K‐medoids clustering of hospital admission characteristics to classify severity of influenza virus infection

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 h 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.