A Predictive Model Development of Hospital Admission during Triage in a Chinese Ear, Nose, and Throat Emergency...

Triaging patients into correct severity categories in an emergency department is an advanced skill that depends on a quick assessment after obtaining very little information. The purpose of this study was to assess specific risk factors associated with hospital admissions in the emergency department environment of the specialized Eye, Ear, Nose, and Throat hospital located in Shanghai, China.

This study was a retrospective cohort study. Patients visiting the emergency department in a tertiary hospital in eastern China from February 2008 to August 2015 were included. Univariate and multivariate analyses were used to identify the risk factors related to hospital admissions. Combining variables calculated from the regression equation of multivariate analysis (binary logistic regression analysis) enabled the risk factors quantification. The receiver operating characteristic analysis was used to identify the most informative cutoff point of the combining predictors.

A total of 188715 patients were enrolled in the study. Of them, 8395 patients (4.4%) required hospital admission. Hour of visit, season, age, sex, chief complaint, anatomical location, and locale of patients were independent risk factors of hospital admission by univariate and multivariate analysis. Combining predictors were calculated from the equation of the multivariate logistic model. The area under the curve of the combining predictors was 0.949, and the 95% confidence interval was 0.947 to 0.951 (P <.001), with a sensitivity of 95.2% and a specificity of 85.6%. A cutoff score of less than -35.1975 was associated with hospital admission.

This study provided a method to build a feasible predictive model of hospital admission during triage. Understanding risk factors is an important part of the triage process in order to correctly assign priorities to the patients served. The outcomes of this study would add additional information for the triage nurse to consider in assessing the patient and assigning acuity ratings. The model developed here requires validation in future research.