Yauheniya Cherkas1, Joshua Ide2, John van Stekelenborg3. 1. Janssen R&D, Horsham, PA, USA. ycherka@its.jnj.com. 2. Johnson & Johnson Consumer, Inc, Skillman, NJ, USA. 3. Janssen R&D, Horsham, PA, USA.
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.
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.
Authors: Kristof Huysentruyt; Oeystein Kjoersvik; Pawel Dobracki; Elizabeth Savage; Ellen Mishalov; Mark Cherry; Eileen Leonard; Robert Taylor; Bhavin Patel; Danielle Abatemarco Journal: Drug Saf Date: 2021-02-01 Impact factor: 5.606