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.
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Early prediction of level-of-care requirements in patients with COVID-19
eLife 9:e60519.
https://doi.org/10.7554/eLife.60519
Further reading
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- 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.
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- Medicine
- Microbiology and Infectious Disease
Background:
Under which conditions antibiotic combination therapy decelerates rather than accelerates resistance evolution is not well understood. We examined the effect of combining antibiotics on within-patient resistance development across various bacterial pathogens and antibiotics.
Methods:
We searched CENTRAL, EMBASE, and PubMed for (quasi)-randomised controlled trials (RCTs) published from database inception to 24 November 2022. Trials comparing antibiotic treatments with different numbers of antibiotics were included. Patients were considered to have acquired resistance if, at the follow-up culture, a resistant bacterium (as defined by the study authors) was detected that had not been present in the baseline culture. We combined results using a random effects model and performed meta-regression and stratified analyses. The trials’ risk of bias was assessed with the Cochrane tool.
Results:
42 trials were eligible and 29, including 5054 patients, qualified for statistical analysis. In most trials, resistance development was not the primary outcome and studies lacked power. The combined odds ratio for the acquisition of resistance comparing the group with the higher number of antibiotics with the comparison group was 1.23 (95% CI 0.68–2.25), with substantial between-study heterogeneity (I2=77%). We identified tentative evidence for potential beneficial or detrimental effects of antibiotic combination therapy for specific pathogens or medical conditions.
Conclusions:
The evidence for combining a higher number of antibiotics compared to fewer from RCTs is scarce and overall compatible with both benefit or harm. Trials powered to detect differences in resistance development or well-designed observational studies are required to clarify the impact of combination therapy on resistance.
Funding:
Support from the Swiss National Science Foundation (grant 310030B_176401 (SB, BS, CW), grant 32FP30-174281 (ME), grant 324730_207957 (RDK)) and from the National Institute of Allergy and Infectious Diseases (NIAID, cooperative agreement AI069924 (ME)) is gratefully acknowledged.
