Evidence map›Paper›PMID 38988924›Full record

ArticleTranslational cancer research2024

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

Ye Ji Jung, Joonghyun Ahn, Sehhoon Park, Jong-Mu Sun, Se-Hoon Lee, Jin Seok Ahn, Myung-Ju Ahn, Sun Young Cho, Hyun Ae Jung

Abstract read
In one paragraph

Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ye Ji Jung *Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0002-3163-6015
Joonghyun Ahn *Biomedical Statistics Center, Data Science Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Sehhoon ParkDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jong-Mu SunDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Se-Hoon LeeDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jin Seok AhnDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Myung-Ju AhnDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Sun Young Cho *Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Hyun Ae Jung *Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

case-fatality rateCoronavirus disease 2019 outbreak (COVID-19 outbreak)non-small cell lung cancer (NSCLC)

Identifiers

PMID38988924
PMCPMC11231785

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.