Annals of emergency medicine
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Multicenter Study
Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.
This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. ⋯ Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.
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Observational Study
A Prospective Evaluation of Clinical HEART Score Agreement, Accuracy, and Adherence in Emergency Department Chest Pain Patients.
The HEART score is a risk stratification aid that may safely reduce chest pain admissions for emergency department patients. However, differences in interpretation of subjective components potentially alters the performance of the score. We compared agreement between HEART scores determined during clinical practice with research-generated scores and estimated their accuracy in predicting 30-day major adverse cardiac events. ⋯ ED clinicians had only moderate agreement with research HEART scores. Combined with uncertainties regarding accuracy in predicting major adverse cardiac events, we urge caution in the widespread use of the HEART score as the sole determinant of ED disposition.