BACKGROUND: Closed-loop systems have been designed to assist practitioners in maintaining stability of various physiologic variables in the clinical setting. In this context, we recently performed in silico testing of a novel closed-loop fluid management system that is designed for cardiac output and pulse pressure variation monitoring and optimization. The goal of the present study was to assess the effectiveness of this newly developed system in optimizing hemodynamic variables in an in vivo surgical setting. METHODS: Sixteen Yorkshire pigs underwent a 2-phase hemorrhage protocol and were resuscitated by either the Learning Intravenous Resuscitator closed-loop system or an anesthesiologist. Median hemodynamic values and variation of hemodynamics were compared between groups. RESULTS: Cardiac index (in liters per minute per square meter) and stroke volume index (in milliliters per square meter) were higher in the closed-loop group compared with the anesthesiologist group over the protocol (3.7 [3.4-4.1] vs 3.5 [3.2-3.9]; 95% Wald confidence interval, -0.5 to -0.23; P < 0.0005 and 40 [34-45] vs 36 [31-38]; 95% Wald confidence interval, -5.9 to -3.1; P < 0.0005, respectively). There was no significant difference in total fluid administration between the closed-loop and anesthesiologist groups (3685 [3230-4418] vs 3253 [2735-3926] mL; 95% confidence interval, -1651 to 431; P = 0.28). Closed-loop group animals also had lower coefficients of variance of cardiac index and stroke volume index during the protocol (11% [10%-16%] vs 22% [18%-23%]; confidence interval, 0.8%-12.3%; P = 0.02 and 11% [8%-16%] vs 17% [13%-21%]; confidence interval, 0.2%-11.4%; P = 0.04, respectively). CONCLUSION: This in vivo study building on previous simulation work demonstrates that the closed-loop fluid management system used in this experiment can perform fluid resuscitation during mild and severe hemorrhages and is able to maintain high cardiac output and stroke volume while reducing hemodynamic variability.
BACKGROUND: Closed-loop systems have been designed to assist practitioners in maintaining stability of various physiologic variables in the clinical setting. In this context, we recently performed in silico testing of a novel closed-loop fluid management system that is designed for cardiac output and pulse pressure variation monitoring and optimization. The goal of the present study was to assess the effectiveness of this newly developed system in optimizing hemodynamic variables in an in vivo surgical setting. METHODS: Sixteen Yorkshire pigs underwent a 2-phase hemorrhage protocol and were resuscitated by either the Learning Intravenous Resuscitator closed-loop system or an anesthesiologist. Median hemodynamic values and variation of hemodynamics were compared between groups. RESULTS: Cardiac index (in liters per minute per square meter) and stroke volume index (in milliliters per square meter) were higher in the closed-loop group compared with the anesthesiologist group over the protocol (3.7 [3.4-4.1] vs 3.5 [3.2-3.9]; 95% Wald confidence interval, -0.5 to -0.23; P < 0.0005 and 40 [34-45] vs 36 [31-38]; 95% Wald confidence interval, -5.9 to -3.1; P < 0.0005, respectively). There was no significant difference in total fluid administration between the closed-loop and anesthesiologist groups (3685 [3230-4418] vs 3253 [2735-3926] mL; 95% confidence interval, -1651 to 431; P = 0.28). Closed-loop group animals also had lower coefficients of variance of cardiac index and stroke volume index during the protocol (11% [10%-16%] vs 22% [18%-23%]; confidence interval, 0.8%-12.3%; P = 0.02 and 11% [8%-16%] vs 17% [13%-21%]; confidence interval, 0.2%-11.4%; P = 0.04, respectively). CONCLUSION: This in vivo study building on previous simulation work demonstrates that the closed-loop fluid management system used in this experiment can perform fluid resuscitation during mild and severe hemorrhages and is able to maintain high cardiac output and stroke volume while reducing hemodynamic variability.
Authors: Ramin Bighamian; Bahram Parvinian; Christopher G Scully; George Kramer; Jin-Oh Hahn Journal: Control Eng Pract Date: 2018-03-14 Impact factor: 3.475
Authors: Alexandre Joosten; Joseph Rinehart; Aurélie Bardaji; Philippe Van der Linden; Vincent Jame; Luc Van Obbergh; Brenton Alexander; Maxime Cannesson; Susana Vacas; Ngai Liu; Hichem Slama; Luc Barvais Journal: Anesthesiology Date: 2020-02 Impact factor: 7.892
Authors: Wilfried Klingert; Jörg Peter; Christian Thiel; Karolin Thiel; Wolfgang Rosenstiel; Kathrin Klingert; Christian Grasshoff; Alfred Königsrainer; Martin Schenk Journal: Intensive Care Med Exp Date: 2018-01-16