Practical pharmacovigilance analysis strategies

Innovations in pharmacovigilance studies of medicines in older people

Pharmacovigilance is defined by the World Health Organization as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine related problem". Pharmacovigilance studies are critical for detecting and assessing adverse events of medicines that may not have been observed in clinical trials. This activity is especially important in older people who are often excluded from clinical trials as they have multiple chronic conditions and use multiple medicines for longer durations than the clinical trials. In this narrative review we describe innovative methods in pharmacovigilance studies of medicines in older people that leverage the increasing availability of digital health technologies, electronic health records and real-world health data to identify and quantify medication related harms in older people.

Disproportionality Analysis of Abemaciclib in the FDA Adverse Event Reporting System: A Real-World Post-Marketing Pharmacovigilance Assessment

BACKGROUND AND OBJECTIVE

Abemaciclib, a cyclin-dependent kinase 4 and 6 inhibitor, demonstrated efficacy in women with hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer. Because of the limitations of clinical trials, which are not representative of large real-world populations, rare events and long-term safety concerns cannot be detected. The current study aimed to evaluate the adverse events of abemaciclib through data mining of the Food and Drug Administration Adverse Event Reporting System (FAERS).

METHODS

Reporting odds ratio and Bayesian confidence propagation neural network of information components were used to quantify the adverse event signals of abemaciclib from the third quarter of 2017 to the first quarter of 2022. Serious and non-serious cases were compared using the Mann-Whitney U test or Chi-squared test, and clinical priority was assigned to signals by scoring (range 0-10 points) five features using a rating scale.

RESULTS

A total of 6125 reports of abemaciclib as the "primary suspected" and 72 significant adverse events of abemaciclib were identified. Common adverse events, such as diarrhea, neutropenia, alanine transaminase, aspartate transaminase, and serum creatinine increases, and other adverse events, including thrombosis, deep vein thrombosis, pulmonary embolism, interstitial lung disease, and pneumonitis were of high concern. Of note, 17 preferred terms were classified as unexpected adverse events that uncovered in the label. In addition, 1, 26, and 45 adverse events were identified as strong, moderate, and weak clinical priorities. The median time to onset for strong, moderate, and weak clinical priority signals was 49, 22, and 28 days, respectively. All of the disproportionality signals had early failure type features, suggesting that adverse events of abemaciclib gradually decreased over time.

CONCLUSIONS

The discovery of disproportionality signals could potentially prompt improved awareness of toxicities for abemaciclib, and the results of time to onset, serious and non-serious reports, and clinical priority analyses provided some supporting evidence for clinicians to manage adverse events.

Sensitivity and specificity in signal detection with the reporting odds ratio and the information component

PURPOSE

As measures of association between an adverse drug reaction (ADR) and exposure to a drug the reporting odds ratio (ROR) and the information component (IC) can be used. We sought to test the reliability of signal detection with these.

METHODS

We simulated ADR counts as binomially distributed random numbers for different expected ADR frequencies and theoretical reporting odds ratios (RORs). We then calculated the empirical IC and the empirical ROR and their confidence intervals. The rate of signals that was detected despite a theoretical ROR of 1 represented the false positive rate, and represented the sensitivity if the ROR was >1.

RESULTS

For expected case counts below 1 the false positive rate oscillates from 0.01 to 0.1 even though 0.025 were intended. Even beyond expected case counts of 5 oscillations can cover a range of 0.018 to 0.035. The first n oscillations with the largest amplitude are eliminated if a minimum case count of n is required. To detect an ROR of 2 with a sensitivity of 0.8, a minimum of 12 expected ADRs are required. In contrast, 2 expected ADRs suffice to detect an ROR of 4.

CONCLUSION

Summaries of measures for disproportionality should include the expected number of cases in the group of interest if a signal was detected. If no signal was detected the sensitivity for the detection of a representative ROR or the minimum ROR that could be detected with probability 0.8 should be reported.

Hypertensive events after the initiation of contemporary cancer therapies for breast cancer control

BACKGROUND

Contemporary therapies improve breast cancer (BC) outcomes. Yet, many of these therapies have been increasingly linked with serious cardiotoxicity, including reports of profound hypertension. Yet, the incidence, predictors, and impacts of these events are largely unknown.

METHODS

Leveraging two large U.S.-based registries, the National Inpatient Sample (NIS) and the Food and Drug Administration Adverse Event Reporting System (FAERS) databases, we assessed the incidence, factors, and outcomes of hypertensive events among BC patients from 2007 to 2015. Differences in baseline characteristics, hypertension-related discharges, and complications were examined over time. Further, we performed a disproportionality analysis using reporting-odds-ratios (ROR) to determine the association between individual BC drugs and hypertensive events. Utilizing an ROR cutoff of >1.0, we quantified associations by drug-class, and individual drugs with the likelihood of excess hypertension.

RESULTS

Overall, there were 5,464,401 BC-admissions, of which 46,989 (0.8%) presented with hypertension. Hypertensive BC patients were older, and saw initially increased in-hospital mortality, which equilibrated over time. The mean incidence of hypertension-related admissions was 732 per 100,000 among BC patients, versus 96 per 100,000 among non-cancer patients (RR 7.71, p < 0.001). Moreover, in FAERS, those with hypertension versus other BC-treatment side-effects were more frequently hospitalized (40.1% vs. 36.7%, p < 0.001), and were most commonly associated with chemotherapy (45.9%). Outside of Eribulin (ROR 3.36; 95% CI 1.37-8.22), no specific drug was associated with a higher reporting of hypertension; however, collectively BC drugs were associated with a higher odds of hypertension (ROR 1.66; 95% CI 1.09-2.53).

CONCLUSIONS

BC therapies are associated with a substantial increase in limiting hypertension.

Rituximab and Pyoderma Gangrenosum: An Investigation of Disproportionality Using a Systems Biology-Informed Approach in the FAERS Database

BACKGROUND

Studies have found an increased risk of pyoderma gangrenosum associated with rituximab. The structural properties and pharmacological action of rituximab may affect the risk of pyoderma gangrenosum. Additionally, pyoderma gangrenosum is associated with autoimmune disorders for which rituximab is indicated.

OBJECTIVE

We aimed to determine whether rituximab is disproportionally associated with pyoderma gangrenosum using a systems biology-informed approach.

METHODS

Adverse event reports were extracted from the US Food and Drug Administration Adverse Event Reporting System (FAERS, 2013-20). The Bayesian Confidence Propagation Neural Network Information Component was used to test for disproportionality. Comparators used to determine potential causal pathways included all other medicines, all medicines with a similar structure (monoclonal antibodies), all medicines with the same pharmacological target (CD20 antagonists) and all medicines used for the same indication(s) as rituximab.

RESULTS

Thirty-two pyoderma gangrenosum cases were identified, 62.5% were female, with a median age of 48 years. There was an increased association of pyoderma gangrenosum with rituximab compared with all other medicines (exponentiated Information Component 6.75, 95% confidence interval (CI) 4.66-9.23). No association was observed when the comparator was either monoclonal antibodies or CD20 antagonists. Conditions for which an association of pyoderma gangrenosum with rituximab was observed were multiple sclerosis (6.68, 95% CI 1.63-15.15), rheumatoid arthritis (2.67, 95% CI 1.14-4.80) and non-Hodgkin's lymphoma (2.94, 95% CI 1.80-3.73).

CONCLUSIONS

Pyoderma gangrenosum was reported more frequently with rituximab compared with all other medicines. The varying results when restricting medicines for the same condition suggest the potential for confounding by indication. Post-market surveillance of biologic medicines in  FAERS should consider a multi-faceted approach, particularly when the outcome of interest is associated with the underlying immune condition being treated by the medicine of interest.

Frequently reported adverse events of rebamipide compared to other drugs for peptic ulcer and gastroesophageal reflux disease

This study aimed to detect safety signals of rebamipide and search for adverse events (AEs) of rebamipide that are more common than those of other drugs for peptic ulcer disease (PUD) and gastroesophageal reflux disease (GERD) in the elderly population. A total of 101,735 AE reports for drugs used to treat PUD and GERD between 2009 and 2018 from the KIDS-KAERS database (KIDS-KD) were used. Disproportionality analysis was performed to calculate the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). Drug labels in Korea, Japan, and China were reviewed to identify signals that have been listed. AEs frequently reported in the elderly population were also analyzed. Seriousness and median time to AEs were evaluated for statistically significant AEs. A total of 14 signals were detected, and 4 signals (dry mouth, dermatitis, purpura/petechia, and fluid overload) were not listed on drug labels; however, they may be included as part of other listed AEs. In the elderly population, 11 AEs such as dyspepsia/indigestion/gastrointestinal distress, somnolence, dry mouth, and edema were common. These AEs were not serious and occurred within 2–9 days. This study identified possible AEs of rebamipide, a relatively safe drug.

Signal detection of drospirenone-containing oral contraceptives: a disproportionality analysis using the Korea Adverse Event Reporting System Database, 2008-2017

OBJECTIVES

To detect the signals for drospirenone-containing oral contraceptives (DCOCs) and describe the reporting pattern of adverse events (AEs) caused by DCOCs compared with levonorgestrel/desogestrel/gestodene-containing (second/third generation) oral contraceptives.

DESIGN

A descriptive analysis of claims data.

SETTING

The Korea Institute of Drug Safety & Risk Management-Korea Adverse Event Reporting System Database from 1 February 2008 to 31 December 2017.

OUTCOME MEASURES

Signals for DCOCs were identified using three data mining indices. The characteristics, death cases, and the annual pattern of AE reports were compared between DCOCs and second/third generation oral contraceptives.

RESULTS

Of the 242 DCOC-related AEs, 54 signals were detected and 10 were identified as new signals that were not included in Korea, US and UK label. The newly detected signals include deep vein thrombophlebitis and frequent urination. Serious AEs were more likely to be reported with DCOCs (7.85%) than with second/third generation oral contraceptives (2.92%). Five deaths after use of DCOCs were reported with vascular AEs, such as pulmonary embolism and thrombosis, whereas one death after use of second/third generation oral contraceptives was reported with the cardiac arrest.

CONCLUSIONS

We identified 10 new signals related to DCOCs that were not included in the current label. Additionally, we found higher reports of the deaths and vascular AEs associated with DCOCs than with second/third generation oral contraceptives, which warrants careful monitoring to ensure the safe use of DCOCs.

Topiramate-related adverse events: Pattern and signals in the Korea Adverse Event Reporting System, 2010-2017

Despite safety concerns associated with topiramate use, the pattern of adverse events and signal analysis of antiepileptic drugs remain elusive.We aimed to determine patient demographics and characteristics of reported AEs of topiramate and to detect the associated signals by comparing those of other antiepileptics.We used the Korea Institute of Drug Safety & Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD) from 2010 to 2017 to determine patient demographics and characteristics of reported AEs for topiramate and other antiepileptics. The proportional reporting ratio, reporting odds ratio, and information component were used in signal detection. Signals were compared against drug labels in Korea, the UK, the EU, and the US.A total of 1300 adverse events cases of topiramate were reported, and the number of topiramate-adverse event pairs was 1861. For topiramate, the proportion of women of childbearing age (20-39 years) with adverse events was more than double that for other antiepileptics. A majority of the 36 detected signals were of neuropsychiatric disorders such as cognitive disorders, concentration impaired, amnesia, hypoaesthesia. Patients with topiramate-induced adverse events were likely to be young and female. Also, adverse events related to carbonic anhydrase isoenzyme showed specifically great disproportionalities.Rigorous clinical management is needed to ensure proper and safe use of topiramate. Special precautions should be taken when prescribing in women of childbearing age.

A stratification method based on clustering for the minimization of data masking effect in signal detection

Background

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection. Many previous studies can be roughly classified into three categories: data removal, regression and stratification. However, frequency differences of adverse drug events (ADEs) reports, which would be an important factor of masking, were not considered in these methods. The aim of this study is to explore a novel stratification method for minimizing the impact of frequency differences on real signals masking.

Methods

Reports in the Chinese Spontaneous Reporting Database (CSRD) between 2010 and 2011 were selected. The overall dataset was stratified into some clusters by the frequency of drugs, ADRs, and drug-event combinations (DECs) in sequence. K-means clustering was used to conduct stratification according to data distribution characteristics. The Information Component (IC) was adopted for signal detection in each cluster respectively. By extracting ADRs from drug product labeling, a reference database was introduced for performance evaluation based on Recall, Precision and F-measure. In addition, some DECs from the Adverse Drug Reactions Information Bulletin (ADRIB) issued by CFDA were collected for further reliability evaluation.

Results

With stratification, the study dataset was divided into 21 clusters, among which the frequency of DRUGs, ADRs or DECs followed the similar order of magnitude respectively. Recall increased by 34.95% from 29.93 to 40.39%, Precision reduced by 10.52% from 54.56 to 48.82%, while F-measure increased by 14.39% from 38.65 to 44.21%. According to ADRIB after 2011, 5 DECs related to Potassium Magnesium Aspartate, 61 DECs related to Levofloxacin Hydrochloride and 26 DECs related to Cefazolin were highlighted.

Conclusions

The proposed method is effectively and reliably for the minimization of data masking effect in signal detection. Considering the decrease of Precision, it is suggested to be a supplement rather than an alternative to non-stratification method.

An evaluation of statistical approaches to postmarketing surveillance

Safety of medical products presents a serious concern worldwide. Surveillance systems of postmarket medical products have been established for continual monitoring of adverse events (AEs) in many countries, and the proliferation of electronic health record systems further facilitates continual monitoring for AEs. We review existing statistical methods for signal detection that are mostly in use in postmarketing safety surveillance of spontaneously reported AEs and we study their performance characteristics by simulation. We compare those with the likelihood ratio test (LRT) method (appropriately modified for use in pharmacovigilance) and use three different methods to generate data (AE based, drug based, and a modification of the method of Ahmed et al). Performance metrics include type I error, power, sensitivity, and false discovery rate, among others. The results show superior performance of the LRT method in almost all simulation experiments. An application to the FDA Adverse Event Reporting System database is illustrated using rhabdomyolysis-related preferred terms reported to FDA during the third-quarter of 2014 to the first-quarter of 2017 for statin drugs. We present a critical discussion and recommendations for use of these methods.

Bayesian hierarchical methods in the detection of potentially teratogenic first-trimester medications

PURPOSE

Bayesian hierarchical models (BHMs) have been used to identify adverse drug reactions, allowing information sharing amongst adverse reactions and drugs expected to have similar properties. This study evaluated the use of BHMs in the routine signal detection analyses of potential first-trimester teratogens, where these models have not previously been applied.

METHODS

Data on 15 058 malformed foetuses exposed to first trimester medications (1995-2011) from 13 European congenital anomaly (CA) registries were analysed. The proportion of each CA in women taking a specific medication was compared with the proportion of that CA in all other women in the dataset (55 CAs × 523 medications). BHMs were grouped by either medications or CAs or by both simultaneously, and the results compared with analysing each medication-CA combination separately and adjusting for multiplicity using a double false discovery rate (FDR) procedure. The proportions of "high-risk" medications (medications which have been shown to carry a moderate to high risk of foetal malformations) identified as potential signals were compared, as well as the total number of potential signals requiring follow up (the effective workload).

RESULTS

BHMs identified more high-risk medications than the double FDR method, but the effective workload was larger. A BHM grouping both medications and CAs, for example, identified 23% of high-risk medications compared with 14% by the double FDR; however, there was an increase from 16 to 71 potential signals requiring follow up.

CONCLUSION

For comparable effective workloads, BHMs did not outperform the double FDR, which is comparatively straightforward to implement and is therefore recommended for continued use in teratogenic signal detection analyses.

Pattern of adverse events induced by aflibercept and ranibizumab: A nationwide spontaneous adverse event reporting database, 2007-2016

Data regarding the safety of anti-vascular endothelial growth factor (anti-VEGF) treatment is limited.To compare the adverse events (AEs) induced by aflibercept and ranibizumab using a spontaneous reporting system and determine the signals.We used data from the Korea Institute of Drug Safety & Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD), collected between 2007 and 2016. Differences in patient demographics, report type, reporter, causality, and serious-AEs between aflibercept and ranibizumab were compared. Metrics including proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were used to compare signals with the AEs on the drug labels in the United States of America and Korea. Logistic regression analysis was performed to identify AEs that are more likely to occur with drug use.A total of 32 aflibercept and 103 ranibizumab cases of AEs were identified. The proportion of AEs that were reported voluntarily was higher with aflibercept (50.5%) use than ranibizumab (4.9%), whereas the AEs reported by post-marketing surveillance were higher with ranibizumab (46.6%) use than aflibercept (31.3%). The percentage of AEs in patients >60 years old, reports by consumers, and the ratio of SAEs to AEs associated with aflibercept (84. %, 9.4%, and 75.0%, respectively) were higher than those of ranibizumab (77.7%, 1.9%, and 19.4%, respectively). The number of newly detected AEs after aflibercept and ranibizumab treatment was 3 and 8, respectively. Among these, conjunctivitis and medicine ineffective were not included on the aflibercept and ranibizumab labels, respectively. Endophthalmitis (OR 6.96, 95% CI 2.74-17.73) was more likely to be reported in patients with aflibercept than in patients without aflibercept, whereas medicine ineffective (OR 18.49, 95% CI 2.39-143.29) and retinal disorder (OR 7.03, 95% CI 1.60-30.96) were more likely to be reported in patients with ranibizumab than in patients without ranibizumab.New signals have been identified for aflibercept and ranibizumab. Further research is necessary to evaluate the causality of AEs that were detected as signals in this study.

Pharmacovigilance in India in Comparison With the USA and European Union: Challenges and Perspectives

Pharmacovigilance (PV) is an integral part of the drug regulation system. PV plays an indispensable role in the identification, assessment, and publicizing of adverse drug reactions (ADRs) through various methods. ADRs account for serious harm to the patients and even lead to morbidity and mortality. The PV databases help in the promotion of safe drug use and protection of public health safety. This article compares the PV system in the USA, Europe, and India, highlighting the challenges and future perspectives to be adapted to widen the horizon of the existing PV structure in India. In India, PV programs are still at the dawning stage when paralleled to the other countries. The National Pharmacovigilance Program and the Pharmacovigilance Program of India are the most recent advancements in this field in the country. The USA and Europe have well-established PV systems in place thanks to technological progress and other resources. India is the largest producer of pharmaceuticals in the world and a major clinical research hub; hence, it requires a more stringent PV setup. With the increase in population and novel drugs in the market each day, there is a need for an effective PV system in India.

Masking by vaccines in pediatric drug safety signal detection in the EudraVigilance database

PURPOSE

Postmarketing drug safety surveillance relies upon measures of disproportionate reporting in spontaneous reporting systems. It has been hypothesized that products or events reported frequently may "mask" signals.

METHODS

We analyzed the masking effect of vaccines in pediatrics in the EudraVigilance database by conducting disproportionality analysis in the full database (containing vaccine exposures) and in a restricted set (excluding vaccine exposures). We measured performance of the reporting odds ratio (ROR) in both data sets using a pediatric-specific drug reference set and in the absence of a reference set. We assessed masking effects across age groups and conducted a classification tree (CART) analysis.

RESULTS

Removal of vaccines decreased the ROR values both in negative and positive controls. Exceptions were drug-event combinations including outcomes frequent in vaccine reports. When restricted to positive control associations, removal of vaccine-related events resulted in increased ROR values for events commonly reported following vaccination. For events rarely associated with vaccination, ROR values decreased for all age groups, especially infants. Analysis in the absence of a reference set showed decrease in ROR following vaccine removal and CART revealed that change in ROR with vaccine removal depended upon age and proportion of reports including a vaccine.

CONCLUSIONS

Removal of vaccines for signal detection in a pediatric population has an impact on ROR, dependent upon the reporting frequency of the event of interest in combination with vaccines. We recommend stratification by age and removal of vaccine exposures if the investigated adverse drug reactions include those typically reported in association with vaccines for the age strata.

Hepatitis B infection reported with cancer chemotherapy: analyzing the US FDA Adverse Event Reporting System

We conducted data mining using the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database on spontaneously reported adverse events to evaluate the association between anticancer drug therapy and hepatitis B infection. Reports of hepatitis B infection were retrieved from the FAERS database. The reporting odds ratio (ROR) was used to estimate the association between hepatitis B infection and various anticancer agents and drug combinations. We detected statistically significant risk signals of hepatitis B for 33 of 64 anticancer agents by ROR (26 cytotoxicity drugs and seven molecular-targeted drugs). We focused on molecular-targeted drugs and assessed the risk of hepatitis B from specific anticancer drug combinations. The frequency of hepatitis B infection was significantly high for drugs such as rituximab, bortezomib, imatinib, and everolimus. The addition of cyclophosphamide, doxorubicin, and fludarabine to drug combinations additively enhanced the frequency of hepatitis B infection. There were no reports on hepatitis B infection associated with trastuzumab or azacitidine monotherapy. However, trastuzumab-containing regimens (e.g., combinations with docetaxel or paclitaxel) were correlated with the incidence of hepatitis B infection, similar to azacitidine monotherapy. Our findings suggest that the concomitant use of anticancer drugs, such as trastuzumab, taxane, and azacitidine, may contribute to the risk of hepatitis B infection. The unique signals detected from the public database might provide clues to eliminate the threat of HBV in oncology.

A mathematical framework to quantify the masking effect associated with the confidence intervals of measures of disproportionality

BACKGROUND

The lower bound of the 95% confidence interval of measures of disproportionality (Lower95CI) is widely used in signal detection. Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other medicines. The primary objective of our study is to develop and validate a mathematical framework for assessing the masking effect of Lower95CI.

METHODS

We have developed our new algorithm based on the masking ratio (MR) developed for the measures of disproportionality. A MR for the Lower95CI (MRCI) is proposed. A simulation study to validate this algorithm was also conducted.

RESULTS

We have established the existence of a very close mathematical relation between MR and MRCI. For a given drug-event pair, the same product will be responsible for the highest masking effect with the measure of disproportionality and its Lower95CI. The extent of masking is likely to be very similar across the two methods. An important proportion of identical drug-event associations affected by the presence of an important masking effect is revealed by the unmasking exercise, whether the proportional reporting ratio (PRR) or its confidence interval are used.

CONCLUSION

The detection of the masking effect of Lower95CI can be automated. The real benefits of this unmasking in terms of new true-positive signals (rate of true-positive/false-positive) or time gained by the revealing of signals using this method have not been fully assessed. These benefits should be demonstrated in the context of prospective studies.

Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database

PURPOSE

To detect signals of adverse drug events after imipenem treatment using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD).

MATERIALS AND METHODS

We performed data mining using KIDS-KD, which was constructed using spontaneously reported adverse event (AE) reports between December 1988 and June 2014. We detected signals calculated the proportional reporting ratio, reporting odds ratio, and information component of imipenem. We defined a signal as any AE that satisfied all three indices. The signals were compared with drug labels of nine countries.

RESULTS

There were 807582 spontaneous AEs reports in the KIDS-KD. Among those, the number of antibiotics related AEs was 192510; 3382 reports were associated with imipenem. The most common imipenem-associated AE was the drug eruption; 353 times. We calculated the signal by comparing with all other antibiotics and drugs; 58 and 53 signals satisfied the three methods. We compared the drug labelling information of nine countries, including the USA, the UK, Japan, Italy, Switzerland, Germany, France, Canada, and South Korea, and discovered that the following signals were currently not included in drug labels: hypokalemia, cardiac arrest, cardiac failure, Parkinson's syndrome, myocardial infarction, and prostate enlargement. Hypokalemia was an additional signal compared with all other antibiotics, and the other signals were not different compared with all other antibiotics and all other drugs.

CONCLUSION

We detected new signals that were not listed on the drug labels of nine countries. However, further pharmacoepidemiologic research is needed to evaluate the causality of these signals.

Sensitivity of Quantitative Signal Detection in Regards to Pharmacological Neuroenhancement

Pharmacological neuroenhancement (PNE) is a form of abuse and has not yet been addressed by methods of pharmacovigilance. In the present study, we tested if quantitative signal detection may be sensitive in regards to PNE. We evaluated the risk of drug abuse and dependence (DAAD) related to substances that are known to be used for PNE and divided this group into agents with (methylphenidate) and without a known abuse potential outside the field of PNE (atomoxetine, modafinil, acetylcholine esterase inhibitors, and memantine). Reporting odds ratios (RORs) were calculated using a case/non-case approach based on global and country-specific drug safety data from the Uppsala Monitoring Centre (UMC). Both control substances (diazepam and lorazepam) and methylphenidate were statistically associated with DAAD in all datasets (except methylphenidate in Italy). Modafinil was associated with DAAD in the total dataset (ROR, 2.7 (95% confidence interval (CI), 2.2-3.3)), Germany (ROR, 4.6 (95% CI, 1.8-11.5)), and the USA (ROR, 2.0 (95% CI, 1.6-2.5)). Atomoxetine was associated with DAAD in the total dataset (ROR, 1.3 (95% CI, 1.2-1.5)) and in the UK (ROR, 3.3 (95% CI, 1.8-6.1)). Apart from memantine, which was associated with DAAD in Germany (ROR, 1.8 (95% CI, 1.0-3.2)), no other antidementia drug was associated with DAAD. Quantitative signal detection is suitable to detect agents with a risk for DAAD. Its sensitivity regarding PNE is limited, although atomoxetine and modafinil, which do not have a known abuse potential outside PNE, and no antidementia drugs, whose use in PNE is presumably low, were associated with DAAD in our analysis.

A Method for the Minimization of Competition Bias in Signal Detection from Spontaneous Reporting Databases

INTRODUCTION

The two methods for minimizing competition bias in signal of disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio (MR)--have focused on the strength of disproportionality for identifying competitors and have been tested using competitors at the drug level.

OBJECTIVES

The aim of this study was to develop a method that relies on identifying competitors by considering the proportion of reports of adverse events (AEs) that mention the drug class at an adequate level of drug grouping to increase sensitivity (Se) for SDR unmasking, and its comparison with MF and MR.

METHODS

Reports in the French spontaneous reporting database between 2000 and 2005 were selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic anemia, convulsions, and gastrointestinal bleeding; related reports were retrieved using standardized Medical Dictionary for Regulatory Activities (MedDRA(®)) queries. Potential competitors of AEs were identified using the developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three methods were tested according to Anatomical Therapeutic Chemical (ATC) classification levels 2-5. For each AE, SDR detection was performed, first in the complete database, and second after removing reports mentioning competitors; SDRs only detected after the removal were unmasked. All unmasked SDRs were validated using the Summary of Product Characteristics, and constituted the reference dataset used for computing the performance for SDR unmasking (area under the curve [AUC], Se).

RESULTS

Performance of the ComIn was highest when considering competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained with MF and MR.

CONCLUSION

The ComIn could greatly minimize the competition bias in SDR detection. Further study using a larger dataset is needed.

Privacy preserving data anonymization of spontaneous ADE reporting system dataset

Background

To facilitate long-term safety surveillance of marketing drugs, many spontaneously reporting systems (SRSs) of ADR events have been established world-wide. Since the data collected by SRSs contain sensitive personal health information that should be protected to prevent the identification of individuals, it procures the issue of privacy preserving data publishing (PPDP), that is, how to sanitize (anonymize) raw data before publishing. Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS data and none of the anonymization methods is favorable for SRS datasets, due to which contain some characteristics such as rare events, multiple individual records, and multi-valued sensitive attributes.

Methods

We propose a new privacy model called MS(k, θ^*)-bounding for protecting published spontaneous ADE reporting data from privacy attacks. Our model has the flexibility of varying privacy thresholds, i.e., θ^*, for different sensitive values and takes the characteristics of SRS data into consideration. We also propose an anonymization algorithm for sanitizing the raw data to meet the requirements specified through the proposed model. Our algorithm adopts a greedy-based clustering strategy to group the records into clusters, conforming to an innovative anonymization metric aiming to minimize the privacy risk as well as maintain the data utility for ADR detection. Empirical study was conducted using FAERS dataset from 2004Q1 to 2011Q4. We compared our model with four prevailing methods, including k-anonymity, (X, Y)-anonymity, Multi-sensitive l-diversity, and (α, k)-anonymity, evaluated via two measures, Danger Ratio (DR) and Information Loss (IL), and considered three different scenarios of threshold setting for θ^*, including uniform setting, level-wise setting and frequency-based setting. We also conducted experiments to inspect the impact of anonymized data on the strengths of discovered ADR signals.

Results

With all three different threshold settings for sensitive value, our method can successively prevent the disclosure of sensitive values (nearly all observed DRs are zeros) without sacrificing too much of data utility. With non-uniform threshold setting, level-wise or frequency-based, our MS(k, θ^*)-bounding exhibits the best data utility and the least privacy risk among all the models. The experiments conducted on selected ADR signals from MedWatch show that only very small difference on signal strength (PRR or ROR) were observed. The results show that our method can effectively prevent the disclosure of patient sensitive information without sacrificing data utility for ADR signal detection.

Conclusions

We propose a new privacy model for protecting SRS data that possess some characteristics overlooked by contemporary models and an anonymization algorithm to sanitize SRS data in accordance with the proposed model. Empirical evaluation on the real SRS dataset, i.e., FAERS, shows that our method can effectively solve the privacy problem in SRS data without influencing the ADR signal strength.

Analysis of the Interaction between Clopidogrel, Aspirin, and Proton Pump Inhibitors Using the FDA Adverse Event Reporting System Database

Clopidogrel is an antiplatelet agent widely used in combination with aspirin to limit the occurrence of cardiovascular (embolic/thrombotic) events. Consensus guidelines recommend proton pump inhibitors (PPIs) as a gastrointestinal (GI) prophylactic measure for all patients receiving dual antiplatelet therapy with clopidogrel and aspirin. The objective of this study was to analyze the effect of the simultaneous use of clopidogrel, aspirin, and PPIs on hemorrhagic and embolic/thrombotic events using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Reports of hemorrhagic and embolic/thrombotic events between 2004 and 2013 were analyzed with a reporting odds ratio (ROR) algorithm and logistic regression methods. The Medical Dictionary for Regulatory Activities Preferred Terms was used to identify such events. Regarding hemorrhagic events, the adjusted RORs of the concomitant use of aspirin and clopidogrel and those of PPIs prescribed with aspirin and clopidogrel were 4.40 (95% confidence interval [CI], 4.02-4.81) and 3.40 (95% CI, 2.84-4.06), respectively. For embolic/thrombotic events, the adjusted RORs of the concomitant use of aspirin and clopidogrel and those of PPIs prescribed with aspirin and clopidogrel were 2.37 (95% CI, 2.16-2.59) and 2.38 (95% CI, 2.00-2.84), respectively. Among patients included in the FAERS database, the concurrent use of aspirin and clopidogrel with PPIs reduced the adjusted ROR of GI hemorrhagic events. PPIs had little influence on the adjusted ROR of embolic/thrombotic events. These results support the use of PPIs as a preventive measure against GI hemorrhagic events for patients receiving clopidogrel and aspirin.

Factors Affecting the Timing of Signal Detection of Adverse Drug Reactions

We investigated factors affecting the timing of signal detection by comparing variations in reporting time of known and unknown ADRs after initial drug release in the USA. Data on adverse event reactions (AERs) submitted to U.S. FDA was used. Six ADRs associated with 6 drugs (rosuvastatin, aripiprazole, teriparatide, telithromycin, exenatide, varenicline) were investigated: Changes in the proportional reporting ratio, reporting odds ratio, and information component as indexes of signal detection were followed every 3 months after each drugs release, and the time for detection of signals was investigated. The time for the detection of signal to be detected after drug release in the USA was 2-10 months for known ADRs and 19-44 months for unknown ones. The median lag time for known and unknown ADRs was 99.0-122.5 days and 185.5-306.0 days, respectively. When the FDA released advisory information on rare but potentially serious health risks of an unknown ADR, the time lag to report from the onset of ADRs to the FDA was shorter. This study suggested that one factor affecting signal detection time is whether an ADR was known or unknown at release.

Association between haemorrhages and treatment with selective and non-selective serotonergic antidepressants: Possible implications of quantitative signal detection

Inhibition of serotonin uptake in platelets seems to be the crucial mechanism underlying SSRI-associated haemorrhages. This effect is also present in antidepressants featuring non-selective serotonin reuptake inhibition (non-SSRI). Impact of selectivity of serotonin reuptake and/or affinity to the serotonin reuptake transporter on the bleeding risk have not yet been studied sufficiently. We retrieved country- and SSRI-/non-SSRI-specific data from the Uppsala Monitoring Centre and used a case/non-case approach to calculate substance-specific reporting odds ratios (ROR) to evaluate the statistical association of treatment with SSRI/non-SSRI and haemorrhages. Country-specific analysis revealed no clear trends towards an increased risk of bleeding related to particular agents of group SSRI/non-SSRI (sporadically ROR>1 for citalopram, duloxetine, escitalopram, fluvoxamine, paroxetine, sertraline, St. John's wort). There was a clear trend in the total dataset towards a "reduced protective effect" (suggested by ROR<1) on the development of haemorrhages with agents featuring comparatively high affinity to the 5-HTT and/or selective serotonin reuptake inhibition (as with escitalopram, citalopram, duloxetine or venlafaxine) in comparison to agents with lower affinity or non-selective serotonin reuptake inhibition (as with mirtazapine or doxepin). Comparison of group-specific aggregated data (SSRI vs. non-SSRI) revealed significant differences regarding the "protective effect" on the development of haemorrhages between groups SSRI vs. non-SSRI in favour of non-SSRI in nearly all countries as well as in the total dataset. Our findings provide preliminary evidence that agents with increased affinity to the 5-HTT and/or selective serotonin reuptake inhibition may be associated with an increased risk of bleeding.

Arrhythmia associated with buprenorphine and methadone reported to the Food and Drug Administration

AIM

To assess the relative frequency of reporting of adverse events involving ventricular arrhythmia, cardiac arrest, corrected QT interval (QTc) prolongation or torsade de pointes to the US Food and Drug Administration (FDA) between buprenorphine and methadone.

DESIGN

Retrospective pharmacoepidemiological study.

SETTING

Adverse drug events reported spontaneously to the FDA between 1969 and June 2011 originating in 196 countries (71% events from the United States).

CASES

Adverse event cases mentioning methadone (n = 14 915) or buprenorphine (n = 7283) were evaluated against all other adverse event cases (n = 4 796 017).

MEASUREMENTS

The primary outcome was the composite of ventricular arrhythmia or cardiac arrest. The secondary outcome was the composite of QTc prolongation or torsade de pointes. The proportional reporting ratio (PRR) was used to identify disproportionate reporting defined as a PRR > 2, χ(2) error > 4, with ≥ 3 cases.

FINDINGS

There were 132 (1.8%) ventricular arrhythmia/cardiac arrest and 19 (0.3%) QTc prolongation/torsade de pointes cases associated with buprenorphine compared with 1729 (11.6%) ventricular arrhythmia/cardiac arrest and 390 (2.6%) QTc prolongation/torsade de pointes cases involving methadone. PRRs associated with buprenorphine were not significant for ventricular arrhythmia/cardiac arrest (1.10, 95%, confidence interval (0.93-1.31, χ(2)  = 1.2) or QTc prolongation/torsade de pointes (1.03, 95% CI = 0.66-1.62, χ(2)  = 0.01), but were for methadone (7.20, 95% CI = 6.88-7.52, χ(2)  = 8027; 10.7, 95% CI = 9.66-11.8, χ(2)  = 1538, respectively).

CONCLUSION

In spontaneously reported adverse events, methadone is associated with disproportionate reporting of cardiac arrhythmias, whereas buprenorphine is not. Although these findings probably reflect clinically relevant differences, a causal connection cannot be presumed and disproportionality analysis cannot quantify absolute risk per treatment episode. Population-based studies to definitively quantify differential incidence rates are warranted.

Use of logistic regression to combine two causality criteria for signal detection in vaccine spontaneous report data

Purpose

We evaluated the use of logistic regression to model the probabilities of spontaneously reported vaccine–event pairs being adverse reactions following immunization (ARFI), using disproportionality and unexpectedness of time-to-onset (TTO) distributions as predictive variables and the presence of events in the global product information as a dependent variable.

Methods

We used spontaneous reports of adverse events from eight vaccines and their labels as proxies for ARFIs. Three logistic regressions were built to predict ARFIs based on different combinations of the proportional reporting ratio (PRR; disproportionality measure) and two Kolmogorov–Smirnov (KS) tests (‘between vaccines’ and the ‘between events’) of TTO distribution: model 1, using the PRR estimate and its 95 % lower confidence interval (CI) limit; model 2, using the p values of the two KS tests; and model 3, using the PRR (point estimate and lower CI limit) and both KS tests. The performance of the regressions (model fit statistics, calibration, and discrimination) was measured on 100 bootstrap samples.

Results

Model 3, using two quantified causality criteria, provided the best performance for all measures. The p value of the ‘between vaccines’ KS test was the most significant predictive factor. Model 1 had the worst performance.

Conclusions

Logistic regression allows estimation of the probability of a vaccine–event pair being an ARFI using two causality criteria at the population level assessed in spontaneous report data: the strength of association (disproportionality measure) and temporality (TTO distribution tests). Logistic regression combines and weights these causality criteria based on their respective ability to predict known safety issues.

Hepatotoxicity associated with agomelatine and other antidepressants: Disproportionality analysis using pooled pharmacovigilance data from the Uppsala Monitoring Centre

Since its marketing approval, the attention to the hepatic side-effect profile of the antidepressant agomelatine (AGM) has gradually increased. Several cases of severe hepatotoxic adverse drug reactions (ADR) have been reported and the European Medicines Agency has released a safety warning regarding AGM-associated hepatotoxicity. However, there are insufficient data for an adequate safety assessment of AGM-related hepatotoxicity. Therefore, we performed a quantitative signal detection analysis using pharmacovigilance data from the Uppsala Monitoring Centre from the WHO that records ADR data from worldwide sources; we calculated reporting odds ratios (ROR) as measures for disproportionality within a case/non-case approach for AGM and several other antidepressants. AGM was statistically associated with an increased risk of hepatotoxicity (ROR 6.4 [95%CI 5.7-7.2]) as well as both positive controls: amineptine (ROR 38.4 [95%CI 33.8-43.6]) and nefazodone (ROR 3.2 [95%CI 3.0-3.5]). Following amineptine, AGM was associated with the second highest ROR, followed by tianeptine (ROR 4.4 [95%CI 3.6-5.3]), mianserin (ROR 3.6 [95%CI 3.3-3.9]), and nefazodone. These results support the hypothesis that AGM is associated with relevant hepatotoxicity. However, the used data and applied method do not allow a quantitative evaluation of hepatotoxicity or assessment of substance-specific differences regarding the extent of hepatotoxicity.

Reducing the noise in signal detection of adverse drug reactions by standardizing the background: a pilot study on analyses of proportional reporting ratios-by-therapeutic area

PURPOSE

Disproportionality screening analysis is acknowledged as a tool for performing signal detection in databases of adverse drug reactions (ADRs), e.g., in the European Union (EU) Drug Authority setting. The purpose of this study was to explore the possibility of decreasing false-positive signals of disproportionate reporting (SDR) by calculating the proportional reporting ratio (PRR)-by-therapeutic area (TA), while still maintaining the ability to detect relevant SDRs.

METHODS

In the EudraVigilance (EV) Database, output from PRR calculated with a restricted TA comparator background was compared in detail to output from conventional authority-setting PRR calculations for four drugs: bicalutamide, abiraterone, metformin, and vildagliptin, within the TAs of prostate gland disease and type 2 diabetes mellitus.

RESULTS

ADR reports per investigated drug ranged from 2,400 to 50,000. The PRR-TA's ability to detect true-positive SDRs (as acknowledged in approved labeling) was increased compared to the conventional PRR, and performed 8-31 % better than a recently proposed stricter EU-SDR definition. The PRR-TA removed false SDRs confounded by disease or disease spill-over by up to 63 %, while retaining/increasing the number of unclassified SDRs relevant for manual validation, and thereby improving the ratio between confounded SDRs (i.e., noise) and unclassified SDRs for all investigated drugs (possible signals).

CONCLUSIONS

The performance of the PRR was improved by background restriction with the PRR-TA method; the number of false-positive SDRs decreased, and the ability to detect true-positive SDRs increased, improving the signal-to-noise ratio. Further development and validation of the method is needed within other TAs and databases, and for disproportionality analysis methods.

A potential event-competition bias in safety signal detection: results from a spontaneous reporting research database in France

BACKGROUND

In spontaneous reporting databases, reports of well-established drug-event associations may mask alerts that arise from other drugs (drug competition bias). However, a symmetrical event-competition bias has not yet been explored whereby known events may mask an association with new events for a given drug or drug class.

OBJECTIVE

The objective of this study was to explore the effects of event-competition bias on safety signals generated from spontaneous reporting databases.

METHODS

The drug classes tested included statins, oral anticoagulants, antipsychotics and HIV antiretrovirals. For each, a type A reaction was selected, and its potential competitive effect on the generation of other safety signals for the drug was explored. These were rhabdomyolysis/myopathy for statins, haemorrhage for oral anticoagulants, extrapyramidal syndrome for antipsychotics and lipodystrophy for HIV antiretrovirals. Signals of disproportionate reporting (SDRs) were detected using the case/non-case approach in the French research spontaneous reporting database (which contains reports from 1 January 1986 to 31 December 2001), before and after removing all reports concerning these competitor events. SDRs were considered as potential signals if not reported in the literature before 1 January 2002 but confirmed since.

RESULTS

The whole database included 207,236 reports, 4,355 of which included statins as one of the suspected drugs. The removal of reports of rhabdomyolysis/myopathy concerned 8,425 reports among which 867 involved statins. After this removal, 11 new SDRs appeared for statins that had not been detected initially. Similarly, 15 SDRs were unmasked for oral anticoagulants, six for antipsychotics and nine for HIV antiretrovirals. After literature-based assessment, five of the 41 unmasked SDRs appeared related to potential safety signals confirmed after 2002.

CONCLUSION

This study demonstrated that a masking phenomenon resulting from an event-competition effect could occur when performing signal detection using disproportionality analyses of spontaneous reporting databases. This should be taken into account when routine signal detection is performed.

Commonality of drug-associated adverse events detected by 4 commonly used data mining algorithms

OBJECTIVES

Data mining algorithms have been developed for the quantitative detection of drug-associated adverse events (signals) from a large database on spontaneously reported adverse events. In the present study, the commonality of signals detected by 4 commonly used data mining algorithms was examined.

METHODS

A total of 2,231,029 reports were retrieved from the public release of the US Food and Drug Administration Adverse Event Reporting System database between 2004 and 2009. The deletion of duplicated submissions and revision of arbitrary drug names resulted in a reduction in the number of reports to 1,644,220. Associations with adverse events were analyzed for 16 unrelated drugs, using the proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM).

RESULTS

All EBGM-based signals were included in the PRR-based signals as well as IC- or ROR-based ones, and PRR- and IC-based signals were included in ROR-based ones. The PRR scores of PRR-based signals were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the EBGM method, as were the IC scores of IC-based signals for all drugs; however, no such effect was observed in the ROR scores of ROR-based signals.

CONCLUSIONS

The EBGM method was the most conservative among the 4 methods examined, which suggested its better suitability for pharmacoepidemiological studies. Further examinations should be performed on the reproducibility of clinical observations, especially for EBGM-based signals.

Statistical and graphical approaches for disproportionality analysis of spontaneously-reported adverse events in pharmacovigilance

AIM

Combine disproportionality analysis with dynamically interactive graphics to understand spontaneously-reported adverse events in pharmacovigilance.

METHODS

Four statistical methods, including Reporting Odds Ratio, Proportional Reporting Ratio, Multi-Item Gamma Poisson Shrinker and Bayesian Confidence Propagation Neural Network that are used for computing disproportionality are described. Tree maps and other graphical techniques are used to display the disproportionality results.

RESULTS

Spontaneously-reported adverse events in pharmacovigilance are collected from physicians, patients, or the medical literature by regulatory agencies, pharmaceutical companies and device manufacturers to monitor the safety of a product once it reaches the market. In order to identify potential safety-signals, disproportionality analysis methods compare the rate at which a particular event of interest co-occurs with a given drug with the rate this event occurs without the drug in the event database. Tree maps are employed to interactively display the adverse events for particular drugs and compare the adverse events among the drugs.

CONCLUSION

Interactive graphical displays of disproportionality allow the analyst to quickly identify safety signals and perform additional follow-up analyses. Combining statistical methods with dynamically interactive graphics affords insights into the data inaccessible by traditional analysis methods.

A conceptual approach to the masking effect of measures of disproportionality

BACKGROUND

Masking is a statistical issue by which true signals of disproportionate reporting are hidden by the presence of other products in the database. Masking is currently not perfectly understood. There is no algorithm to identify the potential masking drugs to remove them for subsequent analyses of disproportionality.

OBJECTIVE

The primary objective of our study is to develop a mathematical framework for assessing the extent and impact of the masking effect of measures of disproportionality.

METHOD

We have developed a masking ratio that quantifies the masking effect of a given product. We have conducted a simulation study to validate our algorithm.

RESULTS

The masking ratio is a measure of the strength of the masking effect whether the analysis is performed at the report or event level, and the manner in which reports are allocated to cells in the contingency table significantly impact the masking mechanisms. The reports containing both the product of interest and the masking product need to be handled appropriately. The proposed algorithm can use simplified masking provided that underlying assumptions (in particular the size of the database) are verified. For any event, the strongest masking effect is associated with the drug with the highest number of records (reports excluding the product of interest).

CONCLUSION

Our study provides significant insights with practical implications for real-world pharmacovigilance that are supported by both real and simulated data. The public health impact of masking is still unknown.

Assessing the extent and impact of the masking effect of disproportionality analyses on two spontaneous reporting systems databases

BACKGROUND

Masking is a statistical issue by which signals are hidden by the presence of other medicines in the database. In the absence algorithm, the impact of the masking effect has not been fully investigated.

OBJECTIVE

Our study is aimed at assessing the extent and the impact of the masking effect on two large spontaneous reporting databases.

STUDY DESIGN

Cross sectional study using a set of terms of importance for public health in two spontaneous reporting databases.

SETTING

The analyses were performed on EudraVigilance (EV) and the Pfizer spontaneous reporting database (PfDB).

MAIN OUTCOME MEASURE

Using the masking ratio, we have identified and removed the products inducing the highest masking effect.

RESULTS

Studying a total of almost 50 000 drug-event combinations masking had an impact on approximately 60% of drug-event combinations were masked by another product with a masking ratio >1 in EV and 84% in PfDB. The prevalence of important masking was quite rare (0.003% of the DECs) and mainly affected events rarely reported in EV. The products involved in the highest masking effects are products known to induce the reaction. The removal of the masking effect of the highest masking product has revealed 974 signals of disproportionate reporting in EV including true signals. The study shows that the original ranking provided by the quantitative methods included in our study is marginally affected by the removal of the masking product.

CONCLUSION

Our study suggests that significant masking is rare in large spontaneous databases and mostly affects events rarely reported in EV.

Mining clinical text for signals of adverse drug-drug interactions

BACKGROUND AND OBJECTIVE

Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs.

METHODS

We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.

RESULTS

Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus.

CONCLUSIONS

It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Cholinesterase inhibitors and Pisa syndrome: a pharmacovigilance study

STUDY OBJECTIVES

Case reports suggest a relationship between cholinesterase inhibitors (ChEIs) and Pisa syndrome (PS), also known as pleurothotonus, a form of dystonia, but this relationship has not been systematically examined. Our objective was to estimate the adjusted reporting ratios of PS with donepezil, rivastigmine, and galantamine in the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.

DESIGN

Retrospective analysis of adverse event reports in the FAERS database.

PATIENTS

Patients with drug-related adverse events in the FAERS database.

MEASUREMENTS AND MAIN RESULTS

The Gamma Poisson Shrinker algorithm was used to estimate the empirical Bayes geometric mean (EBGM) along with the lower and upper 90% confidence interval (CI) limits (EB05 and EB95, respectively), as measures of the adjusted reporting ratio of PS in patients taking ChEIs. EB05 > 2.0 was used as the cutoff for significance for the signals. The EBGM (EB05) was 37.9 (30) for all ChEIs, 25.6 (17.6) for donepezil, 76.4 (50.3) for galantamine, and 33.7 (21.2) for rivastigmine. All adverse event signals were strongly significant based on the a priori set EB05 cutoff. The female:male ratio in the reported cases was 2:1. No significant signals were found between ChEIs and other dystonias. About half of the ChEI users were also taking concomitant antipsychotics.

CONCLUSION

Although FAERS data cannot establish causality due to reporting biases, our findings support a potential dopaminergic-cholinergic imbalance as an underlying mechanism for PS and may help increase clinician awareness, early identification, and treatment of ChEI-related dystonias.

Signal detection on spontaneous reports of adverse events following immunisation: a comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm

PURPOSE

Disproportionality methods measure how unexpected the observed number of adverse events is. Time-to-onset (TTO) methods measure how unexpected the TTO distribution of a vaccine-event pair is compared with what is expected from other vaccines and events. Our purpose is to compare the performance associated with each method.

METHODS

For the disproportionality algorithms, we defined 336 combinations of stratification factors (sex, age, region and year) and threshold values of the multi-item gamma Poisson shrinker (MGPS). For the TTO algorithms, we defined 18 combinations of significance level and time windows. We used spontaneous reports of adverse events recorded for eight vaccines. The vaccine product labels were used as proxies for true safety signals. Algorithms were ranked according to their positive predictive value (PPV) for each vaccine separately; amedian rank was attributed to each algorithm across vaccines.

RESULTS

The algorithm with the highest median rank was based on TTO with a significance level of 0.01 and a time window of 60 days after immunisation. It had an overall PPV 2.5 times higher than for the highest-ranked MGPS algorithm, 16(th) rank overall, which was fully stratified and had a threshold value of 0.8. A TTO algorithm with roughly the same sensitivity as the highest-ranked MGPS had better specificity but longer time-to-detection.

CONCLUSIONS

Within the scope of this study, the majority of the TTO algorithms presented a higher PPV than for any MGPS algorithm. Considering the complementarity of TTO and disproportionality methods, a signal detection strategy combining them merits further investigation. © 2013 GlaxoSmithKline. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.