Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning

Abstract

Abstract Background Shock is a life‐threatening condition that is associated with high mortality and morbidity. Therefore, the timely identification and management of this condition are important. We aimed to develop a prediction model for vasopressor use based on concise serial arterial blood pressure data. Methods We collected continuous arterial blood pressure from patients admitted to the emergency department (ED) resuscitation room. Patients with an initial systolic blood pressure lower than 90 mmHg were included in the study. We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. Discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision–recall curve (AUPRC). Results A total of 120 patients were enrolled in the study. The CNN and LSTM models yielded AUROCs ranging from 0.731 to 0.834 for predicting the need for vasopressor infusion within different time frames (30 min, 1 h, and 6 h). LSTM outperformed the CNN in terms of predicting vasopressor infusion within 30 min and 1 h (p value < 0.05). The AUPRC values ranged from 0.200 to 0.252, and sensitivity–specificity analyses indicated that the models have potential for clinical applications. Conclusion This study used serial arterial blood pressure data to construct a promising prediction model for the need for vasopressors in hypotensive ED patients. The simplicity and accuracy of the model provide valuable insights for developing a clinical decision support tool for resuscitation in emergency settings.

Keywords

• deep learning
• emergency department
• forecasting
• hypotension
• vasoconstrictor agents