Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

Noguchi, Yoshihiro; Tachi, Tomoya; Teramachi, Hitomi

doi:10.1007/s11095-020-02801-3

Cited by 50 publications

(63 citation statements)

References 19 publications

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“…Therefore, to verify the accuracy of the subset analysis, we needed to prepare "hypothetical" true data of AEs. A previous comparative study [14] of five algorithms for detecting drug-drug interaction signals revealed that the Ω shrinkage measure model [16] detected the most conservative signal, while the combination risk ratio model [17] did not detect any interaction signal in less than three reports due to the detection criterion. Therefore, of the five algorithms, we used the combination of signals detected by the three algorithms (the additive model, the multiplicative model, and the chi-square statistical model) as "hypothetical" true data.…”

Section: Discussionmentioning

confidence: 99%

“…This study has the following three limitations. First, unfortunately, unknown AE data do not exist anywhere in the world [14]. Therefore, there were no "real" true data for AEs.…”

Section: Discussionmentioning

confidence: 99%

“…The design of this study is based on a previous paper that discussed trends in methods to detect the signals of AEs caused by individual drugs [15] or drug-drug interactions [14].…”

Section: Methodsmentioning

confidence: 99%

“…The AE targeted for this study was Stevens-Johnson syndrome (SJS), which was extracted from the dataset using the PT in MedDRA/J. However, the choice of target adverse events is the same as in previous similar studies [14,15], and there was no medical or pharmacological reason for this choice.…”

Section: Definitions Of Adverse Drug Eventsmentioning

confidence: 99%

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

2020

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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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Section: Discussionmentioning

confidence: 99%

“…This study has the following three limitations. First, unfortunately, unknown AE data do not exist anywhere in the world [14]. Therefore, there were no "real" true data for AEs.…”

Section: Discussionmentioning

confidence: 99%

“…The design of this study is based on a previous paper that discussed trends in methods to detect the signals of AEs caused by individual drugs [15] or drug-drug interactions [14].…”

Section: Methodsmentioning

confidence: 99%

Section: Definitions Of Adverse Drug Eventsmentioning

confidence: 99%

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

2020

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“…We have already demonstrated that the Ω shrinkage measure 21 proposed by Norén et al detected the most conservative signals in a previous comparative study. 22 In this study, we used two signal search algorithms for searching the JADER database of spontaneous reports of AEs in Japan to detect a signal of each AED and drug interaction, and we evaluated the association between the onset of SJS and TEN and the AED combination therapies.…”

Section: Introductionmentioning

confidence: 99%

Antiepileptic combination therapy with Stevens‐Johnson syndrome and toxic epidermal necrolysis: Analysis of a Japanese pharmacovigilance database

et al. 2020

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Objective: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are immune-mediated diseases characterized by an extensive loss of the epidermal skin layer, often resulting in death. SJS and TEN are often triggered by certain drugs, including antiepileptic drugs (AEDs). Epilepsy is very difficult to treat and often involves the combination of two or more AEDs. In this study, we quantified not only the risk of SJS or TEN associated with single-AED therapy but also the risk related to concomitant AED treatment using reporting-derived signals. Methods: An analysis of the Japanese Adverse Drug Event Report (JADER) database was performed from the first quarter of 2004 to the fourth quarter of 2018. The single-AED signals were evaluated using the proportional reporting ratio (PRR), and the combination therapy signals were evaluated using Ω shrinkage measure and combination risk ratio (CRR). Results: SJS signals were associated with 11 AEDs, and TEN signals were related to 12 AEDs. Moreover, the following AED combinations were associated with SJS signals: carbamazepine-lorazepam (Ω 025 : 0.33, CRR: 2.18) and fosphenytoin-lorazepam (Ω 025 : 0.99, CRR: 39.20). The TEN signals were related to the following combi

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Tumor Lysis Syndrome Associated with Monoclonal Antibodies in Patients with Multiple Myeloma: A Pharmacovigilance Study Based on the FAERS Database

Xia

Gong

Zhao

et al. 2023

Clin Pharma and Therapeutics

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Although some tumor lysis syndrome (TLS) cases have been reported with patients with multiple myeloma (MM) taking monoclonal antibodies (mAbs), the association between TLS and mAbs remains mostly unknown. We aim to investigate the association between TLS and mAbs and describe clinical features. We conducted a disproportionality analysis to investigate the link between mAbs and TLS by excluding known confounders and compared with other anticancer drugs. The association between mAbs and TLS was evaluated using information component (IC). Drugdrug interaction signals were calculated based on the Ω shrinkage measure. Parametric distribution with the goodness-of-fit test was used for the reported time-to-onset analysis. From 2016 Q1, to 2022 Q4, a total of 274 TLS with mAbs were reported in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. There were 27% of patients with TLS with mAbs who died and 20.1% occurred a life-threatening situation. Daratumumab, elotuzumab, and belantamab mafodotin presented a robust disproportionate signal of TLS after excluding known confounders (IC 025 > 0). Daratumumab had the highest disproportionate signal of TLS among all anticancer drugs for MM. Reported time-to-onset analysis showed the median days for TLS with daratumumab, isatuximab, elotuzumab, and belantamab mafodotin were 1.5, 14.5, 5.5, and 5.5 days, respectively. The drugdrug interaction analysis showed the co-administration of drugs known to increase urate, induce hyperkalemia, or hypocalcemia elevated the reporting frequency for TLS with mAbs (Ω 025 > 0). Our postmarketing pharmacovigilance analysis detected the reporting association of TLS and mAbs in patients with MM. Additional studies with robust epidemiological study designs that can validate these findings are warranted.

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Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

Cited by 50 publications

References 19 publications

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

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

Antiepileptic combination therapy with Stevens‐Johnson syndrome and toxic epidermal necrolysis: Analysis of a Japanese pharmacovigilance database

Tumor Lysis Syndrome Associated with Monoclonal Antibodies in Patients with Multiple Myeloma: A Pharmacovigilance Study Based on the FAERS Database

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