Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

Patrick G. Lyons, Sivasubramanium V. Bhavani, Aaloke Mody, Alice Bewley, Katherine Dittman, Aisling Doyle, Samuel L. Windham, Tej M. Patel, Bharat Neelam Raju, Matthew Keller, Matthew M. Churpek, Carolyn S. Calfee, Andrew P. Michelson, Thomas Kannampallil, Elvin H. Geng, Pratik Sinha

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. Methods: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. Findings: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. Interpretation: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. Funding: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Original languageEnglish
Article number104295
JournalEBioMedicine
Volume85
DOIs
StatePublished - Nov 2022

Keywords

  • Hospital outcomes
  • Influenza
  • SARS-CoV-2
  • Statistical modelling
  • Viral pneumonia

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