4.5 Article

Ten-year patient journey of stage III non-small cell lung cancer patients: A single-center, observational, retrospective study in Korea (Realtime autOmatically updated data warehOuse in healTh care; UNIVERSE-ROOT study)

期刊

LUNG CANCER
卷 146, 期 -, 页码 112-119

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.lungcan.2020.05.033

关键词

Real-time updated system; Big data; Real-world data; NSCLC; Treatment

资金

  1. AstraZeneca Korea

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Introduction: Until the recent approval of immunotherapy after completing concurrent chemoradiotherapy (CCRT), there has been little progress in treating unresectable stage III non-small cell lung cancer (NSCLC). This prompted us to search real-world data (RWD) to better understand diagnosis and treatment patterns, and outcomes. Methods: This non-interventional observational study used a unique, novel algorithm for big data analysis to collect and assess anonymized patient electronic medical records from a clinical data warehouse (CDW) over a 10-year period to capture real-world patterns of diagnosis, treatment, and outcomes of stage III NSCLC patients. We describe real-world patterns of diagnosis and treatment of patients with newly-diagnosed stage III NSCLC, and patients' characteristics, and assessment of treatment outcomes. Results: We analyzed clinical variables from 23,735 NSCLC patients. Stage III patients (N = 4138, 18.2 %) were diagnosed as IIIA (N = 2,547, 11.2 %) or IIIB (N = 1,591. 7.0 %). Treated stage III patients (N = 2530, 61.1 %) had a median age of 64.2 years, were mostly male (78.5 %) and had an ECOG performance status of 1 (65.2 %). Treatment comprised curative-intent surgery (N = 1,254, 49.6 %) with 705 receiving neoadjuvant therapy; definitive CRT (N = 648, 25.6 %); palliative CT (N = 270, 10.7 %), or thoracic RT (N = 170, 6.7 %). Median OS (range) for neoadjuvant, surgery, CRT, palliative chemotherapy, lung RT alone, and supportive care was 49.2 (42.0-56.5), 52.5 (43.1-61.9), 30.3 (26.6-34.0), 14.7 (13.0-16.4), 8.8 (6.2-11.3), and 2.0 (1.0-3.0) months, respectively. Conclusions: This unique in-house algorithm enabled a rapid and comprehensive analysis of big data through a CDW, with daily automatic updates that documented real-world PFS and OS consistent with the published literature, and real-world treatment patterns and clinical outcomes in stage III NSCLC patients.

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