Stetzuhn, MatthiasTigges, TimoPielmus, Alexandru GabrielSpies, ClaudiaMiddel, CharlotteKlum, MichaelZaunseder, SebastianOrglmeister, ReinholdFeldheiser, Aarne2022-08-102022-08-102022-07-06https://depositonce.tu-berlin.de/handle/11303/17329http://dx.doi.org/10.14279/depositonce-16110Compensated 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.en620 Ingenieurwissenschaften und zugeordnete Tätigkeitencompensated shockelectrical cardiometryhypovolaemialower body negative pressure chambermachine learningprediction modelArticle2022-08-031424-8220