Michael R. Pinsky

Restoring arterial pressure in hypotensive shock is a primary resuscitation goal, yet increases in cardiac output after fluid administration do not consistently produce meaningful increases in arterial pressure. This dissociation highlights the importance of understanding pressure–flow interactions during hemodynamic interventions. Dynamic arterial elastance (EaDyn) is a functional hemodynamic index proposed to characterize these interactions. Rather than reflecting static vascular resistance, EaDyn is hypothesized to describe the dynamic relationship between cardiac output and arterial load within the framework of ventriculo–arterial coupling. Clinically, EaDyn has been investigated as a predictor of the arterial pressure response to fluid administration in preload-responsive patients and as a marker of pressure stability during vasopressor reduction. However, current clinical evidence remains limited, heterogeneous, and derived largely from selected populations. This review summarizes the physiologic basis of EaDyn, critically appraises the available clinical evidence, and highlights current limitations and priorities for future research.

Tissue autoregulation to match local blood flow to metabolic demands requires both a high enough upstream mean arterial pressure (MAP) and a perfusion pressure difference between this MAP and the downstream arteriolar critical closing pressure to drive blood flow into the capillaries whose downstream capillary pressure is approximated as mean systemic filling pressure. The closing pressure to mean systemic pressure difference represents a vascular waterfall such that increases or decreases in mean systemic filling pressure below closing pressure do not alter tissue flow. Tissues autoregulate their blood flow by altering upstream local vasomotor tone to increase or decrease closing pressure. In vasoplegia, like septic shock, vascular tone is decreased, often decreasing closing pressure to approximating mean systemic filling pressure. Such conditions abolish autoregulation even if MAP and cardiac output are not decreased. Effective resuscitation from septic shock requires restoration of local vascular waterfalls. Often initial fluid resuscitation and vasopressor infusion restore vascular waterfalls and tissue blood flow. But often it does not. There are few readily available real-time quantitative estimates of tissue perfusion. Presently, only capillary refill time (CRT) monitors tissue blood flow changes. If initial resuscitation to target MAP values does not restore tissue perfusion and CRT is >3 seconds, then potentially a vasopressor test to increase MAP to >75 mmHg may be studied. If vasopressor-induced increases in MAP decrease CRT to <3 seconds, then it can be continued. If not, then return to prior levels to minimize iatrogenic vasopressor risk. This article addresses unanswered questions that need further study.

Ejection fraction (EF) is widely used to assess cardiac function in sepsis, yet it is inherently load-dependent and may not reflect intrinsic contractility. We quantified the relative contribution of hemodynamic determinants to left (LVEF) and right ventricular EF (RVEF) in a porcine model of endotoxic shock. Twelve female pigs were instrumented with biventricular conductance catheters and studied at four stages: baseline, endotoxic shock, fluid resuscitation, and norepinephrine infusion. Six candidate determinants (effective arterial elastance (Ea), end-systolic elastance (Ees), end-diastolic volume (EDV), heart rate (HR), the time constant of isovolumic relaxation (τ), and internal flow fraction (IFF)) were evaluated using centering within clusters linear mixed models (LV: 12 animals; RV: 9 animals). Ea, EDV, and Ees were the dominant determinants of LVEF, accounting for 31%, 27%, and 19% of explained variance (combined: 77%), while HR exerted a negative effect. For RVEF, predictor importance was more evenly distributed, with EDV and Ea as the leading contributors in dominance analysis; HR was positively associated with RVEF, opposite to its LV effect. This loading-dominant hierarchy persisted when Ees was replaced by alternative load-independent indices: preload-recruitable stroke work (PRSW) and Starling contractility index (SCI). Despite substantial stage-to-stage changes in preload, afterload, and contractility, EF varied within a narrow range (LVEF 37–45%; RVEF 50–60%), reflecting offsetting changes among determinants. Within this pressure-volume framework, loading conditions rather than contractility were the primary within-animal determinants of LVEF during endotoxic shock. The RV data were consistent with a similar hierarchy, although with differing determinant profiles and greater analytical uncertainty.

Purpose of review: Hemodynamic monitoring has undergone a profound transformation over the last 30 years. The field has transitioned from the “standard of care” invasive pulmonary artery catheterization (PAC) toward minimally invasive and noninvasive technologies. This evolution is characterized by a shift in clinical philosophy. Recent findings: A very strong shift away from the measurement of static pressures and flow defining specific hemodynamic profiles to the assessment of functional physiology and fluid responsiveness characterize the main changes in hemodynamic monitoring over this time interval. This review summarizes the four main milestones, the evidence-based “PAC-wars” and the “Great debate” (1995–2005); the shift to functional hemodynamics and more precise resuscitation (2005–2015), through the rise of goal-directed therapy (GDT) and protocolization based on patient subgroups (2010–2020); to the present where the focus is on noninvasive monitoring, using artificial intelligence (AI) to plumb biosignatures from time series data and focusing on tissue perfusion and end-organ function (2020–2025). Summary: The use of hemodynamic monitoring to plumb bedside cardiopulmonary status and monitor responses to therapy with the goal of precise and personalized resuscitation focusing on end-organ recovery using advanced minimally-invasive and on-invasive monitors coupled with AI-aided physiological pattern recognition represents the pathway we are on.

Sepsis heterogeneity reflects diverse etiologies and patient-specific physiological responses, motivating phenotype identification to enable precision therapeutics. However, most phenotyping approaches rely on intermittently sampled clinical variables, whereas continuously recorded physiological waveforms remain underutilized. We developed a deep-learning framework to derive physiological phenotypes from five-minute pre-onset electrocardiogram, photoplethysmogram and respiratory-impedance waveforms in 2,174 ICU patients meeting Sepsis-3 criteria. From these signals, 192 cardiorespiratory physiomarkers were extracted and embedded using a Feature Tokenizer Transformer encoder, which outperformed alternative representation methods. Consensus clustering identified four stable sepsis physio-phenotypes (SP-1–SP-4) associated with distinct autonomic and peripheral vascular signatures. Despite similar baseline severity and demographics, phenotypes differed significantly in mortality (19–29%), septic shock, vasopressor use and mechanical ventilation, with divergent 28-day survival trajectories (P<0.01). Explainable AI provided clinically interpretable characterizations, and a trained classifier enabled real-time bedside phenotyping. This framework establishes waveform-based phenotyping as a foundation for precision medicine in sepsis care.

Central to managing critically ill patients is the identification of the etiology of cardiorespiratory insufficiency (i.e., shock), early appropriate targeted therapies to support the cardiorespiratory system to sustain adequate blood flow and oxygen to the tissues, plus specific treatments to reverse the cause of shock. Over the past 40 years, numerous advances in our understanding of shock, its severity, and its response to therapies, along with more specific and insightful monitoring approaches, have been developed. This perspective summarizes some aspects of that progress. We have come a long way, but we need to understand three things. First, that once organ injury has occurred all that our treatments can do is mitigate further injury, not reverse it. If initial aggressive resurrection efforts cannot restore organ function, then their actions often cause only iatrogenic injury. Second, existing advanced monitoring devices, no matter how insightful their data, will not improve patient outcomes unless coupled to a treatment that itself improves outcomes. Finally, all our advances over these years have underscored the fundamental need for having a thoughtful and observant bedside clinician cognizant of the pathophysiologic underpinnings of disease and its care who titrates care based on the patient’s individual response.