Physician-Directed Versus Computerized Closed-Loop Control of Blood Pressure Using Phenylephrine in a Swine Model.

BACKGROUND: Vasopressors provide a rapid and effective approach to correct hypotension in the perioperative setting. Our group developed a closed-loop control (CLC) system that titrates phenylephrine (PHP) based on the mean arterial pressure (MAP) during general anesthesia. As a means of evaluating system competence, we compared the performance of the automated CLC with physicians. We hypothesized that our CLC algorithm more effectively maintains blood pressure at a specified target with less blood pressure variability and reduces the dose of PHP required. METHODS: In a crossover study design, 6 swine under general anesthesia were subjected to a normovolemic hypotensive challenge induced by sodium nitroprusside. The physicians (MD) manually changed the PHP infusion rate, and the CLC system performed this task autonomously, adjusted every 3 seconds to achieve a predetermined MAP. RESULTS: The CLC maintained MAP within 5 mm Hg of the target for (mean ± standard deviation) 93.5% ± 3.9% of the time versus 72.4% ± 26.8% for the MD treatment (P = .054). The mean (standard deviation) percentage of time that the CLC and MD interventions were above target range was 2.1% ± 3.3% and 25.8% ± 27.4% (P = .06), respectively. Control statistics, performance error, median performance error, and median absolute performance error were not different between CLC and MD interventions. PHP infusion rate adjustments by the physician were performed 12 to 80 times in individual studies over a 60-minute period. The total dose of PHP used was not different between the 2 interventions. CONCLUSIONS: The CLC system performed as well as an anesthesiologist totally focused on MAP control by infusing PHP. Computerized CLC infusion of PHP provided tight blood pressure control under conditions of experimental vasodilation.

Automated closed-loop resuscitation of multiple hemorrhages: a comparison between fuzzy logic and decision table controllers in a sheep model.

BACKGROUND: Hemorrhagic shock is the leading cause of trauma-related death in the military setting. Definitive surgical treatment of a combat casualty can be delayed and life-saving fluid resuscitation might be necessary in the field. Therefore, improved resuscitation strategies are critically needed for prolonged field and en route care. We developed an automated closed-loop control system capable of titrating fluid infusion to a target endpoint. We used the system to compare the performance of a decision table algorithm (DT) and a fuzzy logic controller (FL) to rescue and maintain the mean arterial pressure (MAP) at a target level during hemorrhages. Fuzzy logic empowered the control algorithm to emulate human expertise. We hypothesized that the FL controller would be more effective and more efficient than the DT algorithm by responding in a more rigid, structured way. METHODS: Ten conscious sheep were submitted to a hemorrhagic protocol of 25 ml/kg over three separate bleeds. Automated resuscitation with lactated Ringer's was initiated 30 min after the first hemorrhage started. The endpoint target was MAP. Group differences were assessed by two-tailed t test and alpha of 0.05. RESULTS: Both groups maintained MAP at similar levels throughout the study. However, the DT group required significantly more fluid than the FL group, 1745 ± 552 ml (42 ± 11 ml/kg) versus 978 ± 397 ml (26 ± 11 ml/kg), respectively (p = 0.03). CONCLUSION: The FL controller was more efficient than the DT algorithm and may provide a means to reduce fluid loading. Effectiveness was not different between the two strategies. Automated closed-loop resuscitation can restore and maintain blood pressure in a multi-hemorrhage model of shock.

Closed-Loop Control of FiO2 Rapidly Identifies Need For Rescue Ventilation and Reduces ARDS Severity in a Conscious Sheep Model of Burn and Smoke Inhalation Injury.

Pulmonary injury can be characterized by an increased need for fraction of inspired oxygen or inspired oxygen percentage (FiO2) to maintain arterial blood saturation of oxygenation (SaO2). We tested a smart oxygenation system (SOS) that uses the activity of a closed-loop control FiO2 algorithm (CLC-FiO2) to rapidly assess acute respiratory distress syndrome (ARDS) severity so that rescue ventilation (RscVent) can be initiated earlier. After baseline data, a pulse-oximeter (noninvasive saturation of peripheral oxygenation [SpO2]) was placed. Sheep were then subjected to burn and smoke inhalation injury and followed for 48 h. Initially, sheep were spontaneously ventilating and then randomized to standard of care (SOC) (n = 6), in which RscVent began when partial pressure of oxygen (PaO2) < 90 mmHg or FiO2 < 0.6, versus SOS (n = 7), software that incorporates and displays SpO2, CLC-FiO2, and SpO2/CLC-FiO2 ratio, at which RscVent was initiated when ratio threshold < 250. RscVent was achieved using a G5 Hamilton ventilator (Bonaduz, Switzerland) with adaptive pressure ventilation and adaptive support ventilation modes for SOC and SOS, respectively. OUTCOMES: the time difference from when SpO2/FiO2 < 250 to RscVent initiation was 4.7 ±â€Š0.6 h and 0.2 ±â€Š0.1 h, SOC and SOS, respectively (P < 0.001). Oxygen responsiveness after RscVent, defined as SpO2/FiO2 > 250 occurred in 4/7, SOS and 0/7, SOC. At 48 h the SpO2/FiO2 ratio was 104 ±â€Š5 in SOC versus 228 ±â€Š59 in SOS (P = 0.036). Ventilatory compliance and peak airway pressures were significantly improved with SOS versus SOC (P < 0.001). Data suggest that SOS software, e.g. SpO2/CLC-FiO2 ratio, after experimental ARDS can provide a novel continuous index of pulmonary function that is apparent before other clinical symptoms. Earlier initiation of RscVent translates into improved oxygenation (reduces ARDS severity) and ventilation.

Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approaches.

BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation. METHODS: Hemodynamic parameters (BPs, HR) were derived from 100-Hz arterial BP waveforms of 10 sheep records, 3 hours to 4 hours long. Two models (mean arterial pressure, Windkessel) were then applied and merged to estimate COVS. ML was used to develop a rule for identifying when models required calibration. All records contained 100-Hz recording of pulmonary arterial blood flow using Doppler transit time (COFP). COFP and COVS were analyzed using equivalence tests and Bland-Altman analysis, as well as waveform and concordance plots. RESULTS: Baseline COFP varied from 3.0 L/min to 5.4 L/min, while posthemorrhage COFP varied from 1.0 L/min to 1.8 L/min. A total of 315,196 pairs of data were obtained. Equivalence tests for individual records showed that COVS was statistically equivalent to COFP (p < 0.05). Smaller equivalence thresholds (<0.3 L/min) indicated an overall high COFP accuracy. The agreement between COFP and COVS was -0.13 (0.69) L/min (Bland-Altman). In an exclusion zone of 12%, trending analysis found a 92% concordance between 5-minute changes in COFP and COVS. CONCLUSION: This study showed that CO can be reliably estimated using BPs and HR from the arterial BP waveform in combination with ML. A next step will be to test this approach using noninvasive BPs and HR.

Is heart-rate complexity a surrogate measure of cardiac output before, during, and after hemorrhage in a conscious sheep model of multiple hemorrhages and resuscitation?

BACKGROUND: Despite its medical utility, continuous cardiac output (CO) monitoring remains a practical challenge on the battlefield and in the prehospital environment. Measuring a CO surrogate, perhaps heart-rate complexity (HRC), might be a viable solution when no direct monitoring of CO is available. Changes in HRC observed before and during hemorrhagic shock may be able to track the simultaneous changes in CO. The goal of this study was to test whether HRC is a surrogate measure of CO before, during, and after hemorrhage in a conscious sheep model of multiple hemorrhages and resuscitation. METHODS: HRC was derived from 100-Hz electrocardiograms of 10 sheep records, 3 hours to 4 hours long, using the method of sample entropy. A real-time detection algorithm was used to detect the R-R interval sequences for HRC calculations. All records contained 100-Hz recordings of pulmonary arterial blood flow using Doppler transit time (criterion standard CO). Gold CO and estimated HRC values were analyzed using overlaid time-synchronized waveform plots as well as Bland-Altman, regression, and four-quadrant analysis. RESULTS: Baseline CO varied from 3.0 L/min to 5.4 L/min, while posthemorrhage CO varied from 1.0 L/min to 1.8 L/min. Importantly, overlaid plots demonstrated an overall high similarity between CO and HRC waveforms before and during hemorrhage, but not during resuscitation. When the electrocardiogram quality was high, the correlation between CO and HRC within the first 45 minutes was greater than 0.75 (p < 0.0001; maximum r, 0.972). Scatter plots also depicted high linearity before and during hemorrhage. Four-quadrant analysis showed that instantaneous changes between consecutive beat-to-beat HRC measurements followed CO measurements (100% concordance rate), while 5-minute time points yielded a 76.19% concordance rate. CONCLUSION: HRC has potential utility as a noninvasive tool for assessing the response of CO to life-threatening injuries such as hemorrhagic shock. However, further investigation and other animal models or human studies are needed.