Thromboembolic adverse event study of combined estrogen-progestin preparations using Japanese Adverse Drug Event Report database.

Combined estrogen-progestin preparations (CEPs) are one of the most commonly used birth control methods worldwide. CEPs have benefits beyond preventing an undesired pregnancy, including reduced ovarian and endometrial cancer risk, reduced dysfunctional uterine bleeding, decreased menstrual flow and menorrhagia, decreased primary dysmenorrhea, improved hirsutism and acne, and decreased risk of premenstrual syndrome/premenstrual dysphoric disorder [ 1 ]. Because CEPs are administered to healthy women over the long-term, patients should be carefully monitored for adverse events (AEs). CEPs, such as oral contraceptives (OCs), have a variety of side effects, of which thrombosis is the most frequent and important [ 2 ]. Numerous studies have demonstrated a relationship between CEPs and thromboembolism (TE), including venous thromboembolism (VTE) [ 1 – 15 ]. According to the American College of Obstetricians and Gynecologists, the incidence of TE increases from 1 to 5 occurrences per 10,000 women per year in non-OC users to 3 to 9 occurrences per 10,000 women per year in OC users [ 16 ]. A systematic review indicated that the risk of VTE in women of childbearing age who were non-OC users was 4 per 10,000 women per year, whereas in OC users, the risk was 7 to 10 per 10,000 women per year [ 4 ]. Appropriate treatment of TE after onset resolves the thrombus; however, in approximately 20%–50% of cases of TE, and in proximal deep vein thrombosis (DVT) to a greater extent, patients develop a post-thrombotic syndrome with lifelong problems including pain and swelling of the leg [ 17 , 18 ]. Rare thrombi cause pulmonary embolism, and 1 in 100 cases results in death [ 13 ]. Few studies have examined the association between CEP use and arterial thromboembolism (ATE), such as myocardial infarction and ischemic stroke [ 2 , 10 , 19 – 23 ]. Although ATE is less frequent than VTE, the consequences of ATE are often more serious [ 23 ]. The World Health Organization (WHO) has reported that the use of CEPs increased the risk of myocardial infarction by approximately 5-fold and the risk of ischemic stroke by approximately 3-fold [ 2 , 19 , 20 ]. Because VTE and ATE are rare AEs associated with CEP use, the implementation phase of epidemiologic research is difficult. The Pharmaceuticals and Medical Devices Agency (PMDA) in Japan has released the Japanese Adverse Drug Event Report (JADER) database, which is a large spontaneous reporting system (SRS) and reflects the realities of clinical practice in Japan [ 24 ]. Therefore, JADER has been used for pharmacovigilance assessments for rare AEs using the reporting odds ratio (ROR) [ 24 – 27 ]. Several studies have indicated that the risk of developing CEP-induced VTE is greatest during the first year of use [ 2 , 4 , 6 , 7 , 9 , 10 , 12 ]. However, detailed onset profiles of CEP-induced VTE are not clear. The analysis of time-to-onset data has been proposed as a new method of detecting signals for AEs in SRSs [ 24 , 27 , 28 ]. In this study, we applied the index of ROR to TE and evaluated time-to-onset profiles of TE for CEPs in the real world. Furthermore, association rule mining has been proposed as a new analytical approach for identifying undetected clinical factor combinations, such as possible risk factors, between variables in huge databases [ 29 – 31 ]. This is the first application of association rule mining for the detection of association rules between CEPs and TE.

The JADER database contained 338,224 reports from April 2004 to November 2014. The number of reports including TE was 14,593. The RORs (95% CI) of agents with Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 56.2 (44.3–71.4), 29.1 (23.5–35.9), 42.9 (32.3–57.0), 44.7 (32.7–61.1), and 38.6 (26.3–56.7), respectively ( Table 2 ). The PRRs (95% CI) of Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 16.8 (13.2–21.3), 13.2 (10.7–16.4), 15.4 (11.6–20.4), 15.6 (11.4–21.3), and 14.8 (10.0–21.7), respectively ( Table 2 ). a) SMQ code (20000082, 20000083, and 20000084) b) CEP: Combined Estrogen-progestin Preparations. c) EE: Ethinyl Estradiol. * Number of cases < 3. ** Number of cases < 2. † Non-case was not reported. In the mosaic plot, Dro-EE and Nor-EE were primarily administered to patients with dysmenorrhea and endometriosis, respectively ( Fig 2 ). The ROR and 95% CI of patients stratified by age in the 10–19, 20–29, 30–39, 40–49, and 50–59 -year-old groups were 19.4 (9.5–39.8), 29.7 (22.4–39.4), 48.9 (39.1–61.2), 66.1 (52.8–82.8), and 45.8 (25.6–81.7), respectively ( Table 2 ). The analysis of time-to-onset profiles revealed that the median values (25%–75%) of thrombosis caused by agents containing Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 150.0 (75.3–314.0), 128.0 (27.0–279.0), 204.0 (44.0–660.0), 142.0 (41.3–344.0), and 16.5 (8.8–32.0) days, respectively ( Table 3 ). The WSP β (95% CI) for Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 1.12 (1.01–1.23), 0.81 (0.72–0.91), 0.81 (0.68–0.96), 0.92 (0.78–1.07), and 0.62 (0.50–0.75), respectively ( Table 3 ). The association rule mining technique was applied to TE (as consequent) using demographic data such as age category and patient history. The apriori algorithm extracts frequent combinations from a large database to efficiently find sets of adverse events that occur more frequently than the minimum support threshold (defined as 0.00001 in this study). This generates sets of adverse drug reactions with the minimum confidence threshold (defined as 0.9 in this study). Furthermore, the maximum size of mined frequent item sets ( maxlen : a parameter in the arules package) was restricted to 3. The result of the mining algorithm was a set of 12 rules ( Table 4 ). The support , confidence , and lift of each association rule are summarized in Table 4 ; the association rules up to the twelfth position in descending order of the support are shown in Table 4 . {Des-EE, uterine leiomyoma} -> {TE} demonstrated a high support value ( Table 4 , id [1]). The association rules of {sodium ferrous citrate, Dro-EE} -> {TE}, {Dro-EE, hypoferric anemia} -> {TE}, and {Lev-EE, anemia} -> {TE} with high scores for lift and support were demonstrated ( Table 4 (id [2], [8], [11], Fig 3 ). Additionally, the association rules of the combination of {smoking, Nor-EE} were high ( Table 4 , id [3]). a) left-hand-sides of rule (antecedents) b) right-hand-side (consequents) The plot represents items and rules as vertices connected with directed edges. Relation parameters are typically added to the plot as labels on the edges or by varying the color or width of the arrows indicating the edges.

This study was the first to evaluate the correlation between CEP and TE using an SRS analysis strategy. Despite the limitations inherent to SRS, we showed the potential risk of TE during CEP use in a real-life setting. The present analysis demonstrated that the incidence of TE with Nor-EE use should be closely monitored for a short onset (within 3 weeks). Patients with anemia who are using CEPs might be advised to adhere to an appropriate care plan. We recommend the close monitoring of patients, and those who experience any symptoms suggestive of TE should be advised to discontinue administration.

AEs recorded from April 2004 to November 2014 in the JADER database were obtained from the PMDA website ( www.pmda.go.jp ). The JADER database consists of 4 tables: patient demographic information, such as sex, age, and reporting year (demo); drug information, such as non-proprietary name of the prescribed drug, route, and start and end date of administration (drug); adverse events, such as type, outcome, and onset date (reac); and primary disease (hist). We constructed a relational database that integrated the 4 data tables using FileMaker Pro 12 software (FileMaker, Inc., Santa Clara, CA, USA). The “drug” file included the role codes assigned to each drug: suspected, concomitant, and interacting drugs (higiyaku, heiyouyaku, and sougosayou in Japanese, respectively). The suspected drug records were extracted and analyzed in this study. Five CEPs (drospirenone combined with ethinyl estradiol (EE, Dro-EE), norethisterone with EE (Ne-EE), levonorgestrel with EE (Lev-EE), desogestrel with EE (Des-EE), and norgestrel with EE (Nor-EE)) were assessed. Since EE is not a constituent of Menoaid (ASKA Pharmaceutical Co., Ltd., Tokyo, Japan) and Wellnara (Bayer Yakuhin, Ltd., Osaka, Japan), they were excluded in the analysis. The number of reported cases of E·P·Hormone depot (ASKA Pharmaceutical Co., Ltd., Tokyo, Japan), Lutes depot (Mochida Pharmaceutical Co., Ltd., Tokyo, Japan), Lutedion (ASKA Pharmaceutical Co., Ltd., Tokyo, Japan), and Sophia (ASKA Pharmaceutical Co., Ltd., Tokyo, Japan) were low and, therefore, they were not assessed in the analysis. AEs in the JADER database are coded according to the terminology preferred by the Medical Dictionary for Regulatory Activities/Japanese version 17.1 (MedDRA/J) ( www.pmrj.jp/jmo/php/indexj.php ). The standardized MedDRA Queries (SMQ) index consists of groupings of MedDRA terms, ordinarily at the preferred term (PT) level, that relate to a defined medical condition or area of interest [ 32 ]. We used the SMQ for embolic and thrombotic events, arterial (SMQ code: 20000082), embolic and thrombotic events, vessel type unspecified and mixed arterial and venous (SMQ code: 20000083), and embolic and thrombotic events, venous (SMQ code: 20000084; Table 1 ). a) Medical Dictionary for Regulatory Activities b) Standardized MedDRA Queries The mosaic plot of the two-way frequency table was constructed with the age-category (X) and primary disease (Y). A mosaic plot is divided into rectangles so that the vertical length of each rectangle is proportional to the proportion of the Y variable at each level of the X variable. We assessed the association between CEPs and TE using the ROR, which is an established parameter for pharmacovigilance research. The ROR is the ratio of the odds of reporting an adverse event versus all other events associated with the drug of interest compared with the reporting odds for all other drugs present in the database [ 33 ]. We calculated the ROR using a two-by-two contingency table by defining the rows using CEPs and all other drugs and the columns using TE and all other adverse events ( Fig 1 ). RORs are expressed as point estimates with 95% confidence intervals (CI). The detection of a signal was dependent on the signal indices exceeding a predefined threshold. Safety signals are considered significant when the ROR estimates and the lower limits of the corresponding 95% CI exceed 1. At least 2 cases are required to define a signal [ 33 , 34 ]. Proportional reporting ratios (PRRs) are measures of disproportionality used for detecting signals in SRS databases [ 35 ]. PRRs are calculated from the same 2 × 2 tables and the ROR is identical to the calculation of relative risk (RR) from a cohort study, i.e., [a / (a + c)] / [b / (b + d)]. If the drug and adverse event are independent, the expected value of the PRR is 1. The minimum criteria for signal detection are as follows: 3 or more cases, PRR of at least 2, and Chi-square of at least 4. Time-to-onset duration of the data from the JADER database was calculated from the time of the patient’s first prescription to the occurrence of the AE. The median duration, quartiles, and Weibull shape parameters (WSPs) were used to evaluate the dates from administration to development of TE [ 27 , 36 – 38 ]. The WSP test is used for the statistical analysis of time-to-onset data and can describe the non-constant rate of the incidence of AE reactions [ 24 , 39 ]. The scale parameter α of the Weibull distribution determines the scale of the distribution function. A larger scale value stretches the distribution. A smaller scale value shrinks the data distribution. The shape parameter β of the Weibull distribution indicates the hazard without a reference population. When β is equal to 1, the hazard is estimated to be constant over time. When β is greater than 1 and the 95% CI of β excludes 1, the hazard is considered to increase over time. When β is smaller than 1 and the 95% CI of β excludes 1, the hazard is considered to decrease over time [ 39 ]. The data analyses were performed using JMP 11.2 (SAS Institute Inc., Cary, NC, USA). The association rule mining approach attempts to search the frequent items in databases and discover interesting relationships between variables. Given a set of transactions T (each transaction is a set of items), an association rule can be expressed as X -> Y, where X and Y are mutually exclusive sets of items [ 40 ]. The rule’s statistical significance and strength are measured by the support and confidence , respectively. Support is defined as the percentage of transactions in the data that contain all items in both the antecedent (left-hand-side of rule: lhs) and the consequent of the rule (right-hand-side of rule: rhs) [ 40 ]. The support indicates how frequently the rule occurs in the transaction. The formula for calculating support is as follows: S u p p o r t   = P ( X  ∩  Y ) = { X  ∩  Y } / { D } D is the total number of the transaction. Confidence corresponds to the conditional probability P (Y|X). A rule with high confidence is important because it provides an accurate prediction of the association of the items in the rule. The formula for calculating confidence is as follows: C o n f i d e n c e   = P ( X  ∩  Y ) / P ( X ) Lift represents the ratio of probability. For a given rule, X and Y occur together to the multiple of the two individual probabilities for X and Y; that is, L i f t   = P ( X  ∩  Y ) / P ( X )  P ( Y ) Since P(Y) appears in the denominator of the lift measure, the lift can be expressed as the confidence divided by P(Y). The lift can be evaluated as follows: lift = 1, if X and Y are independent; lift > 1, if X and Y are positively correlated; lift < 1, if X and Y are negatively correlated. We performed these analyses using the apriori function of the arules library in the arules package of R version 3.3.3 software [ 41 ].