Author
Listed:
- Frederik van Delft
(Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands)
- Mirte Muller
(Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Noord-Holland, the Netherlands)
- Rom Langerak
(Formal Methods and Tools Group, Faculty of EEMCS, University of Twente, Enschede, Overijssel, the Netherlands)
- Hendrik Koffijberg
(Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands)
- Valesca Retèl
(Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands
Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands)
- Daan van den Broek
(Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, Noord-Holland, the Netherlands)
- Maarten IJzerman
(Health Technology and Services Research Department, Technical Medical Centre, University of Twente, Enschede, Overijssel, the Netherlands
Centre for Cancer Research and Centre for Health Policy, University of Melbourne, Parkville, Melbourne, Australia
Peter MacCallum Cancer Centre, Parkville, Melbourne, Australia)
Abstract
Background Although immunotherapy (IMT) provides significant survival benefits in selected patients, approximately 10% of patients experience (serious) immune-related adverse events (irAEs). The early detection of adverse events will prevent irAEs from progressing to severe stages, and routine testing for irAEs has become common practice. Because a positive test outcome might indicate a clinically manifesting irAE that requires treatment to (temporarily) discontinue, the occurrence of false-positive test outcomes is expected to negatively affect treatment outcomes. This study explores how the UPPAAL modeling environment can be used to assess the impact of test accuracy (i.e., test sensitivity and specificity), on the probability of patients entering palliative care within 11 IMT cycles. Methods A timed automata-based model was constructed using real-world data and expert consultation. Model calibration was performed using data from 248 non–small-cell lung cancer patients treated with nivolumab. A scenario analysis was performed to evaluate the effect of changes in test accuracy on the probability of patients transitioning to palliative care. Results The constructed model was used to estimate the cumulative probabilities for the patients’ transition to palliative care, which were found to match real-world clinical observations after model calibration. The scenario analysis showed that the specificity of laboratory tests for routine monitoring has a strong effect on the probability of patients transitioning to palliative care, whereas the effect of test sensitivity was limited. Conclusion We have obtained interesting insights by simulating a care pathway and disease progression using UPPAAL. The scenario analysis indicates that an increase in test specificity results in decreased discontinuation of treatment due to suspicion of irAEs, through a reduction of false-positive test outcomes.
Suggested Citation
Frederik van Delft & Mirte Muller & Rom Langerak & Hendrik Koffijberg & Valesca Retèl & Daan van den Broek & Maarten IJzerman, 2021.
"Modeling Diagnostic Strategies to Manage Toxic Adverse Events following Cancer Immunotherapy,"
Medical Decision Making, , vol. 41(6), pages 693-705, August.
Handle:
RePEc:sae:medema:v:41:y:2021:i:6:p:693-705
DOI: 10.1177/0272989X211002756
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