한빛사논문
Hyun Ae Jung, MD, PhD1; Dae Ho Lee, MD, PhD2; Sun Min Lim, MD, PhD3; Hyeyeon Yu, BS1; Shinkyo Yoon, MD, PhD2; DongKyu Kim, PhD4,5; Kyu-pyo Kim, MD, PhD2; Hyehyun Jeong, MD, PhD2; Haewon Doh, BS4; Subin Lim, BS4; Joohyun Kim, MS6; Xiahong Zhao, PhD7; David Horsburgh, MBA7; Dony Patel, PhD8; Jung-Ae Kim, PhD6; Kong Chian Toh, MPH7
1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
2Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
3Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
4Real-World Evidence Team, ALYND, Yonsei University Health System, Seoul, South Korea
5Department of Family Medicine, Yonsei University College of Medicine, Seoul, South Korea
6IQVIA Solutions, Real World Solutions–Korea, Seoul, South Korea
7IQVIA Solutions, Real World Solutions–Asia Pacific, Singapore
8IQVIA Solutions, Real World Solutions–Global, London, United Kingdom
Corresponding Author: Hyun Ae Jung, MD, PhD
Abstract
Importance: Valuable evidence regarding clinical characteristics, treatments, and outcomes for non-small cell lung cancer (NSCLC) is limited to individual hospital databases or national-level registries. The common data and federated analysis framework developed through the Extensible Platform for Observational Research in Lung Cancer (EXPLORE-LC) initiative allows for research across multiple high-quality data sources, which may provide a deeper understanding of the NSCLC landscape and identification of unmet needs of subpopulations.
Objective: To describe clinical characteristics, initial treatment patterns, subsequent treatment, and overall survival (OS) of patients with NSCLC in South Korea.
Design, setting, and participants: This multicenter cohort study included patients aged 18 years and older who were diagnosed with NSCLC between 2014 and 2019 and followed up until March 2020 at 3 tertiary hospitals in South Korea. Clinical data were collected using a common data model and clinical data warehouse. Patients who had an initial diagnosis of nonsquamous (NSQ) or squamous (SQ) NSCLC and who had received at least 1 treatment for NSCLC were included in the study. Data were analyzed from June through November 2022.
Main outcomes and measures: The primary outcome was clinical OS for patients with NSCLC. Secondary outcomes were clinical characteristics and treatment patterns subsequent to the diagnosis of NSCLC.
Results: Among 22 101 patients with NSCLC who received anticancer treatment analyzed in this study, 17 350 patients (78.5%) had NSQ and 4751 patients (21.5%) had SQ NSCLC. Clinical characteristics and outcomes and treatment patterns were assessed for 13 084 patients with NSQ cancer who had known EGFR and ALK status (75.4%; mean [SD] 62.2 [10.5] years; 6552 males [50.1%]) and all 4751 patients with SQ cancer (mean [SD] age, 67.1 [8.6] years; 4427 males [93.2%]). More than half of patients with NSQ cancer were never smokers (7399 patients [56.6%]). Patients with SQ cancer were mostly males and former or current smokers (4235 patients [89.1%]) and were diagnosed at a later clinical stage than patients with NSQ cancer (eg, stage I: 1165 patients [24.5%] vs 5388 patients [41.2%]). Patients with EGFR-positive and ALK-positive NSQ cancer diagnosed between 2017 and 2019 had better median OS than similar patients diagnosed between 2014 and 2016 (EGFR-positive: not reached [95% CI, 35.9 months to not reached] vs 28.4 months [95% CI, 25.8 to 30.0 months]; P < .001; ALK-positive: not reached [95% CI, not reached] vs 49.5 months [95% CI, 35.1 months to not reached]; P < .001). No significant difference was observed in OS from first-line treatment for patients with SQ cancer.
Conclusions and relevance: This study, which pooled medical data from multiple clinical data warehouses to produce a large study cohort, may provide meaningful insights into the clinical practice of NSCLC and underscores the value of a common data model approach. The analyzable dataset may hold great promise for future comprehensive identification of subpopulations and unmet needs.
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