Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database

Pharmacovigilance Strategies to Address Resistance to Antibiotics and Inappropriate Use-A Narrative Review

The spread of antimicrobial resistance (AMR) is a global challenge. Close and continuous surveillance for quick detection of AMR can be difficult, especially in remote places. This narrative review focuses on the contributions of pharmacovigilance (PV) as an auxiliary tool for identifying and monitoring the ineffectiveness, resistance, and inappropriate use of antibiotics (ABs). The terms "drug ineffective", "therapeutic failure", "drug resistance", "pathogen resistance", and "multidrug resistance" were found in PV databases and dictionaries, denoting ineffectiveness. These terms cover a range of problems that should be better investigated because they are useful in warning about possible causes of AMR. "Medication errors", especially those related to dose and indication, and "Off-label use" are highlighted in the literature, suggesting inappropriate use of ABs. Hence, the included studies show that the terms of interest related to AMR and use are not only present but frequent in PV surveillance programs. This review illustrates the feasibility of using PV as a complementary tool for antimicrobial stewardship activities, especially in scenarios where other resources are scarce.

Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data

As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects information. We propose a method for utilizing SNS data to plot the known side effects of geriatric drugs in a dosing map. We developed a lexicon of drug terms associated with side effects and mapped patterns from social media data. We confirmed that well-known side effects may be obtained by utilizing SNS data. Based on these results, we propose a pharmacovigilance pipeline that can be extended to unknown side effects. We propose the standard analysis pipeline Drug_SNSMiner for monitoring side effects using SNS data and evaluated it as a drug prescription platform for the elderly. We confirmed that side effects may be monitored from the consumer's perspective based on SNS data using only drug information. SNS data were deemed good sources of information to determine ADRs and obtain other complementary data. We established that these learning data are invaluable for AI requiring the acquisition of ADR posts on efficacious drugs.

Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system

There has been a growing attention on using machine learning (ML) in pharmacovigilance. This study aimed to investigate the utility of supervised ML algorithms on timely detection of safety signals in the Korea Adverse Event Reporting System (KAERS), using infliximab as a case drug, between 2009 and 2018. Input data set for ML training was constructed based on the drug label information and spontaneous reports in the KAERS. Gold standard dataset containing known AEs was randomly divided into the training and test sets. Two supervised ML algorithms (gradient boosting machine [GBM], random forest [RF]) were fitted with hyperparameters tuned on the training set by using a fivefold validation. Then, we stratified the KAERS data by calendar year to create 10 cumulative yearly datasets, in which ML algorithms were applied to detect five pre-specified AEs of infliximab identified during post-marketing surveillance. Four AEs were detected by both GBM and RF in the first year they appeared in the KAERS and earlier than they were updated in the drug label of infliximab. We further applied our models to data retrieved from the US Food and Drug Administration Adverse Event Reporting System repository and found that they outperformed existing disproportionality methods. Both GBM and RF demonstrated reliable performance in detecting early safety signals and showed promise for applying such approaches to pharmacovigilance.

Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm

Pharmacovigilance involves monitoring of drugs and their adverse drug reactions (ADRs) and is essential for their safety post-marketing. Because of the different types and structures of medical databases, several previous surveillance studies have analyzed only one database. In the present study, we extracted potential drug-ADR pairs from electronic health record (EHR) data using the MetaNurse algorithm and analyzed them using the Korean Adverse Event Reporting System (KAERS) database for systematic validation. The Medical Dictionary for Regulatory Activities (MedDRA) and World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) were mapped for signal detection. We used the Side Effect Resource (SIDER) database to select 2663 drug-ADR pairs to investigate unknown drug-induced ADRs. The reporting odds ratio (ROR) value was calculated for the drug-exposed and non-exposed groups of drug-ADR pairs, and 19 potential pairs showed significant signals. Appropriate terminology systems and criteria are needed to handle diverse medical databases.

Data mining for detecting signals of adverse drug reaction of doxycycline using the Korea adverse event reporting system database

BACKGROUND

Doxycycline is one of the most prescribed antibiotics by dermatologists. However, the concern regarding adverse events of doxycyline has been rising.

OBJECTIVE

To detect the adverse events of doxycycline using the Korea Adverse Events Reporting System (KAERS) database from January 2014 to December 2018 through a data mining method.

METHODS

A signal was defined as one satisfying all three indices; a proportional reporting ratio, a reporting odds ratio, and an information component. We further checked whether the detected signals exist in drug labels in Korea and five developed countries, the United States, the United Kingdom, Germany, Canada, and Japan.

RESULTS

A total of 3,365,186 adverse event-drug pairs were reported and of which 3,075 were associated with doxycycline. Among the thirty-seven signals, nineteen (malaise, ileus, confusion, malignant neoplasm, ectopic pregnancy, ovarian hyperstimulation, vaginal hemorrhage, bone necrosis, acne, rosacea, seborrheic dermatitis, folliculitis, skin ulceration, crusting, dry skin, paronychia, mottled skin, application site reaction, and application site edema) were not included on any of the drug labels of the six countries.

CONCLUSION

We identified nineteen new doxycycline signals that did not appear on drug labels in six countries. Further studies are warranted to evaluate the causality of the adverse events with doxycycline.

Machine Learning for Detection of Safety Signals From Spontaneous Reporting System Data: Example of Nivolumab and Docetaxel

Introduction: Various methods have been implemented to detect adverse drug reaction (ADR) signals. However, the applicability of machine learning methods has not yet been fully evaluated.

Objective: To evaluate the feasibility of machine learning algorithms in detecting ADR signals of nivolumab and docetaxel, new and old anticancer agents.

Methods: We conducted a safety surveillance study of nivolumab and docetaxel using the Korea national spontaneous reporting database from 2009 to 2018. We constructed a novel input dataset for each study drug comprised of known ADRs that were listed in the drug labels and unknown ADRs. Given the known ADRs, we trained machine learning algorithms and evaluated predictive performance in generating safety signals of machine learning algorithms (gradient boosting machine [GBM] and random forest [RF]) compared with traditional disproportionality analysis methods (reporting odds ratio [ROR] and information component [IC]) by using the area under the curve (AUC). Each method then was implemented to detect new safety signals from the unknown ADR datasets.

Results: Of all methods implemented, GBM achieved the best average predictive performance (AUC: 0.97 and 0.93 for nivolumab and docetaxel). The AUC achieved by each method was 0.95 and 0.92 (RF), 0.55 and 0.51 (ROR), and 0.49 and 0.48 (IC) for respective drug. GBM detected additional 24 and nine signals for nivolumab and 82 and 76 for docetaxel compared to ROR and IC, respectively, from the unknown ADR datasets.

Conclusion: Machine learning algorithm based on GBM performed better and detected more new ADR signals than traditional disproportionality analysis methods.

A retrospective cross-sectional study to determine chirality status of registered medicines in Tanzania

Medicines with a stereogenic center (asymmetric carbon) are mainly present as racemates with a mixture of equal amounts of enantiomers. One enantiomer may be active while the other inactive, alternatively one may produce side-effects and even toxicity. However, there is lack of information on the chirality status (either racemates, single active enantiomer or achiral) of medicines circulated on the market particularly in African countries. We established the chirality status of registered medicines in Tanzania by conducting a retrospective cross-sectional study. Registration data for the past 15 years from 2003 to 2018 were extracted from TMDA-IMIS database to Microsoft excel for review and analysis. A total of 3,573 human medicines had valid registration. Out of which 2,150 (60%) were chiral and 1,423 (40%) achiral. Out of the chiral medicines, 1,591 (74%) and 559 (26%) were racemates and single active enantiomers, respectively. The proportion of racemates within chiral medicines was considerably higher than single enantiomer medicines. The use of racemates may cause harm to the public and may contribute to antimicrobial resistance due to potential existence of inactive and toxic enantiomers. In order to protect public health, regulatory bodies need to strengthen control of chiral medicines by conducting analysis of enantiomeric impurity.

Signals of Adverse Drug Reactions of Paliperidone Compared to Other Atypical Antipsychotics Using the Korean Adverse Event Reporting System Database

BACKGROUND AND OBJECTIVES

Schizophrenia is a severe public health problem and one of the top ten causes of disability, affecting about 1.1% of the world's population. Paliperidone is a new atypical antipsychotic used to treat schizophrenia. Several case reports about unexpected adverse drug reactions of paliperidone have been consistently reported around the world. The purpose of this study was to detect signals of adverse events (AEs) after paliperidone treatment using the Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System database (KIDS-KD).

METHODS

We applied data-mining techniques based on a disproportionality analysis to KIDS-KD consisting of spontaneously reported AE reports related to atypical antipsychotics between January 2009 and December 2018. We calculated three data-mining indices of paliperidone compared to all other atypical antipsychotics. We defined signals that satisfied all three criteria of the indices. We checked if the signals identified were included in the drug labels for South Korea, the USA, the UK, Japan, Germany, and France.

RESULTS

The total number of suspected AE reports related to all atypical antipsychotics in the KIDS-KD from January 2009 to December 2018 was 43,970. Among those, the number of AE reports related to paliperidone was 9453. Overall, 13 signals such as seborrhea, hallucination, obesity, gingivitis, and intervertebral disorder were classified into newly detected meaningful signals.

CONCLUSION

We detected new AE signals of paliperidone that were not listed on the drug labels of six countries, and many that were related to psychotic symptoms, metabolic problems, and endocrine disorders.

Adverse Skin Reactions with Antiepileptic Drugs Using Korea Adverse Event Reporting System Database, 2008-2017

BACKGROUND

Severe and life-threatening drug eruptions include drug reaction with eosinophilia and systemic symptoms (DRESS), Stevens-Johnson syndrome (SJS), and toxic epidermal necrolysis (TEN). One class of medications that has been highly associated with such drug eruptions is antiepileptic drugs (AEDs). We attempt to investigate drug eruptions associated with AEDs as a class, as well as with individual AEDs, in Korea.

METHODS

We used the Korea Institute of Drug Safety and Risk Management - Korea Adverse Event Reporting System (KIDS-KAERS) database, a nationwide database of adverse events reports, between January 2008 and December 2017 to investigate the reporting count of all drug eruptions and calculated the ratio of DRESS/SJS/TEN reports for each AED.

RESULTS

Among a total of 2,942 reports, most were of rash/urticaria (2,702, 91.8%), followed by those of DRESS (109, 3.7%), SJS (106, 3.6%), and TEN (25, 0.85%). The common causative AEDs were lamotrigine (699, 23.8%), valproic acid (677, 23%), carbamazepine (512, 17.4%), oxcarbazepine (320, 10.9%), levetiracetam (181, 6.2%), and phenytoin (158, 5.4%). In limited to severe drug eruptions (DRESS, SJS, and TEN; total 241 reports), the causative AEDs were carbamazepine (117, 48.8%), lamotrigine (57, 23.8%), valproic acid (20, 8.3%), phenytoin (15, 6.3%), and oxcarbazepine (10, 4.2%). When comparing aromatic AED with non-aromatic AED, aromatic AEDs were more likely to be associated with severe drug eruption (aromatic AEDs: 204/1,793 versus non-aromatic AEDs: 37/1,149; OR, 3.86; 95% CI, 2.7-5.5). Death was reported in 7 cases; DRESS was the most commonly reported adverse event (n = 5), and lamotrigine was the most common causative AED (n = 5).

CONCLUSION

Although most cutaneous drug eruptions in this study were rash or urticaria, approximately 8% of reports were of severe or life-threatening adverse drug reactions, such as SJS, TEN, or DRESS. When hypersensitivity skin reactions occurred, aromatic AEDs were associated with 4 fold the risk of SJS/TEN/DRESS compared with non-aromatic AEDs. Our findings further emphasize that high risk AEDs should be prescribed under careful monitoring, and early detection and prompt interventions are needed to prevent severe complications.

Comparison of bleeding risks among non-vitamin K antagonist oral anticoagulants using the Korea adverse event reporting system database

Background:

In order to ensure safer use of non-vitamin K antagonist oral anticoagulants (NOACs), continuously detecting unexpected adverse drug reactions (ADRs) after market approval is necessary.

Methods:

We performed disproportionality analysis to evaluate association between ADRs and NOACs including apixaban, dabigatran, and rivaroxaban using data from the Korea Institute of Drug Safety and Risk Management–Korea Adverse Event Reporting System database (KIDS-KD) between 2012 and 2016. There was no significant signal other than bleeding when considering quantity, signal strength, seriousness, and causality. In order to evaluate the NOAC reports about bleeding, we selected 62 WHO-ART diagnostic codes associated with bleeding. Among the 26 codes that referred to major bleeding, 18 codes referred to gastrointestinal bleeding and 8 were referred to intracranial bleeding. We evaluated the significance of the signals using reporting odds ratios (RORs) adjusted for age and sex.

Results:

Treatments with apixaban, dabigatran, and rivaroxaban were associated with 1989, 1668, and 2960 adverse events, respectively. Any type of bleeding with apixaban, dabigatran, rivaroxaban, and warfarin was reported in 174 (8.8%), 209 (12.5%), 523 (17.8%), and 620 (9.5%) events, respectively. For any bleeding, adjusted RORs of apixaban, dabigatran, and rivaroxaban were 0.99 [95% confidence interval (CI): 0.83–1.17], 1.47 (95% CI: 1.25–1.75), and 2.48 (95% CI: 2.16–2.84), respectively. With respect to major bleeding, the adjusted RORs of apixaban, dabigatran, and rivaroxaban were 1.08 (95% CI: 0.82–1.41), 1.46 (95% CI: 1.10–1.90), and 1.82 (95% CI: 1.43–2.32), respectively.

Conclusion:

Rivaroxaban might have stronger association with bleeding than apixaban and dabigatran.

Implications from China patient safety incidents reporting system

Objective

We aimed to explain the operational mechanism of China National Patient Safety Incidents Reporting System, analyze patterns and trends of incidents reporting, and discuss the implication of the incidents reporting to improve hospital patient safety.

Design

A nationwide, registry-based, observational study design.

Data source

The database of China National Patient Safety Incidents Reporting System.

Outcome measures

Outcome measures of this study included the temporal, regional, and hospital distribution of the reports, as well as the incident type, location, parties, and possible reasons for frequently occurring incidents.

Results

During 2012–2017, 36,498 patient safety incidents were reported. By analyzing the time trends, we found that there was a significant upward trend on incidents reporting in China. The most common type of incidents was drug-related incidents, followed by nursing-related incidents and surgery-related incidents. The three most frequent locations of incident occurrence were Patient’s Room (65.4%), Ambulatory Care Unit (8.4%), and Intensive Care Unit (7.4%). The majority of the incidents involved nurses (40.7%), followed by physicians (29.5%) and medical technologist (13.6%). About 44.4% of the incidents were attributed to the junior staff (work experience ≤5 years). In addition, incidents triggered by the senior staff (work experience >5 years) were more often associated with severe patient harm.

Conclusion

To strengthen the incidents reporting system and generate useful evidence through learning from incidents reporting will be important to China’s success in improving the nation’s patient safety status.

Differential completeness of spontaneous adverse event reports among hospitals/clinics, pharmacies, consumers, and pharmaceutical companies in South Korea

The differential pattern and characteristics of completeness in adverse event (AE) reports generated by hospitals/clinics, pharmacies, consumer and pharmaceutical companies remain unknown. Thus, we identified the characteristics of complete AE reports, compared with those of incomplete AE reports, using a completeness score. We used Korea Institute of Drug Safety and Risk Management-Korea Adverse Event Reporting System Database (KIDS-KD) between January 1, 2016 and December 31, 2016. The completeness score was determined out of a total of 100 points, based on the presence of information on temporal relationships, age and sex of patients, AE progress, name of reported medication, reporting group by profession, causality assessment, and informational text. AE reports were organized into four groups based on affiliation: hospitals/clinics, pharmacies, consumers, and pharmaceutical companies. Affiliations that had median completeness scores greater than 80 points were classified as ‘well-documented’ and these reports were further analyzed by logistic regression to estimate the adjusted odds ratios and 95% confidence intervals. We examined 228,848 individual reports and 735,745 drug-AE combinations. The median values of the completeness scores were the highest for hospitals/clinics (95 points), followed by those for consumers (85), pharmacies (75), and manufacturers (72). Reports with causality assessment of ‘certain’, ‘probable’, or ‘possible’ were more likely to be ‘well-documented’ than reports that had causality assessments of ‘unlikely’. Serious reports of AEs were positively associated with ‘well-documented’ reports and negatively associated with hospitals/clinics.

Data-mining for detecting signals of adverse drug reactions of fluoxetine using the Korea Adverse Event Reporting System (KAERS) database

Selective serotonin reuptake inhibitors (SSRIs) have become one of the most broadly used medications in psychiatry. Fluoxetine is the first representative antidepressant SSRI drug approved by the Food and Drug Administration (FDA) in 1987. Safety information on fluoxetine use alone was less reported than its combined use with other drugs. There were no published papers on adverse drug reactions (ADRs) of fluoxetine analyzing spontaneous adverse events reports. We detected signals of the adverse drug reactions of fluoxetine by data mining using the Korea Adverse Events Reporting System (KAERS) database. We defined signals in this study by the reporting odds ratios (ROR), proportional reporting ratios (PRR), and information components (IC) indices. The KAERS database included 860,224 AE reports, among which 866 reports contained fluoxetine. We compared the labels of fluoxetine among the United States, UK, Germany, France, China, and Korea. Some of the signals, including emotional lability, myositis, spinal stenosis, paradoxical drug reaction, drug dependence, extrapyramidal disorder, adrenal insufficiency, and intracranial hemorrhage, were not labeled in the six countries. In conclusion, we identified new signals that were not known at the time of market approval. However, certain factors should be required for signal evaluation, such as clinical significance, preventability, and causality of the detected signals.