Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions

Yauheniya Cherkas (), Joshua Ide and John Stekelenborg
Additional contact information
Yauheniya Cherkas: Janssen R&D
Joshua Ide: Johnson & Johnson Consumer, Inc
John Stekelenborg: Janssen R&D

Drug Safety, 2022, vol. 45, issue 5, No 15, 582 pages

Abstract: Abstract Introduction Causality assessment of individual case safety reports (ICSRs) is an important step in pharmacovigilance case-level review and aims to establish a position on whether a patient’s exposure to a drug is causally related to the patient experiencing an untoward adverse event. There are many different approaches for case causality adjudication, including the use of expert opinions and algorithmic frameworks; however, a great deal of variability exists between assessment methods, products, therapeutic classes, individual physicians, change of process and conventions over time, and other factors. Objective The objective of this study was to develop a machine learning-based model that can predict the likelihood of a causal association of an observed drug–reaction combination in an ICSR. Methods In this study, we used a set of annotated solicited ICSRs (50K cases) from a company post-marketing database. These data were enriched with novel supplementary features from external and internal data sources that aim to capture facets such as temporal plausibility, scientific validity, and confoundedness that have been shown to contribute to causality adjudication. Using these features, we constructed a Bayesian network (BN) model to predict drug–event pair causality assessment. BN topology was driven by an internally developed ICSR causality decision support tool. Performance of the model was evaluated through examination of sensitivity, positive predictive value (PPV), and the area under the receiver operating characteristic curve (AUC) on an independent set of data from a temporally adjacent interval (20K cases). No external validation was performed because of a lack of publicly available ICSRs with causality assessments for drug–event pairs. Results The model demonstrated high performance in predicting the causality assessment of drug–event pairs compared with clinical judgment using global introspection (AUC 0.924; 95% confidence interval [CI] 0.922–0.927). The sensitivity of the model was 0.900 (95% CI 0.896–0.904), and the PPV of the model was 0.778 (95% CI 0.773–0.783). Conclusion These results show that robust probabilistic modeling of ICSR causality is feasible, and the approach used in the development of the model can serve as a framework for such causality assessments, leading to improvements in safety decision making.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40264-022-01163-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01163-6

Ordering information: This journal article can be ordered from
http://www.springer.com/adis/journal/40264

DOI: 10.1007/s40264-022-01163-6

Access Statistics for this article

Drug Safety is currently edited by Nitin Joshi

More articles in Drug Safety from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().