JMIR Publications

ABSTRACT

Background:

Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.

Objective:

We aimed at developing a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.

Methods:

Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (70%) and validation (30%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver–operator characteristic curve (ROC–AUC) and association measures in the validation cohort.

Results:

Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher according to the addition of predictive factors, attaining the best performance of an ROC–AUC of 0.818 (95% confidence interval 0.792–0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were, respectively, 0.744 (0.716–0.773) and 0.745 (0.709–0.776).

Conclusions:

For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on prehospital information including chief complaints.