Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster session 14

1231P - Machine learning prediction of the case-fatality of COVID-19 and risk factors for adverse outcomes in patients with non-small cell lung cancer

Date

21 Oct 2023

Session

Poster session 14

Topics

COVID-19 and Cancer;  Cancer Diagnostics

Tumour Site

Non-Small Cell Lung Cancer

Presenters

Yeji Jung

Citation

Annals of Oncology (2023) 34 (suppl_2): S711-S731. 10.1016/S0923-7534(23)01942-7

Authors

Y. Jung1, S. Park2, J. Sun3, M. Ahn3, J.S. Ahn3, S. Lee3, H.A. Jung3

Author affiliations

  • 1 Hematology-oncology, Samsung Medical Center (SMC), 135-710 - Seoul/KR
  • 2 Department Of Hematology And Medical Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06615 - Seoul/KR
  • 3 Hematology And Medical Oncology, Samsung Medical Center, 06615 - Seoul/KR

Resources

Login to get immediate access to this content.

If you do not have an ESMO account, please create one for free.

Abstract 1231P

Background

Since the emergence of Coronavirus-2019 (COVID-19) across the globe, patients with cancer have been found to have an increased risk of infection with COVID-19 and are highly likely to experience a severe disease course. This study analyzed the clinical outcomes of COVID-19 in patients with non-small cell lung cancer (NSCLC) and identified the risk factors for adverse outcomes.

Methods

This study analyzed the case-fatality rate and risk factors for COVID-19 among NSCLC patients diagnosed between January 2020 and April 2022 at the Samsung Medical Center, using a machine-learning prediction method. Additionally, the study investigated the effect of COVID-19 on the systemic treatment of patients with advanced-stage NSCLC.

Results

Overall,1,127 patients were included in this study, with 10.3% of the patients being older than 75; of these patients, 51.8% were ex- or current smokers. Among the 584 cured after surgery, 91 had stable disease after concurrent chemo-radiotherapy, and 452 had recurrent or metastatic NSCLC. Of the 452 patients, 387 received palliative systemic treatment during COVID-19, including 188 receiving targeted therapy, 111 receiving cytotoxic chemotherapy, 62 receiving immunotherapy +/- chemotherapy, and 26 receiving other agents. Out of these, 94.6% continued systemic treatment after COVID-19, only one patient discontinued due to complications, and 18 changed their treatment due to disease progression. The case fatality rates were 0.86% for early-stage NSCLC, 4.4% for locally advanced NSCLC, and 9.95% for advanced NSCLC. The factors associated with fatalities included palliative chemotherapy, age (≥75), diabetes, smoking history, lung radiotherapy, hypertension, sex, and COPD. The model using logistic regression and eXtreme gradient boosting performed well (AUC for both = 0.84).

Conclusions

NSCLC patients had a case fatality rate of 4.8% and most continued systemic treatment despite COVID-19. However, those with risk factors need close attention for managing COVID-19 complications.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

M. Ahn: Financial Interests, Personal, Advisory Board: AstraZeneca, Takeda, MSD, Yuhan, Amgen, Alpha Pharmaceutical, Janssen, BMS, Roche, Daiichi Sankyo, Merck, Boronoi. S. Lee: Financial Interests, Personal, Advisory Board: AstraZeneca/MedImmune, Roche, Merck, Pfizer, Lilly, BMS/Ono, Takeda, Janssen, IMBdx; Financial Interests, Personal, Invited Speaker: AstraZeneca/MedImmune, Roche, Merck, Lilly, Amgen; Financial Interests, Institutional, Research Grant: Merck, AstraZeneca, Lunit. All other authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.