Work

Cost-effectiveness of first-line osimertinib informed by electronic medical records via text-mining: a real-world Italian case study of EGFR-mutated advanced NSCLC patients

Year: 2025

Type: article

Abstract: Background Tyrosine kinase inhibitors (TKIs) have significantly changed the therapeutic landscape of non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor ( EGFR ) gene mutation. The primary objective is to assess the use of real-world data (RWD) extracted from electronic health records (EHRs) via a text-mining technique. Then, RWD are used to inform a cost-effectiveness model of first-line TKI third-generation osimertinib compared with standard EGFR inhibitors. Materials and methods A cohort of 190 NSCLC EGFR -mutated patients has been enrolled from an Italian cancer institute. A rule-based named-entity recognition algorithm is carried out to extract RWD from medical records stored in the data warehouse. A Bayesian cost-effectiveness analysis is developed from the Italian health care system perspective. Survival curves are extrapolated over an observed follow-up horizon of a real-world population to estimate the time to treatment discontinuation (TTD) and overall survival (OS) distributions. Results An incremental cost-effectiveness ratio (ICER) of 48 398€/quality-adjusted life year (QALY) is found for the base-case scenario, while 51 779€/QALY is found for a second scenario. While higher total annual costs are observed, effectiveness analysis confirms greater QALYs associated with osimertinib versus non-osimertinib. This result is also supported by significantly longer TTD (median 15 months) and OS (median 27 months) for osimertinib. Sensitivity and scenario analyses are used to validate the results. Conclusions This study represents a pilot assessment for encouraging the adoption of text-mining approaches for RWD generation and informing health economic evaluations in oncology. RWD analysis confirms that osimertinib is cost-effective as a first-line treatment compared with previous generations of TKIs for advanced EGFR -mutated NSCLC patients.

Source: ESMO Real World Data and Digital Oncology

DOI: 10.1016/j.esmorw.2025.100198