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K-medoids clustering of hospital admission characteristics to classify severity of influenza virus infection
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  • Aleda Leis,
  • Erin McSpadden,
  • Hannah Segaloff,
  • Adam Lauring,
  • Caroline Cheng,
  • Joshua Petrie,
  • Lois Lamerato,
  • Manish Patel,
  • Brendan Flannery,
  • Jill Ferdinands,
  • Carrie Karvonen-Gutierrez,
  • Arnold Monto,
  • Emily Martin
Aleda Leis
University of Michigan School of Public Health

Corresponding Author:aledat@umich.edu

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Erin McSpadden
University of Michigan School of Public Health
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Hannah Segaloff
University of Michigan School of Public Health
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Adam Lauring
University of Michigan Medical School
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Caroline Cheng
University of Michigan School of Public Health
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Joshua Petrie
Marshfield Clinic Research Institute
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Lois Lamerato
Henry Ford Health System
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Manish Patel
Centers for Disease Control and Prevention
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Brendan Flannery
Centers for Disease Control and Prevention
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Jill Ferdinands
Centers for Disease Control and Prevention
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Carrie Karvonen-Gutierrez
University of Michigan School of Public Health
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Arnold Monto
University of Michigan
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Emily Martin
University of Michigan
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Abstract

Background: Patients are admitted to the hospital for respiratory illness at different stages of their disease course. It is important to appropriately analyse this heterogeneity in surveillance data to accurately measure disease severity among those hospitalized. The purpose of this study was to determine if unique baseline clusters of influenza patients exist, and to examine the association between cluster membership and in-hospital outcomes. Methods: Patients hospitalized with influenza at two hospitals in Southeast Michigan during the 2017/2018 (n=242) and 2018/2019 (n=115) influenza seasons were included. Physiologic and laboratory variables were collected for the first 24 hours of the hospital stay. K-medoids clustering was used to determine groups of individuals based on these values. Multivariable linear regression or Firth’s logistic regression were used to examine the association between cluster membership and clinical outcomes. Results: Three clusters were selected for 2017/2018, mainly differentiated by blood glucose level. After adjustment, those in C171 had 5.6 times the odds of mechanical ventilator use than those in C172 (95%CI: 1.49,21.1) and a significantly longer mean hospital length of stay than those in both C172 (mean 1.5 days longer, 95%CI: 0.2,2.7) and C173 (mean 1.4 days longer, 95%CI: 0.3,2.5). Similar results were seen between the two clusters selected for 2018/2019. Conclusion: In this study of hospitalized influenza patients, we show that distinct clusters with higher disease acuity can be identified and could be targeted for evaluations of vaccine and influenza antiviral effectiveness against disease attenuation. The association of higher disease acuity with glucose level merits evaluation.
08 Dec 2022Submitted to Influenza and other respiratory viruses
09 Dec 2022Submission Checks Completed
09 Dec 2022Assigned to Editor
11 Dec 2022Reviewer(s) Assigned
26 Jan 2023Review(s) Completed, Editorial Evaluation Pending
26 Jan 2023Editorial Decision: Revise Minor
10 Feb 20231st Revision Received
11 Feb 2023Submission Checks Completed
11 Feb 2023Assigned to Editor
14 Feb 2023Editorial Decision: Accept