Drug-Drug Interaction Signal Detection from Drug Safety Reports

2018 ◽  
Author(s):  
Brian Zylich ◽  
Brian McCarthy ◽  
Andrew Schade ◽  
Huy Tran ◽  
Xiao Qin ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Signal Detection ◽  
Drug Safety ◽  
Drug Drug Interaction

scholarly journals Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

2020 ◽  
Vol 14 (1) ◽  
pp. 4
Author(s):  
Yoshihiro Noguchi ◽  
Keisuke Aoyama ◽  
Satoaki Kubo ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Signal Detection ◽  
Proportional Reporting Ratio ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Signal Score ◽  
Drug Drug Interaction

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

scholarly journals A New Drug–Drug Interaction Between Hydroxychloroquine and Metformin? A Signal Detection Study

Drug Safety ◽  
2020 ◽  
Vol 43 (7) ◽  
pp. 657-660 ◽  
Author(s):  
Jean-Louis Montastruc ◽  
Pierre-Louis Toutain
Keyword(s):  
Drug Interaction ◽  
Signal Detection ◽  
New Drug ◽  
Drug Drug Interaction

scholarly journals Nurses' Awareness and Perception of Drug-Drug and Drug Food Interactions

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Hend A. E. Elshenawi ◽  
Yasmin F. M. Abed Elazeem
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Drug Safety ◽  
Structured Interview ◽  
University Hospital ◽  
Surgical Department ◽  
Low Level ◽  
Drug Drug Interaction ◽  
Plan Management ◽  
Assessment Questionnaire

Context: The issue of drug interactions is a global concern. Studies reported a high prevalence of drug interactions worldwide. The drug-drug interaction (DDIs) and drug-food interactions (DFIs) are often predictable and preventable. Nurses play essential roles in inpatient drug safety. Aim: This study aimed to assess nurses' awareness and perception of drug-drug and drug-food interactions. Methods: A cross-sectional descriptive design was used to achieve the aim of this study on a convenient sample of 150 nurses working at emergency departments(medical, surgical), cardiac care unit, renal department, general surgery department, and the chest and heart surgical department at the Main University Hospital of Alexandria, Egypt. Four study tools used. They were a structured interview questionnaire designed to assess the nurses’ sociodemographic characteristic, nurses’ working experiences to drug-drug and drug-food interactions, and nurses’ history in encountering drug-drug and drug-food interactions; nurses’ awareness of drug/drug interaction assessment questionnaire, nurses’ awareness of drug/food interaction assessment questionnaire, and drug safety nurses’ perception assessment questionnaire. Results: The findings of the current study reveal that 64% of the studied nurses did not receive training on DDIs or DFIs other than that in their basic education. 56% of the nurses came across patients who experienced either DDIs or DFIs. Regarding awareness, around half of them did not make aware of the drug-drug interactions of the studied drug pairs that are frequently used in the clinical practice. Concerning DFIs, 74% of the studied nurses had a low level of total awareness. Regarding nurses’ perception to drug safety, 49.3% of the studied nurses agreed that the risk of drug-drug interaction is high, 55.3% agreed with the importance for prescribers to learn about DDIs and DFIs, and 53.3% of them agreed with the information regarding the DDIs and DFIs useful to the nurse in plan management. The current study revealed a statistically significant association between training received and nurses’ awareness regarding DDIs and DFIs. Conclusion: The study concluded a low level of awareness among the studied nurses regarding DDIs and DFIs with an average perception of the risk of DDIs/DFIs, and the importance of related information in plan management. The study recommended different strategies to be applied to assist prescribers and nurses in identifying potential DDIs, providing educational interventions, facilitating access to DDI information sources, applying computerized alerting systems, and delivering performance feedback among the most commonly recommended strategies.

scholarly journals A Feasibility Study of Drug–Drug Interaction Signal Detection in Regular Pharmacovigilance

Drug Safety ◽  
2020 ◽  
Vol 43 (8) ◽  
pp. 775-785
Author(s):  
Sara Hult ◽  
Daniele Sartori ◽  
Tomas Bergvall ◽  
Sara Hedfors Vidlin ◽  
Birgitta Grundmark ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Signal Detection ◽  
Feasibility Study ◽  
Drug Drug Interaction

scholarly journals Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Pharmaceutics ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 762 ◽  
Author(s):  
Yoshihiro Noguchi ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Signal Detection ◽  
Predictive Value ◽  
Drug Event ◽  
Detection Accuracy ◽  
Analysis Model ◽  
Subset Analysis ◽  
Pharmacovigilance Database ◽  
Drug Drug Interaction

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

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