Early prediction of level-of-care requirements in patients with COVID-19
- Boston University, United States
- Massachusetts General Hospital, Harvard Medical School, United States
Abstract
This study examined records of 2,566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively.ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
Data availability
- Source code for processing patient data is provided together with the submission.- Due to HIPAA restrictions and Data Use Agreements we can not make the original patient data publicly available. Interested parties may submit a request to obtain access to de-identified data to the authors. The authors would request pertinent IRB approval to make available a de-identified version of the data, stripped of any protected health information as specified under HIPAA rules.-The IRB of the hospital system approved the study under Protocol #2020P001112 and the Boston University IRB found the study as being Not Human Subject Research under Protocol #5570X (the BU team worked with a de-identified limited dataset).
Article and author information
Author details
Shahabeddin Sotudian
Ioannis Ch Paschalidis
Funding
National Science Foundation (IIS-1914792)
- Ioannis Ch Paschalidis
National Science Foundation (DMS-1664644)
- Ioannis Ch Paschalidis
National Science Foundation (CNS-1645681)
- Ioannis Ch Paschalidis
National Institute of General Medical Sciences (R01 GM135930)
- Ioannis Ch Paschalidis
Office of Naval Research (N00014-19-1-2571)
- Ioannis Ch Paschalidis
National Institutes of Health (UL54 TR004130)
- Ioannis Ch Paschalidis
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The Institutional Review Board of Mass General Brigham reviewed and approved the study under Protocol #2020P001112. The Boston University IRB found the study as being Not Human Subject Research under Protocol #5570X (the BU team worked with a de-identified limited dataset).
Copyright
© 2020, Hao 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
-
- 3,257
- views
-
- 293
- downloads
-
- 51
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
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)
Early prediction of level-of-care requirements in patients with COVID-19
eLife 9:e60519.
https://doi.org/10.7554/eLife.60519
Further reading
-
- Medicine
- Microbiology and Infectious Disease
- Epidemiology and Global Health
- Immunology and Inflammation
eLife has published articles on a wide range of infectious diseases, including COVID-19, influenza, tuberculosis, HIV/AIDS, malaria and typhoid fever.
-
- Medicine
- Neuroscience
Monomethyl fumarate (MMF) and its prodrug dimethyl fumarate (DMF) are currently the most widely used agents for the treatment of multiple sclerosis (MS). However, not all patients benefit from DMF. We hypothesized that the variable response of patients may be due to their diet. In support of this hypothesis, mice subjected to experimental autoimmune encephalomyelitis (EAE), a model of MS, did not benefit from DMF treatment when fed a lauric acid-rich (LA) diet. Mice on normal chow (NC) diet, in contrast, and even more so mice on high-fiber (HFb) diet showed the expected protective DMF effect. DMF lacked efficacy in the LA diet-fed group despite similar resorption and preserved effects on plasma lipids. When mice were fed the permissive HFb diet, the protective effect of DMF treatment depended on hydroxycarboxylic receptor 2 (HCAR2) which is highly expressed in neutrophil granulocytes. Indeed, deletion of Hcar2 in neutrophils abrogated DMF protective effects in EAE. Diet had a profound effect on the transcriptional profile of neutrophils and modulated their response to MMF. In summary, DMF required HCAR2 on neutrophils as well as permissive dietary effects for its therapeutic action. Translating the dietary intervention into the clinic may improve MS therapy.
