Selected article for: "admission illness and logistic regression"

Author: Christopher M. Petrilli; Simon A. Jones; Jie Yang; Harish Rajagopalan; Luke F. O'Donnell; Yelena Chernyak; Katie Tobin; Robert J. Cerfolio; Fritz Francois; Leora I. Horwitz
Title: Factors associated with hospitalization and critical illness among 4,103 patients with COVID-19 disease in New York City
  • Document date: 2020_4_11
  • ID: 8prg1goh_20
    Snippet: We used descriptive statistics to characterize each cohort of patients: those not hospitalized, all those hospitalized, those discharged to home, and those with critical illness (care in intensive care unit, mechanical ventilation, discharge to hospice, or death). We then fitted multivariable logistic regression models with admission and with critical illness as the outcomes to identify factors associated with those outcomes. We included all sele.....
    Document: We used descriptive statistics to characterize each cohort of patients: those not hospitalized, all those hospitalized, those discharged to home, and those with critical illness (care in intensive care unit, mechanical ventilation, discharge to hospice, or death). We then fitted multivariable logistic regression models with admission and with critical illness as the outcomes to identify factors associated with those outcomes. We included all selected predictors based on a priori clinical significance after testing for collinearity using the variance inflation factor (VIF) and ensuring none had VIF>2. 16 For the admission model, we included all patients testing positive. For the critical illness model, we included only patients who had been discharged alive or had suffered severe complications, omitting patients still hospitalized, for whom final outcome

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