TY - JOUR
T1 - A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data
AU - Hsieh, Miyuki Hsing Chun
AU - Liang, Hsun Yin
AU - Tsai, Chih Ying
AU - Tseng, Yu Ting
AU - Chao, Pi Hui
AU - Huang, Wei I.
AU - Chen, Wen Wen
AU - Lin, Swu Jane
AU - Lai, Edward Chia Cheng
N1 - Funding Information:
This work was supported by research grants from the Taiwan Food and Drug Administration (TFDA) (MOHW109-FDA -D-113-000411). The content of this article does not represent any official position of the TFDA.
Publisher Copyright:
© 2023 Hsieh et al. This work is published and licensed by Dove Medical Press Limited.
PY - 2023
Y1 - 2023
N2 - Purpose: Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating datamining tools, and evaluate adaptability of such system. Patients and Methods: Based on relevant guidelines and structural frameworks in Taiwan’s pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan’s National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system. Results: Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation. Conclusion: The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.
AB - Purpose: Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating datamining tools, and evaluate adaptability of such system. Patients and Methods: Based on relevant guidelines and structural frameworks in Taiwan’s pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan’s National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system. Results: Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation. Conclusion: The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.
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U2 - 10.2147/CLEP.S395922
DO - 10.2147/CLEP.S395922
M3 - Article
AN - SCOPUS:85146703521
SN - 1179-1349
VL - 15
SP - 91
EP - 107
JO - Clinical Epidemiology
JF - Clinical Epidemiology
ER -