Evaluation of a Physiologic-Driven Closed-Loop Resuscitation Algorithm in an Animal Model of Hemorrhagic Shock.

OBJECTIVES: Appropriate resuscitation from hemorrhagic shock is critical to restore tissue perfusion and to avoid over-resuscitation. The objective of this study was to test the ability of a closed-loop diagnosis and resuscitation algorithm called resuscitation from shock using functional hemodynamic monitoring using invasive monitoring (ReFit1) and minimally invasive monitoring (ReFit2) to identify, treat, and stabilize a porcine model of severe hemorrhagic shock. DESIGN: We created a ReFit algorithm using dynamic hemodynamic parameters of pulse pressure variation (PPV), stroke volume variation (SVV), dynamic arterial elastance (Eadyn = PPV/SVV), driven by mean arterial pressure (MAP), mixed venous oxygen saturation, and heart rate targets to define the need for fluids, vasopressors, and inotropes. SETTING: University-based animal laboratory. SUBJECTS: Twenty-seven female pigs. INTERVENTIONS: Anesthetized, intubated, and ventilated (8 mL/kg) pigs were bled at 10 mL/min until a MAP of less than 40 mm Hg, held for 30 minutes, then resuscitated. The ReFit algorithm used the above dynamic parameters to drive computer-controlled infusion pumps to deliver blood, lactated Ringer's solution, norepinephrine, and in ReFit1 dobutamine. In four animals, after initial resuscitation from hemorrhagic shock, the ability of the ReFit1 algorithm to treat acute air embolism-induced pulmonary hypertension and right heart failure was also tested. MAIN RESULTS: In 10 ReFit1 and 17 ReFit2 animals, the time to stabilization from shock was not dissimilar to open controlled resuscitation performed by an expert physician (52 ± 12, 50 ± 13, and 60 ± 15 min, respectively) with similar amounts of fluids and norepinephrine needed. In four ReFit1 animals after initial stabilization, the algorithm successfully resuscitated the animals after inducing an acute air embolism right heart failure, with all animals recovering stability within 30 minutes. CONCLUSIONS: Our physiologically based functional hemodynamic monitoring-centered closed-loop resuscitation system can effectively diagnose and treat cardiovascular shock due to hemorrhage and air embolism.

Predictive value of dynamic arterial elastance for vasopressor withdrawal: a systematic review and meta-analysis.

BACKGROUND: Dynamic arterial elastance (Eadyn) has been investigated for its ability to predict hypotension during the weaning of vasopressors. Our study focused on assessing Eadyn's performance in the context of critically ill adult patients admitted to the intensive care unit, regardless of diagnosis. MAIN BODY: Our study was conducted in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analysis checklist. The protocol was registered in PROSPERO (CRD42023421462) on May 26, 2023. We included prospective observational studies from the MEDLINE and Embase databases through May 2023. Five studies involving 183 patients were included in the quantitative analysis. We extracted data related to patient clinical characteristics, and information about Eadyn measurement methods, results, and norepinephrine dose. Most patients (76%) were diagnosed with septic shock, while the remaining patients required norepinephrine for other reasons. The average pressure responsiveness rate was 36.20%. The synthesized results yielded an area under the curve of 0.85, with a sensitivity of 0.87 (95% CI 0.74-0.93), specificity of 0.76 (95% CI 0.68-0.83), and diagnostic odds ratio of 19.07 (95% CI 8.47-42.92). Subgroup analyses indicated no variations in the Eadyn based on norepinephrine dosage, the Eadyn measurement device, or the Eadyn diagnostic cutoff to predict cessation of vasopressor support. CONCLUSIONS: Eadyn, evaluated through subgroup analyses, demonstrated good predictive ability for the discontinuation of vasopressor support in critically ill patients.

Autonomous precision resuscitation during ground and air transport of an animal hemorrhagic shock model.

We tested the ability of a physiologically driven minimally invasive closed-loop algorithm, called Resuscitation based on Functional Hemodynamic Monitoring (ReFit), to stabilize for up to 3 h a porcine model of noncompressible hemorrhage induced by severe liver injury and do so during both ground and air transport. Twelve animals were resuscitated using ReFit to drive fluid and vasopressor infusion to a mean arterial pressure (MAP) > 60 mmHg and heart rate < 110 min-1 30 min after MAP < 40 mmHg following liver injury. ReFit was initially validated in 8 animals in the laboratory, then in 4 animals during air (23nm and 35nm) and ground (9 mi) to air (9.5nm and 83m) transport returning to the laboratory. The ReFit algorithm kept all animals stable for ~ 3 h. Thus, ReFit algorithm can diagnose and treat ongoing hemorrhagic shock independent to the site of care or during transport. These results have implications for treatment of critically ill patients in remote, austere and contested environments and during transport to a higher level of care.

Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.

Use of artificial intelligence in critical care: opportunities and obstacles.

BACKGROUND: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY: Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Early Physiologic Numerical and Waveform Characteristics of Simulated Hemorrhagic Events With Healthy Volunteers Donating Blood.

OBJECTIVES: Early signs of bleeding are often masked by the physiologic compensatory responses delaying its identification. We sought to describe early physiologic signatures of bleeding during the blood donation process. SETTING: Waveform-level vital sign data including electrocardiography, photoplethysmography (PPG), continuous noninvasive arterial pressure, and respiratory waveforms were collected before, during, and after bleeding. SUBJECTS: Fifty-five healthy volunteers visited blood donation center to donate whole blood. INTERVENTION: After obtaining the informed consent, 3 minutes of resting time was given to each subject. Then 3 minutes of orthostasis was done, followed by another 3 minutes of resting before the blood donation. After the completion of donating blood, another 3 minutes of postbleeding resting time, followed by 3 minutes of orthostasis period again. MEASUREMENTS AND MAIN RESULTS: From 55 subjects, waveform signals as well as numerical vital signs (heart rate [HR], respiratory rate, blood pressure) and clinical characteristics were collected, and data from 51 subjects were analyzable. Any adverse events (AEs; dizziness, lightheadedness, nausea) were documented. Statistical and physiologic features including HR variability (HRV) metrics and other waveform morphologic parameters were modeled. Feature trends for all participants across the study protocol were analyzed. No significant changes in HR, blood pressure, or estimated cardiac output were seen during bleeding. Both orthostatic challenges and bleeding significantly decreased time domain and high-frequency domain HRV, and PPG amplitude, whereas increasing PPG amplitude variation. During bleeding, time-domain HRV feature trends were most sensitive to the first 100 mL of blood loss, and incremental changes of different HRV parameters (from 300 mL of blood loss), as well as a PPG morphologic feature (from 400 mL of blood loss), were shown with statistical significance. The AE group (n = 6) showed decreased sample entropy compared with the non-AE group during postbleed orthostatic challenge (p = 0.003). No significant other trend differences were observed during bleeding between AE and non-AE groups. CONCLUSIONS: Various HRV-related features were changed during rapid bleeding seen within the first minute. Subjects with AE during postbleeding orthostasis showed decreased sample entropy. These findings could be leveraged toward earlier identification of donors at risk for AE, and more broadly building a data-driven hemorrhage model for the early treatment of critical bleeding.

Severity-Driven Trends in Mortality in a Large Regionalized Critical Care Transport Service.

OBJECTIVE: The epidemiology accompanying helicopter emergency medical services (HEMS) transport has evolved as agencies have matured and become integrated into regionalized health systems, as evidenced primarily by nationwide systems in Europe. System-level congruence between Europe and the United States, where HEMS is geographically fragmentary, is unclear. In this study, we provide a temporal, epidemiologic characterization of the largest standardized private, nonprofit HEMS system in the United States, STAT MedEvac. METHODS: We obtained comprehensive timing, procedure, and vital signs data from STAT MedEvac prehospital electronic patient care records for all adult patients transported to UPMC Health System hospitals in the period of January 2012 through October 2021. We linked these data with hospital electronic health records available through June 2018 to establish length of stay and vital status at discharge. RESULTS: We studied 90,960 transports and matched 62.8% (n = 57,128) to the electronic health record. The average patient age was 58.6 years ( 19 years), and most were male (57.9%). The majority of cases were interfacility transports (77.6%), and the most common general medical category was nontrauma (72.7%). Sixty-one percent of all patients received a prehospital intervention. Overall, hospital mortality was 15%, and the average hospital length of stay (LOS) was 8.8 days ( 10.0 days). Observed trends over time included increases in nontrauma transports, level of severity, and in-hospital mortality. In multivariable models, case severity and medical category correlated with the outcomes of mortality and LOS. CONCLUSION: In the largest standardized nonprofit HEMS system in the United States, patient mortality and hospital LOS increased over time, whereas the proportion of trauma patients and scene runs decreased.

Consistency of data reporting in fluid responsiveness studies in the critically ill setting: the CODEFIRE consensus from the Cardiovascular Dynamic section of the European Society of Intensive Care Medicine.

PURPOSE: To provide consensus recommendations regarding hemodynamic data reporting in studies investigating fluid responsiveness and fluid challenge (FC) use in the intensive care unit (ICU). METHODS: The Executive Committee of the European Society of Intensive Care Medicine (ESICM) commissioned and supervised the project. A panel of 18 international experts and a methodologist identified main domains and items from a systematic literature, plus 2 ancillary domains. A three-step Delphi process based on an iterative approach was used to obtain the final consensus. In the Delphi 1 and 2, the items were selected with strong (≥ 80% of votes) or week agreement (70-80% of votes), while the Delphi 3 generated recommended (≥ 90% of votes) or suggested (80-90% of votes) items (RI and SI, respectively). RESULTS: We identified 5 main domains initially including 117 items and the consensus finally resulted in 52 recommendations or suggestions: 18 RIs and 2 SIs statements were obtained for the domain "ICU admission", 11 RIs and 1 SI for the domain "mechanical ventilation", 5 RIs for the domain "reason for giving a FC", 8 RIs for the domain pre- and post-FC "hemodynamic data", and 7 RIs for the domain "pre-FC infused drugs". We had no consensus on the use of echocardiography, strong agreement regarding the volume (4 ml/kg) and the reference variable (cardiac output), while weak on administration rate (within 10 min) of FC in this setting. CONCLUSION: This consensus found 5 main domains and provided 52 recommendations for data reporting in studies investigating fluid responsiveness in ICU patients.

Discovering the Clinical Relevance of Heart-Lung Interactions.

In 1978, Dr. Pinsky's scientific career became firmly directed toward understanding the deeper meaning of heart-lung interactions. This would define his focus for the next 45 yr. At the time, he and colleagues studied the effects of changes in intrathoracic pressure on left ventricular performance in humans, documenting that the primary effect of large negative swings in intrathoracic pressure was to increase left ventricular transmural ejection pressure, and thus left ventricular afterload, selectively. They concluded that large intrathoracic pressure changes directly influence cardiac performance. This fundamental observation was followed by many additional observations in both highly invasive animal studies supported by less invasive clinical studies, which showed that intrathoracic pressure-induced changes in the gradients for venous return to the heart and left ventricular ejection from the heart disproportionately affected both right ventricular and left ventricular function. The direct clinical implications of these results form the rationale for use of continuous positive airway pressure as a primary treatment of acute cardiogenic pulmonary edema and immediate endotracheal intubation for acute upper airway obstruction. These findings subsequently led to the practical use of dynamic changes in left ventricular stroke volume and the associated arterial pulse pressure during positive-pressure ventilation to identify volume responsiveness and, thus, to personalize resuscitation efforts in the treatment of acute cardiovascular insufficiency.

Application of Cardiovascular Physiology to the Critically Ill Patient.

OBJECTIVES: To use the ventricular pressure-volume relationship and time-varying elastance model to provide a foundation for understanding cardiovascular physiology and pathophysiology, interpreting advanced hemodynamic monitoring, and for illustrating the physiologic basis and hemodynamic effects of therapeutic interventions. We will build on this foundation by using a cardiovascular simulator to illustrate the application of these principles in the care of patients with severe sepsis, cardiogenic shock, and acute mechanical circulatory support. DATA SOURCES: Publications relevant to the discussion of the time-varying elastance model, cardiogenic shock, and sepsis were retrieved from MEDLINE. Supporting evidence was also retrieved from MEDLINE when indicated. STUDY SELECTION, DATA EXTRACTION, AND SYNTHESIS: Data from relevant publications were reviewed and applied as indicated. CONCLUSIONS: The ventricular pressure-volume relationship and time-varying elastance model provide a foundation for understanding cardiovascular physiology and pathophysiology. We have built on this foundation by using a cardiovascular simulator to illustrate the application of these important principles and have demonstrated how complex pathophysiologic abnormalities alter clinical parameters used by the clinician at the bedside.

Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings.

BACKGROUND: Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care. METHODS: We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories. RESULTS: There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times. CONCLUSIONS: In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.

Fluids and Early Vasopressors in the Management of Septic Shock: Do We Have the Right Answers Yet?

Septic shock is a common condition associated with hypotension and organ dysfunction. It is associated with high mortality rates of up to 60% despite the best recommended resuscitation strategies in international guidelines. Patients with septic shock generally have a Mean Arterial Pressure below 65 mmHg and hypotension is the most important determinant of mortality among this group of patients. The extent and duration of hypotension are important. The two initial options that we have are 1) administration of intravenous (IV) fluids and 2) vasopressors, The current recommendation of the Surviving Sepsis Campaign guidelines to administer 30 ml/kg fluid cannot be applied to all patients. Complications of fluid over-resuscitation further delay organ recovery, prolong ICU and hospital length of stay, and increase mortality. The only reason for administering intravenous fluids in a patient with circulatory shock is to increase the mean systemic filling pressure in a patient who is volume-responsive, such that cardiac output also increases. The use of vasopressors seems to be a more appropriate strategy, the very early administration of vasopressors, preferably during the first hour after diagnosis of septic shock, may have a multimodal action and potential advantages, leading to lower morbidity and mortality in the management of septic patients. Vasopressor therapy should be initiated as soon as possible in patients with septic shock.

Heart-Lung Interactions.

The pulmonary and cardiovascular systems have profound effects on each other. Overall cardiac function is determined by heart rate, preload, contractility, and afterload. Changes in lung volume, intrathoracic pressure (ITP), and hypoxemia can simultaneously change all of these four hemodynamic determinants for both ventricles and can even lead to cardiovascular collapse. Intubation using sedation depresses vasomotor tone. Also, the interdependence between right and left ventricles can be affected by lung volume-induced changes in pulmonary vascular resistance and the rise in ITP. An increase in venous return due to negative ITP during spontaneous inspiration can shift the septum to the left and cause a decrease in left ventricle compliance. During positive pressure ventilation, the increase in ITP causes a decrease in venous return (preload), minimizing ventricular interdependence and will decrease left ventricle afterload augmenting cardiac output. Thus, positive pressure ventilation is beneficial in acute heart failure patients and detrimental in hypovolemic patients where it can cause a significant decrease in venous return and cardiac output. Recently, this phenomenon has been used to assess patient's volume responsiveness to fluid by measuring pulse pressure variation and stroke volume variation. Heart-lung interaction is very dynamic and changes in lung volume, ITP, and oxygen level can have various effects on the cardiovascular system depending on preexisting cardiovascular function and volume status. Heart failure and either hypo or hypervolemia predispose to greater effects of ventilation of cardiovascular function and gas exchange. This review is an overview of the basics of heart-lung interaction.

Cardiopulmonary interactions in left heart failure.

The primary impact of ventilation and ventilatory efforts on left ventricular (LV) function in left ventricular dysfunction relate to how changes in intrathoracic pressure (ITP) alter the pressure gradients for venous return into the chest and LV ejection out of the chest. Spontaneous inspiratory efforts by decreasing ITP increase both of these pressure gradients increasing venous blood flow and impeding LV ejection resulting in increased intrathoracic blood volume. In severe heart failure states when lung compliance is reduced, or airway resistance is increased these negative swings in ITP can be exacerbated leading to LV failure and acute cardiogenic pulmonary edema. By merely reversing these negative swings in ITP by the use of non-invasive continuous positive airway pressure (CPAP), these profoundly detrimental forces can be immediately reversed, and cardiovascular stability can be restored in moments. This forms the clinical rationale for the immediate use of CPAP for the treatment of acute cardiogenic pulmonary edema. Increasing ITP during positive pressure ventilation decreases the pressure gradients for venous return and LV ejection decreasing intrathoracic blood volume. In a hypovolemic patient even with LV dysfunction this can result in hypotension due to inadequate LV preload. Minor increases in ITP as occur using pressure-limited positive-pressure ventilation primarily reverse the increased LV afterload of negative swings in ITP and if fluid overload was already present, minimally alter cardiac output. The effect of changes in lung volume on LV function are related primarily to its effects on right ventricular (RV) function through changes in pulmonary vascular resistance and overdistention (hyperinflation). In acute lung injury with alveolar collapse, positive pressure ventilation may reduce pulmonary vascular resistance if alveolar recruitment predominates. Hyperinflation, however, impedes diastolic filling while simultaneously increasing pulmonary vascular resistance. Thus, increasing lung volume can reduce RV afterload by reversing hypoxic pulmonary vasoconstriction or increase afterload by overdistention. Hyperinflation can also impede RV filling. All of these processes can be readily identified at the bedside using echocardiography.

Relationship between intraventricular mechanical dyssynchrony and left ventricular systolic and diastolic performance: An in vivo experimental study.

Left ventricular mechanical dyssynchrony (LVMD) refers to the nonuniformity in mechanical contraction and relaxation timing in different ventricular segments. We aimed to determine the relationship between LVMD and LV performance, as assessed by ventriculo-arterial coupling (VAC), LV mechanical efficiency (LVeff ), left ventricular ejection fraction (LVEF), and diastolic function during sequential experimental changes in loading and contractile conditions. Thirteen Yorkshire pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/reinfusion and fluid bolus), and contractility (esmolol/dobutamine). LV pressure-volume data were obtained with a conductance catheter. Segmental mechanical dyssynchrony was assessed by global, systolic, and diastolic dyssynchrony (DYS) and internal flow fraction (IFF). Late systolic LVMD was related to an impaired VAC, LVeff , and LVEF, whereas diastolic LVMD was associated with delayed LV relaxation (logistic tau), decreased LV peak filling rate, and increased atrial contribution to LV filling. The hemodynamic factors related to LVMD were contractility, afterload, and heart rate. However, the relationship between these factors differed throughout the cardiac cycle. LVMD plays a significant role in LV systolic and diastolic performance and is associated with hemodynamic factors and intraventricular conduction.