International multicenter study comparing COVID-19 in patients with cancer to patients without cancer: impact of risk factors and treatment modalities on survivorship
Abstract
Background: In this international multicenter study we aimed to determine the independent risk factors associated with increased 30-day mortality and the impact of cancer and novel treatment modalities in a large group of patients with and without cancer with COVID-19 from multiple countries.
Methods: We retrospectively collected de-identified data on a cohort of patients with and without cancer diagnosed with COVID-19 between January and November 2020, from 16 international centers.
Results: We analyzed 3966 COVID-19 confirmed patients, 1115 with cancer and 2851 nwithout cancer patients. Patients with cancer were more likely to be pancytopenic, and have a smoking history, pulmonary disorders, hypertension, diabetes mellitus, and corticosteroid use in the preceding two weeks (p≤0.01). In addition, they were more likely to present with higher inflammatory biomarkers (D-dimer, ferritin and procalcitonin), but were less likely to present with clinical symptoms (p≤0.01). By country-adjusted multivariable logistic regression analyses, cancer was not found to be an independent risk factor for 30-day mortality (p=0.18) whereas lymphopenia was independently associated with increased mortality in all patients, and in patients with cancer. Older age (≥65 years) was the strongest predictor of 30-day mortality in all patients(OR=4.47, p<0.0001). Remdesivir was the only therapeutic agent independently associated with decreased 30-day mortality ()(OR=0.64, p=0.036). Among patients on low-flow oxygen at admission, patients who received remdesivir had a lower 30-day mortality rate than those who did not (5.9% vs 17.6%; p=0.03).
Conclusions: Increased 30-day all-cause mortality from COVID-19 was not independently associated with cancer but was independently associated with lymphopenia often observed in hematolgic malignancy. Remdesivir, particularly in patients with cancer receiving low-flow oxygen, can reduce 30-day all-cause mortality.
Funding: National Cancer Institute, National Institutes of Health.
Data availability
We are unable to share the data given our restriction policy and the fact that this study includes data from 16 centers from the five continents and we have no agreement in place to share data.*Thank you for indicating that you are unable to make this data publicly available and providing some further information regarding this. Exceptions to our usual data sharing policy are subject to editorial approval, as per our data availability policy [https://submit.elifesciences.org/html/elife_author_instructions.html#policies]. To enable the editors to make an informed decision, please can you provide further information on the data sharing plan for your manuscript. This information should be added to your data availability statement (found in the Submission Information section of the submission form). Some of these points may already be covered, however please ensure that the statement covers the following:- Please provide an explanation of why the data cannot be shared.We are unable to share our data given the restrictive policy of our institution. We also do not have the permission to share data from the other institutions that participated in this study- Please describe how an interested researcher would be able to access the original data e.g. Who would they need to contact? Do they need to apply or submit a project proposal? If so, who would assess this proposal (e.g. a data access committee or IRB)? Are there any restrictions on who can access the data e.g. could commercial research be performed on the data?Given that this study involves data from 16 centers from the five continents, we have no agreement to share data.- Please provide any code or software that you have used to analyse the data.The analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC).- Please provide access to all materials and data for which the restrictions do not apply. For instance, would it be possible to share a deidentified version of the dataset? If not, would you be able to share processed version of the dataset e.g. an Excel sheet with numbers used to plot the graphs and charts in your manuscript?We are unable to share the data.
Article and author information
Author details
Funding
National Cancer Institute
- Issam I Raad
NIH Clinical Center
- Issam I Raad
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: This study (Protocol # 2020-0437) was approved by the institutional review board at MD Anderson Cancer Center and the institutional review boards of the collaborating centers. A patient waiver of informed consent was obtained.
Reviewing Editor
- Samra Turajlic, The Francis Crick Institute, United Kingdom
Version history
- Received: June 24, 2022
- Preprint posted: August 26, 2022 (view preprint)
- Accepted: January 16, 2023
- Accepted Manuscript published: January 30, 2023 (version 1)
- Version of Record published: March 2, 2023 (version 2)
Copyright
© 2023, Raad et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 656
- Page views
-
- 123
- Downloads
-
- 1
- Citations
Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Epidemiology and Global Health
- Medicine
- Microbiology and Infectious Disease
eLife has published the following articles on SARS-CoV-2 and COVID-19.
-
- Cancer Biology
- Computational and Systems Biology
Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, ‘Blood pressure reading without diagnosis’, ‘Abnormalities of heartbeat’, and ‘Intestinal obstruction’ were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories ‘Cough→Jaundice→Intestinal obstruction’ and ‘Pain→Jaundice→Abnormal results of function studies’. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.