Academic Journal
Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning
| العنوان: | Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning |
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| المؤلفون: | Yeongho Choi, Ki Hong Kim, Yoonjic Kim, Dong Hyun Choi, Yoon Ha Joo, Sae Won Choi, Kyoung Jun Song, Sang Do Shin |
| المصدر: | Hong Kong Journal of Emergency Medicine, Vol 31, Iss 5, Pp 233-241 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Surgery LCC:Medical emergencies. Critical care. Intensive care. First aid |
| مصطلحات موضوعية: | deep learning, emergency department, forecasting, hypotension, vasoconstrictor agents, Surgery, RD1-811, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9 |
| الوصف: | 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 |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2309-5407 1024-9079 |
| Relation: | https://doaj.org/toc/1024-9079; https://doaj.org/toc/2309-5407 |
| DOI: | 10.1002/hkj2.12039 |
| URL الوصول: | https://doaj.org/article/1bdb3433c18b426ba933b6debe251351 |
| رقم الانضمام: | edsdoj.1bdb3433c18b426ba933b6debe251351 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23095407 10249079 |
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| DOI: | 10.1002/hkj2.12039 |