Literature DB >> 33374503

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

Yoshihiro Noguchi1, Keisuke Aoyama1, Satoaki Kubo1, Tomoya Tachi1, Hitomi Teramachi1,2.   

Abstract

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.

Entities:  

Keywords:  combination risk ratio; concomitant signal score; drug-drug interaction; proportional reporting ratio; spontaneous reporting systems

Year:  2020        PMID: 33374503      PMCID: PMC7822185          DOI: 10.3390/ph14010004

Source DB:  PubMed          Journal:  Pharmaceuticals (Basel)        ISSN: 1424-8247


  18 in total

1.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

Authors:  S J Evans; P C Waller; S Davis
Journal:  Pharmacoepidemiol Drug Saf       Date:  2001 Oct-Nov       Impact factor: 2.890

2.  Comparison of data mining methodologies using Japanese spontaneous reports.

Authors:  Kiyoshi Kubota; Daisuke Koide; Toshiki Hirai
Journal:  Pharmacoepidemiol Drug Saf       Date:  2004-06       Impact factor: 2.890

Review 3.  The reporting odds ratio and its advantages over the proportional reporting ratio.

Authors:  Kenneth J Rothman; Stephan Lanes; Susan T Sacks
Journal:  Pharmacoepidemiol Drug Saf       Date:  2004-08       Impact factor: 2.890

Review 4.  Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.

Authors:  Santiago Vilar; Carol Friedman; George Hripcsak
Journal:  Brief Bioinform       Date:  2018-09-28       Impact factor: 11.622

5.  Utilization of chi-square statistics for screening adverse drug-drug interactions in spontaneous reporting systems.

Authors:  Masahiko Gosho; Kazushi Maruo; Keisuke Tada; Akihiro Hirakawa
Journal:  Eur J Clin Pharmacol       Date:  2017-03-09       Impact factor: 2.953

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

Authors:  Yoshihiro Noguchi; Tomoya Tachi; Hitomi Teramachi
Journal:  Pharm Res       Date:  2020-04-30       Impact factor: 4.200

7.  A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System.

Authors:  Yoshihiro Noguchi; Anri Ueno; Manami Otsubo; Hayato Katsuno; Ikuto Sugita; Yuta Kanematsu; Aki Yoshida; Hiroki Esaki; Tomoya Tachi; Hitomi Teramachi
Journal:  Front Pharmacol       Date:  2018-03-09       Impact factor: 5.810

Review 8.  Review of Statistical Methodologies for Detecting Drug-Drug Interactions Using Spontaneous Reporting Systems.

Authors:  Yoshihiro Noguchi; Tomoya Tachi; Hitomi Teramachi
Journal:  Front Pharmacol       Date:  2019-11-08       Impact factor: 5.810

9.  A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms.

Authors:  André M Carrington; Paul W Fieguth; Hammad Qazi; Andreas Holzinger; Helen H Chen; Franz Mayr; Douglas G Manuel
Journal:  BMC Med Inform Decis Mak       Date:  2020-01-06       Impact factor: 2.796

View more
  1 in total

1.  Comment on: "Drug-Drug Interaction of the Sodium Glucose Co-transporter 2 Inhibitors with Statins and Myopathy: A Disproportionality Analysis Using Adverse Events Reporting Data".

Authors:  Yoshihiro Noguchi
Journal:  Drug Saf       Date:  2022-06-17       Impact factor: 5.228

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.