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Severity of illness models for respiratory syncytial virus-associated hospitalization
1Department of Pediatrics and Communicable Diseases, University of Michigan Medical School, and Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
Insights
Predictive models for pediatric respiratory syncytial virus (RSV) hospitalizations are feasible. These models accurately identify children at risk for prolonged stays, aiding clinical management.
Area of Science:
- Pediatric Infectious Diseases
- Clinical Epidemiology
- Biostatistics
Background:
- Respiratory syncytial virus (RSV) is a common cause of pediatric hospitalization.
- Predicting prolonged hospital stays in young children with RSV is clinically significant.
- Existing severity of illness models may not be optimized for RSV in this age group.
Purpose of the Study:
- To assess the feasibility of developing multivariate severity of illness models for pediatric patients hospitalized with RSV.
- To identify key variables measurable on the first hospital day that predict prolonged hospitalization.
- To evaluate the predictive performance (discrimination and calibration) of such models.
Main Methods:
- Retrospective cohort study of 802 hospitalized children aged 2 years or younger with community-acquired RSV.
- Multivariate logistic regression analysis using nine variables assessed on day 1 of hospitalization.
- Receiver operator characteristic (ROC) curve analysis for model discrimination.
- Goodness-of-fit testing for model calibration.
Main Results:
- 182 (23%) of patients experienced prolonged hospitalization (≥7 days).
- Multivariate logistic regression identified significant predictors of prolonged hospitalization (p < 0.0001).
- The model demonstrated excellent discrimination (Area Under the Curve = 0.894) and calibration (p = 0.216).
Conclusions:
- Multivariate severity of illness models for RSV-associated hospitalizations are feasible and possess excellent predictive properties.
- The developed models show strong classification, discrimination, and calibration.
- Further research is needed to validate generalizability across different centers and epidemics.
Abstract:
The objective of this investigation was to examine the feasibility of multivariate severity of illness models for pediatric patients hospitalized with respiratory syncytial virus (RSV) infection. From a preexisting retrospective cohort study database, all infants and children 2 yr of age or younger with community-acquired RSV infection admitted to the University of Michigan's C. S. Mott Children's Hospital during nine epidemics were examined. The study group consisted of 802 hospitalized patients younger than 2 yr of age with community-acquired RSV infection; 182 (23%) patients had prolonged hospital length of stay defined as 7 d or greater. Multivariate logistic regression modeling of nine variables measurable during the first hospital day was strongly associated with prolonged hospitalization (p < 0.0001). Receiver operator characteristic curve analysis resulted in an area under the curve of 0.894, indicating excellent model discrimination. Goodness-of-fit testing indicated excellent model calibration for observed versus predicted outcomes (p = 0.216). We conclude that severity of illness models for RSV-associated hospitalization with excellent predictive properties in terms of classification, discrimination, and calibration are possible. Further study is required to determine if such models are generalizable across multiple centers and epidemics.