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

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          Abstract

          Background

          Since the emergence of coronavirus disease 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

          The study included patients diagnosed with COVID-19 between January 2020 and April 2022 at the Samsung Medical Center in Seoul, Republic of Korea, who have a history of NSCLC. The case-fatality rate and risk factors for COVID-19 were analyzed 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 years; of these patients, 51.8% were ex- or current smokers. Among the 584 patients cured after surgery, 91 had stable disease after concurrent chemo-radiotherapy, and 452 had recurrent or metastatic NSCLC. Among 452 patients with recurrent or metastatic NSCLC, 387 received systemic treatment in a palliative setting during COVID-19. Of these, 188 received targeted therapy, 111 received cytotoxic chemotherapy, 63 received immunotherapy +/− chemotherapy, and 26 received other agents. Among them, 94.6% of patients continued systemic treatment after the COVID-19 infection. Only one patient discontinued treatment because of complications of the COVID-19 infection, and 18 patients changed their systemic treatment because of disease progression. The case fatality rates were 0.86% for patients with early-stage NSCLC, 4.4% for patients with locally advanced NSCLC, and 9.96% for patients with advanced NSCLC. The factors associated with fatalities included palliative chemotherapy, type of palliative chemotherapy, age (≥75 years), diabetes, smoking history, history of lung radiotherapy, hypertension, sex, and chronic obstructive pulmonary disease (COPD). The predictive model using logistic regression and eXtreme Gradient Boosting (XGB) performed well [area under the curve (AUC) for logistic regression =0.84 and AUC for XGB =0.84].

          Conclusions

          The case fatality rate in patients with NSCLC was 4.8%, while most patients with advanced NSCLC continued to receive systemic treatment. However, patients with risk factors require careful management of COVID-19 complications.

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          Most cited references18

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          Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

          There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy

              In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) emerged in China and has spread globally, creating a pandemic. Information about the clinical characteristics of infected patients who require intensive care is limited.
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                Author and article information

                Journal
                Transl Cancer Res
                Transl Cancer Res
                TCR
                Translational Cancer Research
                AME Publishing Company
                2218-676X
                2219-6803
                27 June 2024
                30 June 2024
                : 13
                : 6
                : 2587-2595
                Affiliations
                [1 ]deptDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center , Sungkyunkwan University School of Medicine , Seoul, Republic of Korea;
                [2 ]deptBiomedical Statistics Center, Data Science Research Institute, Samsung Medical Center , Sungkyunkwan University School of Medicine , Seoul, Republic of Korea;
                [3 ]deptDivision of Infectious Diseases, Samsung Medical Center , Sungkyunkwan University School of Medicine , Seoul, Republic of Korea
                Author notes

                Contributions: (I) Conception and design: YJ Jung, HA Jung; (II) Administrative support: J Ahn, S Park, JM Sun, SH Lee, JS Ahn, MJ Ahn, SY Cho; (III) Provision of study materials or patients: HA Jung; (IV) Collection and assembly of data: J Ahn; (V) Data analysis and interpretation: YJ Jung; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                [#]

                These authors contributed equally to this work as co-first authors.

                [*]

                These authors contributed equally to this work.

                Correspondence to: Hyun Ae Jung, MD, PhD. Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea. Email: hyunae.jung@ 123456samsung.com ; Sun Young Cho, MD, PhD. Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea. Email: sunyoung81.jo@ 123456samsung.com .
                Author information
                https://orcid.org/0000-0002-3163-6015
                Article
                tcr-13-06-2587
                10.21037/tcr-23-2188
                11231785
                e2a45cdc-3f44-40ad-b7c8-56d32af817a0
                2024 Translational Cancer Research. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

                History
                : 28 November 2023
                : 07 May 2024
                Funding
                Funded by: the Korean Society of Medical Oncology (KSMO) 2021 and the National Research Foundation of Korea (NRF) funded by the Ministry of Science and Information and Communication Technology (ICT)
                Award ID: No. NRF-2021R1F1A1054782
                Categories
                Original Article

                coronavirus disease 2019 outbreak (covid-19 outbreak),case-fatality rate,non-small cell lung cancer (nsclc)

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