TY - JOUR
T1 - Detecting and filtering immune-related adverse events signal based on text mining and observational health data sciences and informatics common data model
T2 - Framework development study
AU - Yu, Yue
AU - Ruddy, Kathryn
AU - Mansfield, Aaron
AU - Zong, Nansu
AU - Wen, Andrew
AU - Tsuji, Shintaro
AU - Huang, Ming
AU - Liu, Hongfang
AU - Shah, Nilay
AU - Jiang, Guoqian
N1 - Publisher Copyright:
© 2020 Yue Yu, Kathryn Ruddy, Aaron Mansfield, Nansu Zong, Andrew Wen, Shintaro Tsuji, Ming Huang, Hongfang Liu, Nilay Shah, Guoqian Jiang.
PY - 2020/6
Y1 - 2020/6
N2 - Background: Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective: The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration-approved immune checkpoint inhibitors. Methods: In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. Results: By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. Conclusions: We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.
AB - Background: Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. Objective: The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration-approved immune checkpoint inhibitors. Methods: In our framework, we first used the Food and Drug Administration's Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. Results: By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. Conclusions: We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.
KW - Adverse drug reaction reporting systems/standards
KW - Drug-related side effects and adverse reactions
KW - Immunotherapy/adverse effects
KW - Pharmacovigilance
KW - Text mining
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U2 - 10.2196/17353
DO - 10.2196/17353
M3 - Article
AN - SCOPUS:85097460217
SN - 2291-9694
VL - 8
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
IS - 6
M1 - e17353
ER -