PEEP Titration in a Computational Model of Dynamic Lung Recruitment

American Journal of Respiratory and Critical Care Medicine · 2025-05-01

Abstract Introduction: Ventilator-induced lung injury is associated with intratidal recruitment and derecruitment (R/D). Lung recruitment may be responsive to positive end-expiratory pressure (PEEP). However, intratidal R/D are pressure- and time-dependent phenomena. Bates et al. developed a mathematical model of R/D represented by a hysteretic process with a time constant [1]. In this study, we investigated the effect of PEEP on intratidal R/D during mechanical ventilation of the model with nonlinear lung compliance. Methods: The R/D model of Bates et al. [1] was coupled to a sigmoid pressure-volume (P-V) relationship for regional lung compliance [2] and a uniform distribution of critical opening pressures. 10,000 virtual patients were simulated with randomly sampled parameters for: 1) maximum critical opening pressure; 2) consistently-recruited lung fraction; 3) consistently-derecruited lung fraction; 4) respiratory system resistance; 5) R/D time constant; and 6) P-V sigmoid parameters. Mechanical ventilation was simulated for 60 breaths using a volume-controlled waveform, with 450 mL tidal volume, 15 min-1 rate, and 1:2 inspiratory:expiratory ratio. PEEP was varied from 0 to 20 cmH2O. Measured outcomes of the simulations were static compliance, end-expiratory recruitment, intratidal R/D, and intratidal overdistension (exceeding 90% of maximal inflation). Results: Across all virtual patients, intratidal R/D decreased with increasing PEEP, albeit with diminishing returns above 7 cmH2O (Figure). Overdistension increased rapidly at PEEP exceeding 12 cmH2O. For low PEEP, intratidal R/D was correlated with fast R/D time constants, small fractions of consistently-recruited lung, and large amounts of lung recruitable at low critical opening pressures. For high PEEP, intratidal R/D was correlated with fast R/D time constants and high maximum critical opening pressures. A subset of virtual patients exhibited local minima of intratidal R/D at PEEP equal to or slightly greater than that which maximized static compliance. Conclusions: This model of dynamic recruitment/derecruitment during ventilation highlights the sensitivity of optimal PEEP settings (i.e., balancing R/D and overdistension) to patient-specific distributions of lung recruitability and mechanics. Results are consistent with imaging studies of intratidal R/D and overdistension in injured lungs [3,4]. Furthermore, we demonstrate a mechanism whereby intratidal R/D increases with high driving pressure due to low compliance, resulting from either low end-expiratory recruitment or overdistension at high PEEP. References: [1] JHT Bates et al., Crit Care Explor 2(12):e0299, 2020. [2] JG Venegas et al., J Appl Physiol 84(1):389-395, 1998. [3] ARS Carvalho et al., Crit Care 11(4):R86, 2007. [4] AH Jonkman et al., Am J Resp Crit Care Med 208(1):25-38, 2023.

Dual-Sensor CPR Feedback Accounts for Mattress Deflection in Chest Compression Depth Measurements During Simulated CPR

Resuscitation · 2025-10-01

Performance of a Dual-Sensor Cardiopulmonary Resuscitation Feedback System in Estimating Compression Depth and Rate During Simulated Chest Compressions

Journal of Medical Devices · 2025-03-06 · 1 citations

Abstract This study evaluated the performance of a dual-sensor cardiopulmonary resuscitation (CPR) feedback system in estimating chest compression depth and rate over a range of depth and rate combinations during rigid and compliant surface conditions using a computer-controlled motion system to simulate chest compressions. Ten dual-sensor CPR pads were tested using a computer-controlled motion system which simulated chest compressions at target depths of 1.9, 3.8, 4.8, 6.4, and 8.9 cm and target rates of 60, 80, 100, 120, and 140 compressions per minute (cpm). A rigid surface was simulated by applying motion only to the anterior sensor, and a compliant surface was simulated by applying motion to both the anterior and posterior sensor, challenging the algorithm to calculate a net compression depth by subtracting motion of the posterior sensor. For all simulated compressions, including every rate and depth combination and for both rigid and compliant surface simulations, the mean (±sd) depth error was 0.05 (±0.08) cm and the rate error was −0.55 (±1.44) cpm. A dual-sensor CPR system accurately estimates compression depth within ±0.25 cm and compression rate within ±3 cpm over a wide range and combination of clinically relevant chest compression depths and rates during both rigid and compliant surface simulations. Use of a computer-controlled motion system provides a more direct assessment of accuracy than manual compressions performed on instrumented manikins.

Virtual patient model for evaluating automated inspired oxygen control

Computers in Biology and Medicine · 2025-06-20 · 3 citations

sensor bias. This study demonstrates the utility of large-scale virtual patient modeling for sampling wide ranges of physiologic parameters using a multifactorial approach. Sampled conditions may be rarely observed in clinical practice or underrepresented in clinical trials yet warrant careful consideration when evaluating safety and efficacy of autonomous medical device control. The potential impact of the virtual patient model and proposed study design is improved rigor in the evaluation of medical device safety and efficacy, achieved by using computational modeling to complement the shortcomings of clinical trials.

Abstract Sa601: Relationship between ETCO2 and Hemodynamics in Swine: An Exploratory Secondary Analysis

Circulation · 2024-11-12

Background: End-tidal carbon dioxide (ETCO2) has been proposed as a surrogate for cardiac output during cardiopulmonary resuscitation (CPR). Chest compressions alter the capnogram waveform, complicating the calculation of ETCO2 values. To explore the utility of ETCO2 as a surrogate marker, we have conducted a retrospective analysis of the relationship between local maxima in the capnogram and hemodynamic metrics in preclinical resuscitation studies. Methods: In eight domestic swine (~30 kg), the IVC blood flow, aortic pressure (AOP), right atrial pressure (RAP), and capnogram were measured during resuscitation. Coronary perfusion pressure (CPP) is the difference between the AOP and the RAP during relaxation. ETCO2 is calculated using three methods: averaging the maximum CO2 level reached during each chest compression, selecting the global CO2 maximum measured during each breath, and identifying the maximum CO2 level from the last compression for each breath. The data are modeled using multivariate linear regression as follows: ETCO2= IVC + CPP + CC Depth + CC rate + Epoch # + Animal # Results: Table 1 shows R-squared values relating intra-compression CO2 maxima to hemodynamic measures and compression characteristics. The maximum CO2 value in a breath shows stronger correlations with these parameters than the other CO2 measurements. Table 2 presents regression coefficients for the model using this measure. CC Depth and CPP have coefficients of 3.67 and 2.05 respectively, indicating significant positive influences, while IVC has a coefficient of 0.84, indicating a weaker positive impact compared to CC Depth and CPP. Table 1. Method for Calculating the ETCO2 Calculating ETCO2 R-Squared Global CO2 Maximum per breath 0.67 Average of CO2 Maxima 0.64 CO2 Maximum of Last Compression 0.36 Table 2. Variable Coefficients Outcomes Regression Coefficients CPP 2.05 IVC 0.84 CC Depth 3.67 CC Rate -0.02 Epoch Number -0.57 Conclusions: The experimental results show that the global CO2 maximum per breath has the closest relationship to the hemodynamics in this experiment. The analysis suggests that increasing CC Depth, CPP, and IVC flow also increases the calculated ETCO2.

Low respiratory quotient correlates with high mortality in patients undergoing mechanical ventilation

The American Journal of Emergency Medicine · 2024-01-11 · 1 citations

Virtual Clinical Trial of Automated Inspired Oxygen Titration to Prevent Desaturation During Sudden Pathophysiological Disturbances

2024-04-30

Recurrence of ventricular fibrillation in out‐of‐hospital cardiac arrest: Clinical evidence and underlying ionic mechanisms

The Journal of Physiology · 2024-04-25 · 9 citations

Defibrillation remains the optimal therapy for terminating ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OHCA) patients, with reported shock success rates of ∼90%. A key persistent challenge, however, is the high rate of VF recurrence (∼50-80%) seen during post-shock cardiopulmonary resuscitation (CPR). Studies have shown that the incidence and time spent in recurrent VF are negatively associated with neurologically-intact survival. Recurrent VF also results in the administration of extra shocks at escalating energy levels, which can cause cardiac dysfunction. Unfortunately, the mechanisms underlying recurrent VF remain poorly understood. In particular, the role of chest-compressions (CC) administered during CPR in mediating recurrent VF remains controversial. In this review, we first summarize the available clinical evidence for refibrillation occurring during CPR in OHCA patients, including the postulated contribution of CC and non-CC related pathways. Next, we examine experimental studies highlighting how CC can re-induce VF via direct mechano-electric feedback. We postulate the ionic mechanisms involved by comparison with similar phenomena seen in commotio cordis. Subsequently, the hypothesized contribution of partial cardiac reperfusion (either as a result of CC or CC independent organized rhythm) in re-initiating VF in a globally ischaemic heart is examined. An overview of the proposed ionic mechanisms contributing to VF recurrence in OHCA during CPR from a cellular level to the whole heart is outlined. Possible therapeutic implications of the proposed mechanistic theories for VF recurrence in OHCA are briefly discussed.

348 The effect of chest compression location on cerebral oxygenation during cardiac arrest in swine

Resuscitation · 2023-10-30

1604: CARDIAC POWER IS A PREDICTOR OF CEREBRAL OXYGENATION DURING HEMORRHAGE IN SWINE

Critical Care Medicine · 2023-12-14

Introduction: Cardiac power is a strong predictor of mortality in a variety of critical conditions. Measurements of cerebral oxygenation (rSO2) may be a useful non-invasive monitoring tool. However, the physiology underlying changes in rSO2 during critical states such as hemorrhage is not completely understood. This analysis examined the effect of blood pressure, flow, and cardiac power on rSO2. Methods: Eight swine underwent a pressure-targeted hemorrhage protocol. Animals were instrumented with ultrasonic flow probes on a carotid and pulmonary artery and a jugular vein. Invasive pressure catheters were placed in the aorta and right atrium. rSO2 was measured using a tissue oximeter. Arterial blood was removed at a rate of 20 mL/min to a target diastolic pressure of 35 mmHg. Baseline airway pressure of 15 cm H2O was applied up to three times during the hemorrhage to introduce transient periods of decreased cardiac output. The aortic pressure waveform was used to identify individual heartbeats. Blood pressures and flows were measured for each heartbeat in mmHg and ml/min, respectively. Cardiac power was calculated as the product of blood pressure and flow. Cardiac power, peak systolic aortic pressure, and carotid blood flow were entered into univariate regression models to predict rSO2. Results: A total of 53,314 heartbeats were identified in the 8 animals. Mean systolic aortic pressure at baseline was 85±12 mmHg and reduced to 44±11 mmHg later into the hemorrhage. Baseline mean cardiac power was 0.15±0.08 W and reduced to 0.04±0.03 W, 40±7 min from start of bleed. Cardiac power, peak systolic aortic pressure, and carotid blood flow were all predictors of rSO2 [p< 0.05]. Cardiac power was the strongest predictor, explaining 85% of the variance in the rSO2 data, with peak systolic aortic pressure explaining 77%, and carotid blood flow explaining 83%. Conclusions: Cardiac power, peak systolic aortic pressure, and carotid blood flow are all significant predictors of rSO2 values with cardiac power explaining slightly more of the variance in rSO2 data. This highlights that composite measures of hemodynamics including both pressure and flow are better correlates of rSO2 than pressure and flow independently in this animal model of hemorrhagic shock.