ArticleIEEE transactions on bio-medical engineering2021
Physiology-Informed Real-Time Mean Arterial Blood Pressure Learning and Prediction for Septic Patients Receiving Norepinephrine.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Machine learning for predicting acute hypotension: A systematic review.Frontiers in cardiovascular medicine · 2022Pooled it
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Personalized pulse wave propagation modeling to improve vasopressor dosing management in patients with severe traumatic brain injury.PLoS computational biology · 2025Article
- Mixed-Weight Neural Bagging for Detecting mIEEE transactions on bio-medical engineering · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveSeptic shock is a life-threatening manifestation of infection with a mortality of 20-50% [1]. A catecholamine vasopressor, norepinephrine (NE), is widely used to treat septic shock primarily by increasing blood pressure. For this reason, future blood pressure knowledge is invaluable for properly controlling NE infusion rates in septic patients. However, recent machine learning and data-driven methods often treat the physiological effects of NE as a black box. In this paper, a real-time, physiology-informed human mean arterial blood pressure model for septic shock patients undergoing NE infusion is studied.
methodsOur methods combine learning theory, adaptive filter theory, and physiology. We learn least mean square adaptive filters to predict three physiological parameters (heart rate, pulse pressure, and the product of total arterial compliance and arterial resistance) from previous data and previous NE infusion rate. These predictions are combined according to a physiology model to predict future mean arterial blood pressure.
resultsOur model successfully forecasts mean arterial blood pressure on 30 septic patients from two databases. Specifically, we predict mean arterial blood pressure 3.33 minutes to 20 minutes into the future with a root mean square error from 3.56 mmHg to 6.22 mmHg. Additionally, we compare the computational cost of different models and discover a correlation between learned NE response models and a patient's SOFA score.
conclusionOur approach advances our capability to predict the effects of changing NE infusion rates in septic patients. SIGNIFICANCE: More accurately predicted MAP can lessen clinicians' workload and reduce error in NE titration.
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