Using Real World Evidence to predicting patient disease progression
Global PharmaCo aiming to understand from Real World Evidence size and treatment journey of EU patient populations in new indication as well as to predict patients early-on to provide optimal treatment.
Global PharmaCo seeking to leverage Real World Evidence in EU countries to engage medical stakeholders as well as payers in access discussions and to develop AI capabilities to strengthen future evidence generation practice.
Focus was to support a pre-launch asset in new indication where there was limited understanding of the eligible patient population leading to delayed diagnosis and non-optimal treatment pathways for patients
Therefore objective was to
Size and better characterize patient populations in key European countries
Predict patients disease progression and find risk-factors to identify patients earlier on
Demonstrate the potential value of optimized treatment
Analytic approach and data sources
Used anonymized patient–level medical records from different sources to overcome limitations in EU data sets
Build consistent research data model to apply same analyses to different data sets and increase replicability of analytics
Mapped prevalence and incidence for target patient population on large RWE data sets validating different definitions
Developed novel methodology to identify patient events not directly retrievable from medical codes leveraging expert panel
Analyze local patients characteristics, journeys, and treatment pathways to quantify overall opportunity towards optimal treatment
Applied predictive and explanatory machine learning models to identify patient populations most likely to progress to higher disease severity
Performed clustering of patient archetypes and identifies allocated risk-profile.
50% of patients going through sub-optimal treatment journey
5-12 months prediction of patients ahead of time (AUC 0.83)
5-30% opportunity to reduce hospitalizations
Insights are expected to support:
Market access and payor discussion – with health economic model that can be build based on prevalence estimates
Medical – with published results that can be used for medical knowledge dissemination and education of HCPs
Commercial – with insights about patient pathways that can be used in messaging to physicians