| アブストラクト | BACKGROUND: While traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs). OBJECTIVE: This study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems. METHODS: Messages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided. RESULTS: The comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase. CONCLUSIONS: The specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals. |
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| 投稿日 | 2018/11/22 |
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| 投稿者 | Kurzinger, Marie-Laure; Schuck, Stephane; Texier, Nathalie; Abdellaoui, Redhouane; Faviez, Carole; Pouget, Julie; Zhang, Ling; Tcherny-Lessenot, Stephanie; Lin, Stephen; Juhaeri, Juhaeri |
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| ジャーナル名 | Journal of medical Internet research |
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| 組織名 | Epidemiology and Benefit Risk Evaluation, Sanofi, Chilly-Mazarin, France.;Kappa Sante, Paris, France.;Kap Code, Paris, France.;Information Technology and Solutions, Sanofi, Lyon, France.;Global Pharmacovigilance, Sanofi, Bridgewater, NJ, United States.;Epidemiology and Benefit Risk Evaluation, Sanofi, Bridgewater, NJ, United States. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/30459145/ |
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