Endovascular Perfusion Augmentation After Resuscitative Endovascular Balloon Occlusion of the Aorta Improves Renal Perfusion and Decreases Vasopressors

Patel, Nilesh; Gaffley, Michaela; Leblanc, Mathias J.R.; Lane, Magan R.; Hoareau, Guillaume L.; Johnson, Michael; Jordan, James; Neff, Lucas P.; Williams, Timothy K.

doi:10.1016/j.jss.2022.06.067

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Ischemia-reperfusion injury (IRI) involved controlled hemorrhage followed by 30 min of complete aortic occlusion and then restoration of flow to the lower body 21 . Ischemia-reperfusion injury followed by intermittent occlusion of the supraceliac aorta (EPACC) was performed as previously reported 22 . The HEM and IRI shock models, developed specifically for our MLFRA experiments, were subdivided into hypovolemic, euvolemic, and hypervolemic phases corresponding to pure blood loss, transfusion of the shed blood, and transfusion of an additional 25% blood volume from a donor animal, respectively.…”

Section: Cohort Descriptionmentioning

confidence: 99%

“…EPACC animals were treated with weight-based boluses of VetivexTM pHyLyteTM solution (5 ml/kg) each delivered over 2 min (Supplementary Fig. 1), with titration of both norepinephrine infusion and/or intra-aortic balloon volume determined by a resuscitation algorithm targeting a MAP > 60 and CVP ≥ 5 22 . Boluses from EPACC animals were only included from time periods where both the norepinephrine infusion rate and intra-aortic balloon volume were held constant such that the only factor affecting hemodynamics was the infusion of fluid.…”

Section: Cohort Descriptionmentioning

confidence: 99%

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Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Gupta,

Basu,

Williams

et al. 2024

Sci Rep

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Fluid bolus therapy (FBT) is fundamental to the management of circulatory shock in critical care but balancing the benefits and toxicities of FBT has proven challenging in individual patients. Improved predictors of the hemodynamic response to a fluid bolus, commonly referred to as a fluid challenge, are needed to limit non-beneficial fluid administration and to enable automated clinical decision support and patient-specific precision critical care management. In this study we retrospectively analyzed data from 394 fluid boluses from 58 pigs subjected to either hemorrhagic or distributive shock. All animals had continuous blood pressure and cardiac output monitored throughout the study. Using this data, we developed a machine learning (ML) model to predict the hemodynamic response to a fluid challenge using only arterial blood pressure waveform data as the input. A Random Forest binary classifier referred to as the ML fluid responsiveness algorithm (MLFRA) was trained to detect fluid responsiveness (FR), defined as a ≥ 15% change in cardiac stroke volume after a fluid challenge. We then compared its performance to pulse pressure variation, a commonly used metric of FR. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), confusion matrix metrics, and calibration curves plotting predicted probabilities against observed outcomes. Across multiple train/test splits and feature selection methods designed to assess performance in the setting of small sample size conditions typical of large animal experiments, the MLFRA achieved an average AUROC, recall (sensitivity), specificity, and precision of 0.82, 0.86, 0.62. and 0.76, respectively. In the same datasets, pulse pressure variation had an AUROC, recall, specificity, and precision of 0.73, 0.91, 0.49, and 0.71, respectively. The MLFRA was generally well-calibrated across its range of predicted probabilities and appeared to perform equally well across physiologic conditions. These results suggest that ML, using only inputs from arterial blood pressure monitoring, may substantially improve the accuracy of predicting FR compared to the use of pulse pressure variation. If generalizable, these methods may enable more effective, automated precision management of critically ill patients with circulatory shock.

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Section: Cohort Descriptionmentioning

confidence: 99%

Section: Cohort Descriptionmentioning

confidence: 99%

Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Gupta,

Basu,

Williams

et al. 2024

Sci Rep

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“…The protocols for experimental swine anesthesia and autologous blood donor extraction were the same as described previously. [20][21][22] All animals received a 1-L bolus of Plasma-Lyte A (Baxter Healthcare Corporation, Deerfield, IL) and up to an additional 1-L bolus if aortic flow was <30 mL/kg/min. Vascular access sites were surgically exposed.…”

Section: Animal Preparationmentioning

confidence: 99%

Automated partial resuscitative endovascular balloon occlusion of the aorta reduces blood loss and hypotension in a highly lethal porcine liver injury model

Cambronero

Sanin

Patel

et al. 2023

J Trauma Acute Care Surg

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BACKGROUND Partial and intermittent resuscitative endovascular balloon occlusion of the aorta (pREBOA and iREBOA, respectively) are lifesaving techniques designed to extend therapeutic duration, mitigate ischemia, and bridge patients to definitive hemorrhage control. We hypothesized that automated pREBOA balloon titration compared with automated iREBOA would reduce blood loss and hypotensive episodes over a 90-minute intervention phase compared with iREBOA in an uncontrolled liver hemorrhage swine model. METHODS Twenty-four pigs underwent an uncontrolled hemorrhage by liver transection and were randomized to automated pREBOA (n = 8), iREBOA (n = 8), or control (n = 8). Once hemorrhagic shock criteria were met, controls had the REBOA catheter removed and received transfusions only for hypotension. The REBOA groups received 90 minutes of either iREBOA or pREBOA therapy. Surgical hemostasis was obtained, hemorrhage volume was quantified, and animals were transfused to euvolemia and then underwent 1.5 hours of automated critical care. RESULTS The control group had significantly higher mortality rate (5 of 8) compared with no deaths in both REBOA groups, demonstrating that the liver injury is highly lethal (p = 0.03). During the intervention phase, animals in the iREBOA group spent a greater proportion of time in hypotension than the pREBOA group (20.7% [16.2–24.8%] vs. 0.76% [0.43–1.14%]; p < 0.001). The iREBOA group required significantly more transfusions than pREBOA (21.0 [20.0–24.9] mL/kg vs. 12.1 [9.5–13.9] mL/kg; p = 0.01). At surgical hemostasis, iREBOA had significantly higher hemorrhage volumes compared with pREBOA (39.2 [29.7–44.95] mL/kg vs. 24.7 [21.6–30.8] mL/kg; p = 0.04). CONCLUSION Partial REBOA animals spent significantly less time at hypotension and had decreased transfusions and blood loss. Both pREBOA and iREBOA prevented immediate death compared with controls. Further refinement of automated pREBOA is necessary, and controller algorithms may serve as vital control inputs for automated transfusion. LEVEL OF EVIDENCE Therapeutic/Care Management; Level III.

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“…Many have been described for various therapeutic interventions, such as burn resuscitation and blood pressure control under anesthesia (22)(23)(24)(25)(26)(27)(28)(29)(30). Our team continues to refine a closed-loop heuristicbased critical care algorithm, incorporating multiple hemodynamic and physiologic parameters to target resuscitation strategies in a highly reproducible distributive shock model (31)(32)(33)(34). This platform, called Precision Automated Critical Care Management (PACC-MAN), cohesively modulates vasopressor titration and crystalloid administration in response to real-time physiologic data and predetermined hemodynamic triggers.…”

Section: Introductionmentioning

confidence: 99%

Man Versus Machine: Provider Directed Versus Precision Automated Critical Care Management in a Porcine Model of Distributive Shock

Sanin,

Cambronero,

Wood

et al. 2024

Shock

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BACKGROUND Critical care management of shock is a labor-intensive process. Precision automated critical care management (PACC-MAN) is an automated closed-loop system incorporating physiologic and hemodynamic inputs to deliver interventions while avoiding excessive fluid or vasopressor administration. To understand PACC-MAN efficacy, we compared PACC-MAN to provider-directed management (PDM). We hypothesized that PACC-MAN would achieve equivalent resuscitation outcomes to PDM while maintaining normotension with lower fluid and vasopressor requirements. METHODS Twelve swine underwent 30% controlled hemorrhage over 30 minutes, followed by 45 minutes of aortic occlusion to generate a vasoplegic shock state, transfusion to euvolemia, and randomization to PACC-MAN or PDM for 4.25 hours. Primary outcomes were total crystalloid volume, vasopressor administration, total time spent at hypotension (MAP <60 mmHg), and total number of interventions. RESULTS Weight-based fluid volumes were similar between PACC-MAN and PDM, median and IQR are reported (73.1 mL/kg [59.0-78.7] vs. 87.1 mL/kg [79.4-91.8] p = 0.07). There was no statistical difference in cumulative norepinephrine (PACC-MAN: 33.4 mcg/kg [27.1-44.6] vs. PDM: 7.5 [3.3-24.2] mcg/kg, p = 0.09). The median percentage of time spent at hypotension was equivalent (PACC-MAN: 6.2% [3.6-7.4] and PDM: 3.1% [1.3-6.6], p = 0.23). Urine outputs were similar between PACC-MAN and PDM (14.0 mL/kg vs. 21.5 mL/kg, p = 0.13). CONCLUSION Automated resuscitation achieves equivalent resuscitation outcomes to direct human intervention in this shock model. This study provides the first translational experience with the PACC-MAN system versus PDM.

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Endovascular Perfusion Augmentation After Resuscitative Endovascular Balloon Occlusion of the Aorta Improves Renal Perfusion and Decreases Vasopressors

Cited by 6 publications

References 15 publications

Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Improving the precision of shock resuscitation by predicting fluid responsiveness with machine learning and arterial blood pressure waveform data

Automated partial resuscitative endovascular balloon occlusion of the aorta reduces blood loss and hypotension in a highly lethal porcine liver injury model

Man Versus Machine: Provider Directed Versus Precision Automated Critical Care Management in a Porcine Model of Distributive Shock

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