All cases leverage the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to analyse large-scale healthcare data for real-world evidence (RWE).
Case 1: Dual-Agent Anti-Hypertensive Drug
- Challenge: Pharmaceutical company needed additional evidence to support a new dual-agent anti-hypertensive drug for a quick EMA (European Medicines Agency) bid resubmission.
- Solution: Analysed electronic medical records (EMR) from Germany and France using OMOP to show the drug's effectiveness in reducing blood pressure.
- Impact: Results supported the drug's bid and highlighted the benefits of OMOP for fast turnaround projects.
Case 2: Menopausal Women and Vasomotor Symptoms
- Challenge: Characterize menopausal patient populations for a client's RWE study.
- Solution: Leveraged the OHDSI network and OMOP to analyse data from multiple databases for a comprehensive characterization.
- Impact: Identified thiazide diuretics as the most effective first-line anti-hypertensive drugs compared to ACE inhibitors. This study led to a publication in The Lancet and established a framework for future network studies.
Case 3: Incidence of Adverse Cardiovascular Events in patients with acute myocardial infarction
- Challenge: A pharmaceutical company needed real-world data on adverse cardiovascular events after a heart attack to support their novel cardiovascular therapy.
- Solution: Analysed data from the US PharMetrics Plus database using OMOP to identify risk factors for adverse events after a heart attack.
- Impact: Results complemented ongoing clinical trials by characterizing real-world incidence rates and informing future work for the client.
Case 4: LEGEND Hypertension
- Challenge: Determine the optimal first-line therapy for hypertension with limited data on comparative effectiveness.
- Solution: Analysed data from multiple databases in the OHDSI network using OMOP to compare the effectiveness and safety of different anti-hypertensive drug classes.
- Impact: Identified thiazide diuretics as the most effective first-line treatment and established a framework for future large-scale network studies in other therapeutic areas.
Case 5: Predicting Future Cost Burden in T2DM
- Challenge: A pharmaceutical company wanted to understand which patients with type 2 diabetes mellitus (T2DM) would benefit most from their drug to inform future clinical trials.
- Solution: Analysed data from multiple databases using OMOP to develop a model predicting high-cost T2DM patients.
- Impact: Insights helped the client design future clinical trials to target patients most likely to benefit from their drug.
Case 6: Large-Scale Analysis of Dual Combination Therapies for Hypertension
- Challenge: Lack of data on clinical use patterns of dual combination therapies for hypertension.
- Solution: Analysed data from 11 databases across 8 countries using the federated network model of OHDSI.
- Impact: Identified significant variation in dual combination therapy use across countries and demographics, highlighting the need for more standardized treatment patterns.
Key Takeaways:
- OMOP allows for standardized analysis of healthcare data from various sources, increasing analytical power.
- It facilitates real-world evidence generation for quicker regulatory processes and drug development.
- The OHDSI network provides collaborative tools and expertise for large-scale studies.
- OMOP findings can inform clinical trial design, marketing strategies, and healthcare resource allocation.
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