Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department

Abstract

Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016−2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.

Keywords: artificial intelligence; length of stay; machine learning; prediction; syncope.

Figure 1

Study Design. * Uncomplicated hypertension, Cardiac arrhythmias, Fluid and electrolyte disorders, Uncomplicated diabetes, Chronic pulmonary disease, Complicated hypertension, Hypothyroidism, Renal failure, Depression, Congestive heart failure, Complicated diabetes, Neurological disorders, Obesity, Valvular disease, Peripheral vascular disorders, Drug abuse, Alcohol abuse, Rheumatoid arthritis/collagen vascular disorder, Solid tumor without metastasis, Deficiency anemia, Coagulopathy, Pulmonary circulation disorder, Liver disease, Psychoses, Weight loss, Metastatic cancer, Lymphoma, Paralysis, Peptic ulcer disease excluding bleeding, AIDS/HIV and Blood loss anemia.

Figure 2

Receiver Operator Characteristics (ROC), corresponding AUC, and F1 score values are given for each of the five LoS prediction models: (a) ≤0 days (indicating ED discharge) versus >0 days, (b) ≤24 h versus >24 h, (c) ≤48 h versus >48 h, (d) ≤4 days versus >4 days, (e) ≤7 days versus >7 days.