Leveraging Machine Learning to Assess the Association of Rash and Survival in Patients With Advanced NSCLC

Author(s)

Qianyu Yuan, PhD, Aaron Dolor, PhD, Yunzhi Qian, PhD, Doug Donnelly, BS, Melissa Estevez, MS, Yulia Kuznetsova, PhD, Nisha Singh, MS, Prakirthi Yerram, PharmD;
Flatiron Health, New York, NY, USA

Presentation Documents

OBJECTIVES: The association between rash and survival is well-documented for first and second-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs), but less for third-generation. This study leveraged machine learning (ML)-extracted real-world adverse events (rwAEs) to evaluate incidence and association between rash incidence and survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with EGFR TKIs.
METHODS: This study used the nationwide Flatiron Health electronic health record-derived, deidentified database. The study included adults aged ≥18 years with advanced EGFR-mutated NSCLC, treated with 1L EGFR TKI monotherapy between January 2011 and June 2024. A natural language processing model was used to extract rwAEs. Descriptive statistics were used to compare the incidence of 37 rwAEs overall and by TKI generation. Kaplan-Meier and Cox models evaluated the association between rash incidence and real-world overall survival (rwOS) and progression-free survival (rwPFS). This study also evaluated ICD codes and ML extraction, alone and combined, for identifying rash and its relationship with survival outcomes.
RESULTS: 5606 patients were included in the analysis. Compared with first- and second-generation TKIs, third-generation TKIs showed higher incidences of anemia, and QT prolongation and lower rash, aligning with clinical trials. Overall, rash incidence was 51%. Rash was associated with improved rwOS (hazard ratio [HR], 0.75; confidence interval [CI], 0.70-0.81) and rwPFS (HR, 0.85; CI, 0.80-0.90) across all TKI generations, notably with third-generation TKIs (rwOS: HR, 0.64; CI, 0.57-0.72; rwPFS: HR, 0.73; CI, 0.67-0.81). Using ICD codes alone showed lower rash incidence (11%) than combining ML extraction with ICD codes (52%), but survival benefits were consistent across methods.
CONCLUSIONS: The study supports the use of ML to scalably extract rwAEs. With earlier-generation EGFR TKIs, rwAE incidence and rash-related survival benefit aligned with clinical expectations. Third-generation TKIs demonstrate similar survival benefits.

Conference/Value in Health Info

2025-05, ISPOR 2025, Montréal, Quebec, CA

Value in Health, Volume 28, Issue S1

Code

MSR24

Topic

Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics

Disease

SDC: Oncology, STA: Personalized & Precision Medicine

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