1. Medicine
  2. Microbiology and Infectious Disease
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Early prediction of level-of-care requirements in patients with COVID-19

  1. Boran Hao
  2. Shahabeddin Sotudian
  3. Taiyao Wang
  4. Tingting Xu
  5. Yang Hu
  6. Apostolos Gaitanidis
  7. Kerry Breen
  8. George C Velmahos
  9. Ioannis Ch Paschalidis  Is a corresponding author
  1. Boston University, United States
  2. Massachusetts General Hospital, Harvard Medical School, United States
Research Article
  • Cited 11
  • Views 2,705
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Cite this article as: eLife 2020;9:e60519 doi: 10.7554/eLife.60519

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

  1. Boran Hao

    Center for Information and Systems Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Shahabeddin Sotudian

    Center for Information and Systems Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5864-6192
  3. Taiyao Wang

    Center for Information and Systems Eng., Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0331-3892
  4. Tingting Xu

    Center for Information and Systems Eng, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Yang Hu

    Center for Information and Systems Engineering, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Apostolos Gaitanidis

    Division of Trauma, Emergency Services, and Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Kerry Breen

    Division of Trauma, Emergency Services, and Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. George C Velmahos

    Division of Trauma, Emergency Services, and Surgical Critical Care, Massachusetts General Hospital, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Ioannis Ch Paschalidis

    Department of Electrical and Computer Engineering, and Biomedical Engineering, Boston University, Boston, United States
    For correspondence
    yannisp@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3343-2913

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).

Reviewing Editor

  1. Evangelos J Giamarellos-Bourboulis, Attikon University Hospital, Greece

Publication history

  1. Received: June 29, 2020
  2. Accepted: October 4, 2020
  3. Accepted Manuscript published: October 12, 2020 (version 1)
  4. Version of Record published: October 29, 2020 (version 2)

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.

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  1. Further reading

Further reading

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    Wuqing Huang et al.
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    Background: Lipid metabolism plays an important role in viral infections. We aimed to assess the causal effect of lipid-lowering drugs (HMGCR inhibitiors, PCSK9 inhibitiors and NPC1L1 inhibitior) on COVID-19 outcomes using 2-sample Mendelian Randomization (MR) study.

    Methods: We used two kinds of genetic instruments to proxy the exposure of lipid-lowering drugs, including eQTLs of drugs target genes, and genetic variants within or nearby drugs target genes associated with LDL cholesterol from GWAS. Summary-data-based MR (SMR) and inverse-variance weighted MR (IVW-MR) were used to calculate the effect estimates.

    Results: SMR analysis found that a higher expression of HMGCR was associated with a higher risk of COVID-19 hospitalization (OR=1.38, 95%CI=1.06-1.81). Similarly, IVW-MR analysis observed a positive association between HMGCR-mediated LDL cholesterol and COVID-19 hospitalization (OR=1.32, 95%CI=1.00-1.74). No consistent evidence from both analyses was found for other associations.

    Conclusions: This 2-sample MR study suggested a potential causal relationship between HMGCR inhibition and the reduced risk of COVID-19 hospitalization.

    Funding: Fujian Province Major Science and Technology Program.

    1. Computational and Systems Biology
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    Background:

    Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.

    Methods:

    Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.

    Results:

    Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery.

    Conclusions:

    We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention.

    Funding:

    NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).