.
Like many other studies of AEs using spontaneous reporting systems, our present study has some limitations. First, by using a spontaneous AE reporting system, the detected signals may not have a causal relationship. KIDS-KD contains information of causality assessment. However, causality assessment information of imipenem dealt only 53% and that of all drugs only 47%. Therefore, it is difficult to evaluate causality of the KIDS-KD in terms of content or format, and additional pharmacoepidemiologic (PE) studies are needed to evaluate the causal relationship. Second, the present study has various degrees of underreporting, therefore, the true incidence of signals cannot be estimated.29 Third, delays in reporting and incomplete information are also factors that degrade the quality of signals from spontaneous reporting systems.30 Fourth, the WHO-ART codes (PT) and the actual disease may not match. WHO-ART codes (PT) are selected as the preferred term by the user.31 For example, symptoms with the same morphological aspect, but different clinical aspects cannot be processed appropriately by the WHO-ART system.32 Fifth, there is no information about the total number of patients who prescribed the drug. Thus, it is necessary to conduct PE studies, including a cohort study, considering the actual number of patients who prescribed the drug.
Early detection of signals is very important to secure patients for drug safety. Recently, the number of reported AEs in South Korea has grown explosively. Academies, pharmaceutical companies and institutions need to cooperate actively to effectively detect signals using the KIDS-KD.
In conclusion, we found signals that have not been mentioned previously. If necessary, a causality assessment is required by conducting well designed PE studies, including a cohort study, case-control study, or case crossover study. Clinicians and pharmacists are expected to be able to better care for patients based on the scientific evidence unraveled in these studies.
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