INTRODUCTION
Infectious respiratory diseases caused by influenza virus, respiratory
syncytial virus, and SARS-CoV-2 can cause significant illness and are
responsible for hundreds of thousands of hospitalizations in the United
States annually1. Data on in-hospital progression of
disease and treatment course are broadly available and used to evaluate
severity of illness2,3, or the impact of
vaccination4,5 and treatment6-8.
However, the primary cause of admission, particularly in those with
baseline multimorbidity, might not be due to acute illness but other
causes either exacerbated by milder respiratory tract infection (e.g.,
asthma) or possibly unrelated to infection (e.g., dehydration). This
might bias results of vaccine or antiviral effectiveness against
prevention or attenuation of severe disease. Differences in general
health and health care seeking behaviour are difficult to directly
measure9,10, and individuals may present and be
admitted to the hospital at different stages in their disease course
with varying disease severity. These patterns vary by population, health
system, and specific etiology11-14. While patients
hospitalized with respiratory diseases such as influenza have
historically been older with significant
comorbidity11,15, the pattern has differed in various
phases of the COVID-19 pandemic16.
The heterogeneity of the hospitalized population at admission creates
challenges when examining events occurring during hospitalization.
Differential baseline comorbidity and presenting symptomology can
significantly confound the use of hospital data as a surveillance metric
for respiratory disease severity, and can bias estimates of the
effectiveness of interventions to reduce influenza morbidity or
progression of disease.
Unsupervised machine learning algorithms provide a way to derive and
characterize different groups of patients independent of an outcomes or
treatment framework17,18. When applied to clinical
data, this methodology can help identify distinct phenotypes of
individuals driven by underlying relationships between health metrics.
The aims of the current study were to develop clinically distinct
clusters of patients based on laboratory and physiologic measurements
within the first 24 hours of hospitalization, to determine if cluster
membership was associated with worse in-hospital outcomes, and to
evaluate the association of influenza vaccination on in-hospital
outcomes within a given cluster.