An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study
- 288 Downloads
Signal detection from healthcare databases is possible, but is not yet used for routine surveillance of drug safety. One challenge is to develop methods for selecting signals that should be assessed with priority.
The aim of this study was to develop an automated system combining safety signal detection and prioritization from healthcare databases and applicable to drugs used in chronic diseases.
Patients present in the French EGB healthcare database for at least 1 year between 2005 and 2015 were considered. Noninsulin glucose-lowering drugs (NIGLDs) were selected as a case study, and hospitalization data were used to select important medical events (IME). Signal detection was performed quarterly from 2008 to 2015 using sequence symmetry analysis. NIGLD/IME associations were screened if one or more exposed case was identified in the quarter, and three or more exposed cases were identified in the population at the date of screening. Detected signals were prioritized using the Longitudinal-SNIP (L-SNIP) algorithm based on strength (S), novelty (N), and potential impact of signal (I), and pattern of drug use (P). Signals scored in the top 10% were identified as of high priority. A reference set was built based on NIGLD summaries of product characteristics (SPCs) to compute the performance of the developed system.
A total of 815 associations were screened and 241 (29.6%) were detected as signals; among these, 58 (24.1%) were prioritized. The performance for signal detection was sensitivity = 47%; specificity = 80%; positive predictive value (PPV) 33%; negative predictive value = 82%. The use of the L-SNIP algorithm increased the early identification of positive controls, restricted to those mentioned in the SPCs after 2008: PPV = 100% versus PPV = 14% with its non-use. The system revealed a strong new signal with dipeptidylpeptidase-4 inhibitors and venous thromboembolism.
The developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.
Compliance with ethical standards
Conflicts of interest
Mickael Arnaud, Bernard Bégaud, Frantz Thiessard, Quentin Jarrion, Julien Bezin, Antoine Pariente, and Francesco Salvo have no conflict of interest directly relevant to the content of this study.
This study is part of the Drugs Systematized Assessment in real-liFe Environment (DRUGS-SAFE) research program that is funded by the French Medicines Agency (Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM). The program aims at providing an integrated system allowing the concomitant monitoring of drug use and safety in France. The potential impact of drugs (e.g., non-insulin glucose-lowering drugs), the frailty of populations, and the seriousness of risks drive the research program. This publication represents the views of the authors and does not necessarily represent the opinion of the French Medicines Agency.
- 31.Jamekornkul C, Chaisumritchoke ST. Developing a signal triage algorithm for Thai national adverse drug reaction database. Thai J Pharm Sci. 2016;40(3):153–7.Google Scholar
- 33.European Medicines Agency. Guideline on good pharmacovigilance practices (GVP): module IX—signal management. 2012. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/06/WC500129138.pdf. Accessed 18 Aug 2017.
- 34.United States Food and Drug Administration. Classifying significant postmarketing drug safety issues. Fed Regist. 2012. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM295211.pdf. Accessed 16 Nov 2017.
- 35.Sottosanti L, Ferrazin F. Italian pharmacovigilance system. Manns Pharmacovigil. 3rd ed. Chichester: Wiley; 2014.Google Scholar
- 36.Coloma PM, Schuemie MJ, Trifirò G, Furlong L, van Mulligen E, Bauer-Mehren A, et al. Drug-induced acute myocardial infarction: identifying “prime suspects” from electronic healthcare records-based surveillance system. PLoS ONE 2013;8:e72148. https://doi.org/10.1371/journal.pone.0072148.
- 37.Bezin J, Duong M, Lassalle R, Droz C, Pariente A, Blin P, et al. The national healthcare system claims databases in France, SNIIRAM and EGB: Powerful tools for pharmacoepidemiology. Pharmacoepidemiol. Drug Saf. 2017. http://doi.wiley.com/10.1002/pds.4233. Accessed 2 Jun 2017.
- 38.European Medicines Agency. Inclusion/exclusion criteria for the “Important medical events” list. 2017. http://www.ema.europa.eu/docs/en_GB/document_library/Other/2016/08/WC500212100.pdf. Accessed 16 Nov 2017.
- 50.Arnaud M, Bezin J, Bégaud B, Pariente A, Salvo F. Trends in the incidence of use of noninsulin glucose-lowering drugs between 2006 and 2013 in France. Fundam Clin Pharmacol. 2017;31(6):663–75.Google Scholar