Open AccessCharacterizing the Feasibility and Performance of Real-World Tumor Progression End Points and Their Association With Overall Survival in a Large Advanced Non–Small-Cell Lung Cancer Data Set
Author(s) -
Sandra D. Griffith,
Rebecca Miksad,
Geoff Calkins,
Paul You,
Nicole G. Lipitz,
Ariel B. Bourla,
Erin R. Williams,
Daniel J. George,
Deborah Schrag,
Sean Khozin,
William B. Capra,
Michael D. Taylor,
Amy Pickar Abernethy
Publication year - 2019
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.19.00013
Subject(s) - medicine , discontinuation , lung cancer , real world data , clinical endpoint , progression free survival , tumor progression , oncology , clinical trial , survival analysis , cancer , overall survival , computer science , data science
PURPOSE Large, generalizable real-world data can enhance traditional clinical trial results. The current study evaluates reliability, clinical relevance, and large-scale feasibility for a previously documented method with which to characterize cancer progression outcomes in advanced non–small-cell lung cancer from electronic health record (EHR) data.METHODS Patients who were diagnosed with advanced non–small-cell lung cancer between January 1, 2011, and February 28, 2018, with two or more EHR-documented visits and one or more systemic therapy line initiated were identified in Flatiron Health’s longitudinal EHR-derived database. After institutional review board approval, we retrospectively characterized real-world progression (rwP) dates, with a random duplicate sample to ascertain interabstractor agreement. We calculated real-world progression-free survival, real-world time to progression, real-world time to next treatment, and overall survival (OS) using the Kaplan-Meier method (index date was the date of first-line therapy initiation), and correlations between OS and other end points were assessed at the patient level (Spearman’s ρ).RESULTS Of 30,276 eligible patients,16,606 (55%) had one or more rwP event. Of these patients, 11,366 (68%) had subsequent death, treatment discontinuation, or new treatment initiation. Correlation of real-world progression-free survival with OS was moderate to high (Spearman’s ρ, 0.76; 95% CI, 0.75 to 0.77; evaluable patients, n = 20,020), and for real-world time to progression correlation with OS was lower (Spearman’s ρ, 0.69; 95% CI, 0.68 to 0.70; evaluable patients, n = 11,902). Interabstractor agreement on rwP occurrence was 0.94 (duplicate sample, n = 1,065) and on rwP date 0.85 (95% CI, 0.81 to 0.89; evaluable patients n = 358 [patients with two independent event captures within 30 days]). Median rwP abstraction time from individual EHRs was 18.0 minutes (interquartile range, 9.7 to 34.4 minutes).CONCLUSION We demonstrated that rwP-based end points correlate with OS, and that rwP curation from a large, contemporary EHR data set can be reliable, clinically relevant, and feasible on a large scale.