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Hypotension

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Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we ap...

The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients.

Artificial intelligence in medicine
Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learnin...

Machine learning model to predict hypotension after starting continuous renal replacement therapy.

Scientific reports
Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficult to predict because several factors have interactive and complex effects on the risk. The present ...

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BMC nephrology
BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

Scientific reports
In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of ...

Predicting intraoperative hypotension using deep learning with waveforms of arterial blood pressure, electroencephalogram, and electrocardiogram: Retrospective study.

PloS one
To develop deep learning models for predicting Interoperative hypotension (IOH) using waveforms from arterial blood pressure (ABP), electrocardiogram (ECG), and electroencephalogram (EEG), and to determine whether combination ABP with EEG or CG impro...

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review.

Oxidative medicine and cellular longevity
Blood pressure (BP) is a basic determinant for organ blood flow supply. Insufficient blood supply will cause tissue hypoxia, provoke cellular oxidative stress, and to some extent lead to organ injury. Perioperative BP is labile and dynamic, and intra...

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

IEEE journal of biomedical and health informatics
Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuo...

Effect of pneumatic leg compression on post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy: a double-blind randomised controlled trial.

Anaesthesia
Post-induction hypotension is common and associated with postoperative complications. We hypothesised that pneumatic leg compression reduces post-induction hypotension in elderly patients undergoing robot-assisted laparoscopic prostatectomy. In this ...