دورية أكاديمية
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Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia.

التفاصيل البيبلوغرافية
العنوان: Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia.
المؤلفون: Stetzuhn M; Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany., Tigges T; Department of Electronics and Medical Signal Processing, Technical University, 10587 Berlin, Germany., Pielmus AG; Department of Electronics and Medical Signal Processing, Technical University, 10587 Berlin, Germany., Spies C; Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany., Middel C; Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany., Klum M; Department of Electronics and Medical Signal Processing, Technical University, 10587 Berlin, Germany., Zaunseder S; Faculty of Information Technology, Fachhochschule Dortmund-University of Applied Sciences and Arts, 44139 Dortmund, Germany., Orglmeister R; Department of Electronics and Medical Signal Processing, Technical University, 10587 Berlin, Germany., Feldheiser A; Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.; Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, Evang. Kliniken Essen-Mitte, Huyssens-Stiftung/Knappschaft, 45136 Essen, Germany.
المصدر: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Jul 06; Vol. 22 (14). Date of Electronic Publication: 2022 Jul 06.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI, c2000-
مواضيع طبية MeSH: Hypovolemia*/diagnosis , Lower Body Negative Pressure*/adverse effects, Algorithms ; Humans ; Machine Learning ; Male ; Stroke Volume/physiology
مستخلص: Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBNP) chamber. A machine-learning algorithm based on variables of EC was designed. During LBNP, SVI-TTE declined consecutively, whereas the vital signs (arterial pressures and heart rate) remained within normal ranges. Compared to heart rate (AUC: 0.83 (95% CI: 0.73-0.87)) and systolic arterial pressure (AUC: 0.82 (95% CI: 0.74-0.85)), a model integrating EC variables (AUC: 0.91 (0.83-0.94)) showed a superior ability to predict a decrease in SVI-TTE ≥ 20% ( p = 0.013 compared to heart rate, and p = 0.002 compared to systolic blood pressure). Simulated central hypovolaemia was related to a substantial decline in SVI-TTE but only minor changes in vital signs. A model of EC variables based on machine-learning algorithms showed high predictive power to detect a relevant decrease in SVI and may provide an automated, non-invasive method to indicate hypovolaemia and compensated shock.
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فهرسة مساهمة: Keywords: compensated shock; electrical cardiometry; hypovolaemia; lower body negative pressure chamber; machine learning; prediction model
تواريخ الأحداث: Date Created: 20220727 Date Completed: 20220728 Latest Revision: 20220731
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9316072
DOI: 10.3390/s22145066
PMID: 35890746
قاعدة البيانات: MEDLINE
الوصف
تدمد:1424-8220
DOI:10.3390/s22145066