ArticleDrug safety2018
An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study.
Article in Drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
- Novel Antidiabetic Drugs and Risk of Venous Thromboembolism: A Literature Review.Seminars in thrombosis and hemostasis · 2025Review
- Recommendations to Enable Broader Use of Real-World Evidence to Inform Decision-Making Throughout Pharmacovigilance Signal Management.Pharmacoepidemiology and drug safety · 2025Review
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.Pharmacoepidemiology and drug safety · 2023Article
- High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.Drug safety · 2022Article
- Concomitant Medication Use With Xiyanping Injection and the Risk of Suspected Allergic Reactions: A Nested Case-Control Study Based on China's National Medical Insurance Database.Frontiers in pharmacology · 2022Article
- Dipeptidyl peptidase-4 inhibitors and risk of venous thromboembolism: data mining of FDA adverse event reporting system.International journal of clinical pharmacy · 2020Article
- Hypothesis-free signal detection in healthcare databases: finding its value for pharmacovigilance.Therapeutic advances in drug safety · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSignal 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.
aimThe 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.
methodsPatients 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.
resultsA 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.
conclusionThe developed system seems promising for the routine use of healthcare data for safety surveillance of drugs used in chronic diseases.
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