Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia
| dc.contributor.author | Stetzuhn, Matthias | |
| dc.contributor.author | Tigges, Timo | |
| dc.contributor.author | Pielmus, Alexandru Gabriel | |
| dc.contributor.author | Spies, Claudia | |
| dc.contributor.author | Middel, Charlotte | |
| dc.contributor.author | Klum, Michael | |
| dc.contributor.author | Zaunseder, Sebastian | |
| dc.contributor.author | Orglmeister, Reinhold | |
| dc.contributor.author | Feldheiser, Aarne | |
| dc.date.accessioned | 2022-08-10T09:20:46Z | |
| dc.date.available | 2022-08-10T09:20:46Z | |
| dc.date.issued | 2022-07-06 | |
| dc.date.updated | 2022-08-03T16:27:51Z | |
| dc.description.abstract | 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. | en |
| dc.identifier.eissn | 1424-8220 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/17329 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-16110 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | compensated shock | en |
| dc.subject.other | electrical cardiometry | en |
| dc.subject.other | hypovolaemia | en |
| dc.subject.other | lower body negative pressure chamber | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | prediction model | en |
| dc.title | Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 5066 | en |
| dcterms.bibliographicCitation.doi | 10.3390/s22145066 | en |
| dcterms.bibliographicCitation.issue | 14 | en |
| dcterms.bibliographicCitation.journaltitle | Sensors | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 22 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Energie- und Automatisierungstechnik::FG Elektronik und medizinische Signalverarbeitung | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Elektronik und medizinische Signalverarbeitung | de |
| tub.affiliation.institute | Inst. Energie- und Automatisierungstechnik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |