VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

Review Anesthesiology

Cardiac output estimation using pulse wave analysis-physiology, algorithms, and technologies: a narrative review

Bernd Saugel et al.

Summary: Pulse wave analysis allows estimation of cardiac output based on continuous analysis of arterial blood pressure waveform. Different algorithms and monitoring systems are available based on the complexity of signals and calibration methods used.

BRITISH JOURNAL OF ANAESTHESIA (2021)

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Article Anesthesiology

Deep learning models for the prediction of intraoperative hypotension

Solam Lee et al.

Summary: The study demonstrated that deep learning algorithms can effectively predict hypotensive events in real-time, with better performance when utilizing a combination of biosignals compared to single signals. The findings confirm the potential application of deep learning models in surgeries.

BRITISH JOURNAL OF ANAESTHESIA (2021)

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Article Computer Science, Information Systems

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

Santiago Romero-Brufau et al.

Summary: The study aimed to develop an accurate prediction model for general care inpatient deterioration, utilizing feature engineering and machine learning techniques to create the Mayo Clinic Early Warning Score (MC-EWS) which demonstrated excellent discrimination in both internal and external validation datasets, with lower alert rates.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2021)

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Editorial Material Anesthesiology

VitalDB: fostering collaboration in anaesthesia research

Simon T. Vistisen et al.

BRITISH JOURNAL OF ANAESTHESIA (2021)

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Article Medical Informatics

Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

Hyun-Lim Yang et al.

Summary: An open-source algorithm developed in this study using convolutional neural network and transfer learning technique showed better performance in estimating cardiac output compared to commercial APCO devices. The deep learning model had significantly smaller absolute errors and may be helpful in optimizing patient care for high-risk individuals.

JMIR MEDICAL INFORMATICS (2021)

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Editorial Material Anesthesiology