An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study

Mickael Arnaud, Bernard Bégaud, Frantz Thiessard, Quentin Jarrion, Julien Bezin, Antoine Pariente and Francesco Salvo ()
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Mickael Arnaud: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219
Bernard Bégaud: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219
Frantz Thiessard: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, ERIAS Team, UMR 1219
Quentin Jarrion: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219
Julien Bezin: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219
Antoine Pariente: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219
Francesco Salvo: University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Pharmacoepidemiology Team, UMR 1219

Drug Safety, 2018, vol. 41, issue 4, No 6, 377-387

Abstract: Abstract Introduction 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. Aim 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. Methods 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. Results 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. Conclusion The developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.

Date: 2018
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DOI: 10.1007/s40264-017-0618-y

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