OHDSI Global | Enhancing Cardiovascular Adverse Event Detection in ICI-Treated Cancer Patients: Lessons Learned from Natural Language Processing Integration with OMOP CDM
Cardiovascular (CV) adverse events (AEs) in cancer patients receiving immune checkpoint inhibitors (ICIs) are often under-detected in clinical trials, partly due to the strict inclusion criteria and limited follow-up typical of such studies. This underscores the need for real-world data analysis to gain a fuller understanding of these events. In this study, we leveraged both structured data and NLP-extracted unstructured electronic health record (EHR) data. The integration of NLP with structured data enriched the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), providing a valuable analytic use case for the OHDSI community.
Download the poster (as showcased at the OHDSI Global 2024 conference) by completing the form.
Background
• Cardiovascular (CV) adverse events (AEs) in cancer patients receiving immune checkpoint inhibitors (ICIs) are often under-detected in clinical trials.
• Clinical trials typically have strict inclusion criteria and incomplete follow-up, highlighting the need for real-world data analysis.
• This study used both structured data and NLP-extracted unstructured EHR data.
• The integration of NLP with structured data enriched the OMOP CDM, presenting an important analytic use case for the OHDSI community.
Conclusion
• NLP enhances the detection of CV AEs in ICI-treated cancer patients, particularly for less common events like pericarditis (78%) and myocarditis (60%).
• The combination of NLP and structured data improves AE identification, with NLP contributing 32-78% of additional cases across various categories.
• High precision, recall, and F1 scores validate the accuracy of NLP, enabling more comprehensive follow-up and monitoring in oncology care.
Complete the form above to download the poster and discover the methods used and the results obtained.
Click HERE to visit our Oncology Insights overview for more information on the LynxCare datasets within our European hospital network.
Background
• Cardiovascular (CV) adverse events (AEs) in cancer patients receiving immune checkpoint inhibitors (ICIs) are often under-detected in clinical trials.
• Clinical trials typically have strict inclusion criteria and incomplete follow-up, highlighting the need for real-world data analysis.
• This study used both structured data and NLP-extracted unstructured EHR data.
• The integration of NLP with structured data enriched the OMOP CDM, presenting an important analytic use case for the OHDSI community.
Conclusion
• NLP enhances the detection of CV AEs in ICI-treated cancer patients, particularly for less common events like pericarditis (78%) and myocarditis (60%).
• The combination of NLP and structured data improves AE identification, with NLP contributing 32-78% of additional cases across various categories.
• High precision, recall, and F1 scores validate the accuracy of NLP, enabling more comprehensive follow-up and monitoring in oncology care.
Complete the form above to download the poster and discover the methods used and the results obtained.
Click HERE to visit our Oncology Insights overview for more information on the LynxCare datasets within our European hospital network.
