Skip to main content
Download PDF

Extending the ‘host response’ paradigm from sepsis to cardiogenic shock: evidence, limitations and opportunities

Critical Care volume 27, Article number: 460 (2023) Cite this article

Abstract

Recent clinical and research efforts in cardiogenic shock (CS) have largely focussed on the restoration of the low cardiac output state that is the conditio sine qua non of the clinical syndrome. This approach has failed to translate into improved outcomes, and mortality has remained static at 30–50%. There is an unmet need to better delineate the pathobiology of CS to understand the observed heterogeneity of presentation and treatment effect and to identify novel therapeutic targets. Despite data in other critical illness syndromes, specifically sepsis, the role of dysregulated inflammation and immunity is hitherto poorly described in CS. High-dimensional molecular profiling, particularly through leukocyte transcriptomics, may afford opportunity to better characterise subgroups of patients with shared mechanisms of immune dysregulation. In this state-of-the-art review, we outline the rationale for considering molecular subtypes of CS. We describe how high-dimensional molecular technologies can be used to identify these subtypes, and whether they share biological features with sepsis and other critical illness states. Finally, we propose how the identification of molecular subtypes of patients may enrich future clinical trial design and identification of novel therapies for CS.

Graphical Abstract

Introduction

Cardiogenic shock (CS) is a complex syndrome of hypoperfusion resulting from cardiac dysfunction. Historically, a simple model of the pathophysiology of CS has focused on a reduction in cardiac output leading to reduced end-organ perfusion with systemic tissue oxygen starvation which culminates in death in 30–50% of patients [1]. As such, beyond specific interventions such as culprit-vessel percutaneous coronary intervention (PCI) in acute myocardial infarction-CS (AMI-CS), current best care is supportive, targeting normalisation of hemodynamic (cardiac output and blood pressure) and biochemical perturbations through the use of inopressors and/or mechanical circulatory support (MCS) [2, 3]. However, augmentation of cardiac output has yet to demonstrate a survival benefit in clinical trials [4,5,6,7]. Recently published randomised control trial (RCT) [7] and meta-analysis [8] data illustrate no mortality benefit with the use of MCS in CS. This highlights the importance of finding alternative or complementary therapeutic strategies which have the potential to modify the syndrome itself once established.

One of the many challenges that CS presents, and in parallel with other critical illness syndromes such as sepsis [9, 10], is significant heterogeneity. The magnitude and haemodynamic presentation of shock differs considerably between patients, contributing to a range of clinical phenotypes, often despite similar aetiological insults [11]. Further heterogeneity is observed in the individual response to supportive measures which likely impacts disease severity, prognosis and treatment response. Whilst this variation has been described clinically [11,12,13], its pathobiological basis has hitherto been poorly delineated. In parallel with the response to infection in sepsis, cardiomyocyte necrosis and tissue hypoxia in CS may activate an inflammatory response. Although intended to be reparative and restorative, as in sepsis and ARDS, it is plausible that the ‘host response’ to myocardial injury and end organ failure may be misaligned, maladaptive or even injurious. Potential adverse sequelae of maladaptive inflammatory and immune activation include impaired microcirculation, inappropriate vasodilatation further compromising tissue perfusion, impaired cardiomyocyte recovery and compounded organ hypoperfusion through inappropriate vasodilatation and progression of the shock state. Importantly, the extent of inflammatory activation in response to AMI varies from patient-to-patient [14] with greater inflammation linked to short- and longer-term adverse outcomes [15]. This emerging ‘host-response’-oriented model is now established in inflammatory critical illness but has yet to be fully appreciated in CS. Accordingly, in this review we consider the relevant basis for such a model in CS, focusing primarily on AMI-CS where the pathophysiology and outcomes have been better studied to date. We theorise how this may enhance current research endeavours to improve patient outcomes.

Cardiogenic shock subphenotypes

In recognition of a need to relate clinical care and triage with CS severity, recent societal and registry efforts have attempted to stratify patients into groups defined by clinical, haemodynamic, metabolic and biochemical parameters [11,12,13]. The Society for Cardiovascular Angiography and Interventions (SCAI) Shock Classification, proposed by an expert panel, groups patients into stages A (“At risk”) to E (“Extremis”) [12]. The SCAI classification is currently the most well-validated in terms of mortality prediction [16,17,18,19] and has been adopted into clinical guidelines and clinical practice [20, 21]. Extending this, an unsupervised machine learning analysis of registry data identified three distinct clinical CS phenotypes, termed ‘non-congested’, ‘cardiorenal’ and ‘cardio-metabolic’ [11]. These phenotypes had distinct haemodynamic and biochemical profiles with reproducible associations with shock severity and mortality [22].

Whilst such groupings may inform CS severity, there is evidence that the patients defined within these cohorts, their associated mortality risk and treatment responses remain highly heterogeneous [23,24,25]. These classifications also provide no additional information on pathobiology to guide treatments. Delineating patient groups based on combinations of clinical manifestations and biomarker patterns that link with disease pathobiology, so-called sub-phenotyping, has been proposed to bridge underlying disease mechanisms with clinical phenotype [26]. Notably, in the recent ‘Extra-Corporeal Life Support in Infarct Related Shock’ (ECLS-SHOCK) study of 420 patients with AMI-CS, mortality was approximately 50% and of those that died, more than half died of refractory cardiogenic shock [7]. This finding suggests that there are drivers of persistent shock and organ failure that extend beyond cardiac output. Vasodilatory CS is defined by a haemodynamic profile characterised by low systemic vascular resistance and reduced cardiac output (CO) with or without infection. This subphenotype appears to have greater shock severity, more organ failures and a worse prognosis [27, 28]. Similarly, patients with microcirculatory dysfunction, or the uncoupling of macro- and peripheral microcirculatory blood flow in CS, may represent an additional sub-phenotype reflecting immune-mediated endothelial dysfunction and microvascular thrombosis identifiable by impairments in capillary refill time [29, 30].

The ‘host response’ to cardiogenic shock

The role of inflammation in cardiovascular disease [31] and the immune response to myocardial infarction is well established [32, 33]. Data in CS are largely derived from small-scale observational studies or sub-studies of randomised controlled trials examining circulating cytokines and proteins, with limited mechanistic data. Higher levels of biomarkers of systemic inflammation, such as C-reactive protein (CRP), procalcitonin (PCT) and IL-6, are associated with more severe hypoperfusion in CS, whilst levels of PCT and IL-6 correlate with multi-organ failure (MOF) [34] and mortality [35]. In a small single-centre study of AMI-CS, admission IL-6 was more strongly associated with 30-day mortality than more traditionally cardiac-specific markers, such as N-terminal pro-brain natriuretic peptide (NT-pro-BNP) [36]. Table 1 presents a comprehensive, though not exhaustive, list of inflammatory biomarkers implicated in the pathophysiology of CS.

Table 1 Studies investigating the role of inflammatory mediators in CS
Full size table

The occurrence of sepsis super-imposed on CS, and CS secondary to sepsis (septic cardiomyopathy), further drives the hypothesis that dysregulated immunity may contribute to CS pathobiology [53,54,55]. Patients with CS have multiple risk factors for increased risk of infection including preceding cardiac arrest, gut hypo-perfusion and risk of bacterial translocation, use of multiple invasive central venous catheters and prolonged mechanical ventilation and intensive care unit (ICU) stay. Estimates of the incidence of concomitant or secondary sepsis range widely, from 6% of patients with AMI-CS in a large US payer database to around 50% in single-centre observational cohort studies [45, 55]. Reasons for this wide range likely relate to overlap between the traditional clinical and biochemical markers used to diagnose CS and sepsis [45, 53, 54] as well as variability in the requirement for detection of a causative microorganism. The presence of concomitant sepsis is associated with increased shock severity [28], an increased risk of organ failures [55, 56] and higher mortality [45, 55].

Whether infection is a driver of dysregulated immunity or secondary to it, however, remains unclear. One potential source of sterile inflammation is endotoxin translocation from either digestive tract hypoperfusion or ischemia–reperfusion in CS. Endotoxemia reduces cardiac performance [57] and can propagate a low cardiac output state [58]. Despite sound rationale, three small studies [59,60,61] have failed to identify direct proof of endotoxemia in patients with CS with the caveat that no study has hitherto reported quantitative measurement (i.e. mass) of circulating endotoxins and many endotoxin assays have inherent detection limitations. An alternative explanation for the occurrence of secondary sepsis in AMI-CS is the development of maladaptive immune response, or even an acquired immunodeficiency. In a large cohort of critically ill patients with diffuse insults, after an initial phase of adaptive injury-induced immune response, a persistence of altered T-cell and monocyte response at one week was associated with secondary infection [62].

Septic cardiomyopathy typifies the overlap between septic and cardiogenic shock. Similar to CS, the precise mechanisms of cardiac dysfunction remain poorly elucidated. There is overlap in the observed immune response [38, 44, 47, 63, 64] to both AMI-CS and septic cardiomyopathy, despite the absence of a primary cardiac insult in the former. It is postulated that the systolic dysfunction of septic cardiomyopathy is an adaptive response which manifests as more classical cardiogenic shock when there is maladaptation and associated cellular dysfunction [65, 66]; a comprehensive review is beyond the scope of this article but is covered elsewhere [67]. Further research into potentially shared pathobiology between septic cardiomyopathy and cardiogenic shock syndromes through comparisons of existing multi-modal sepsis datasets with emerging CS datasets will hopefully be mechanistically and therapeutically revealing.

An emerging potential modulator of CS pathobiology is the circulating enzyme dipeptidyl peptidase 3 (DPP3). This zinc-dependent metallopeptidase is found intracellularly throughout all the body’s organ systems [68] and is released into the circulation during cell death [69, 70]. High levels of circulating DPP3 (cDPP3) have been found to be associated with increased severity in a range of shocked states [71]. Deletion of DPP3 impacts production of both proinflammatory and anti-inflammatory cytokines [72]. Injection of DPP3 in a murine model produced myocardial depression, whilst administration of a specific antibody targeted against cDPP3 normalised haemodynamics [73]. In vivo cDPP3 levels were measured in patients recruited to the multi-centre Optima CC trial [46] comparing epinephrine versus norepinephrine for haemodynamic support in CS [74]. High levels of cDPP3 (> 51.9 ng/ml) were associated with greater risk of death at 90 days, greater organ dysfunction and lower cardiac index, findings which have been confirmed in subsequent observational studies [75, 76]. A large multi-centre, prospective study investigating the role of cDPP3 in acute coronary syndromes (ACS) found that high levels were independently predictive of the development of in-hospital CS (HR 1.49, 95% CI 1.14–1.96, P = 0.004) [52]. Whilst promising, further data to clarify the precise biological role of DPP3 in CS are needed.

The contribution of baseline inflammation in the system-wide pathogenesis of CS has been further highlighted by a potential role for clonal haematopoiesis (CH) [77, 78]. CH is the acquisition of somatic mutations of potentially oncogenic genes in haematopoietic stem cells (HSC) that results in distinct immune cell clones with dysregulated function. The carriage of these mutations has been associated with increased risk of atherosclerotic cardiovascular disease and cardiac failure [79]. Some in vitro studies have suggested that macrophages and monocytes with clonal features have a hyper-inflammatory phenotype [79, 80]. In a biomarker sub-study of the multi-centre “Culprit Lesion Only PCI Versus Multivessel PCI in Cardiogenic Shock—CULPRIT-SHOCK” trial [81] a correlation between the burden of CH and risk of death or requirement for renal replacement therapy was observed [77]. CH was also associated with increased levels of the inflammatory cytokines IL-6 and IL1-beta. Similarly, in a single-centre matched retrospective cohort study comparing the presence of CH in patients with CS versus those with stable heart failure (HF), CS patients had a 50% higher prevalence of CH-related mutations (odds ratio 1.5; p = 0.02) [78]. Higher CH was associated with reduced survival and dysregulation of circulating inflammatory cytokines, particularly in those patients with mutations of tet methylcytosine dioxygenase 2 (TET2). Collectively these data suggest that CH may augment the acute inflammatory state in CS, contributing to the development of superimposed vasodilatory shock, and in turn to worse outcomes.

Given the assertion that immune activation and dysregulated inflammation are implicated in the pathogenesis of CS, it would follow that immunomodulation may improve clinical outcomes. To date, despite trials in patients with heart failure [82,83,84], there have been no clinical trials specifically testing immunomodulatory therapy in patients with CS. This likely reflects both challenges of studies in CS patients per se as well as the rudimentary state of understanding of the inflammatory response as a therapeutic target. One ongoing study will assess the effects of the IL-6 monoclonal antibody tocilizumab on the development of CS after MI (ClinicalTrials.gov Identifier: NCT05350592), testing the importance of inflammation and the neurohormonal response to the development of CS. Another ongoing study will test the Oxiris membrane™, which removes circulating pro-inflammatory cytokines and lipopolysaccharides (ClinicalTrials.gov Identifiers: NCT05642273 and NCT04886180), in the most severe cohort of CS patients supported with venoarterial extracorporeal membrane oxygenation. Figure 1 illustrates how the past and current understanding of CS pathophysiology has shaped both treatment goals and clinical trial design.

Fig. 1
figure 1

Past and present risk stratification in CS. + Severity staging pyramid adopted from Society of Cardiovascular Angiography & Intervention (SCAI) [12]. Historically the management of cardiogenic shock (CS) has focussed on the normalisation of haemodynamic and biochemical parameters. As such, early research has compared the optimal modality to achieve this i.e. pharmacotherapy versus mechanical circulatory support. More recently, investigators have aimed phenotype patients into risk categories using both clinical parameters and measurements of inflammatory mediators to define severity of shock or risk of death

Full size image

Future State of the ‘Host Response’ to Cardiogenic Shock: sub-phenotypes, endotyping and treatable traits

The observations describing the inflammatory response to CS above highlight the need for future studies which capture rich data on immune phenotypes. The immune system is highly complex, characterised by multidimensional relationships across multiple scales. Genes, cells and whole organs function together to preserve homeostasis often with multiple layers of redundancy. This complexity has driven the use of high-dimensional readouts, based on genetics, transcriptomics (RNA-sequencing), proteomics and metabolomics, to collect measurements informative for different features of the host immune state. In CS, there is the opportunity to discover molecular features or mechanisms associated with observed clinical traits, either individually in the form of single associated genetic loci, genes or proteins, or in ensemble gene sets or co-expression modules. Such molecular features, as well as clinical patient characteristics, can enable the discovery and definition of sub-phenotypes (subgroups). A disease endotype refers to instances where sub-phenotype (subgroup) characteristics/biomarkers define or associate with a specific pathophysiological mechanism. In terms of clinical utility, there is interest in establishing treatable traits, whereby sub-phenotype characteristics/biomarkers identify a group of patients with a specific pathophysiological derangement which manifests a predictable response to a specific therapy.

The most common approach to sub-phenotype and endotype discovery is peripheral blood leukocyte transcriptomics using RNA-sequencing. Whole blood provides a snapshot of the gene expression and abundance of cell types at different stages of haematopoiesis. Whilst peripheral blood sampling is logistically simple, the cellular composition of peripheral blood may change significantly over the natural history of critical illness and in response to critical care interventions and therapies [85]. Whole blood isolation of peripheral blood mononuclear cells (PBMCs) is informative but limits analysis to lymphocyte and monocyte populations [14]. This approach has the advantage of isolating cell types with greater relevance to the adaptive immune system but is relatively laborious and omits the key granulocyte populations which are the major effectors of the acute host response. The granularity of RNA-sequencing data acquisition and cellular heterogeneity can be further enhanced by quantification of the transcriptome of individual cells (single-cell RNA-sequencing) as opposed to measurement of average gene expression measured across a large population of cells (bulk RNA-sequencing).

The largest whole-blood transcriptomic studies, predominantly performed in sepsis populations [86,87,88,89], have used unsupervised clustering approaches to partition patients into discrete categories. The hypothesis is that these gene-expression groupings reflect fundamental differences in the host response, which are not better explained by known clinical covariates like age, sex, blood cell proportions, causative organism or immunosuppression. Clinical outcomes can then be compared across the subgroups, with association found for mortality after adjusting for known confounders in sepsis by different research teams [87] or worsening of a clinical score [90, 91].

Identified genes or other molecular features can be further investigated using established biological pathway datasets to understand their biological function. For example, recent work identified a poor outcome sepsis endotype, broadly replicated across different infectious disease contexts [86], that had features of maladaptive ‘emergency myelopoiesis’, with increased abundance of activated neutrophils, so-called myeloid-derived suppressor cells (MDSCs), haematopoietic stem and progenitor cells (HSPCs) and specific immature neutrophil populations. A patient’s membership in a subphenotype or endotype can be modelled as a continuous trait, rather than discrete categories [92] which increases the power to detect dynamic changes in immune status over the course of disease or in response to therapy. Conceptually, endotyping can also support the identification of existing or emerging animal model systems that best represent clinical CS subphenotypes to support preclinical testing [93,94,95,96].

The majority of such host response profiling has been performed in infectious diseases and sepsis where the relevance of peripheral blood is intuitive with a paucity of large-scale work performed using tissue samples of relevance to CS. The ShockOmics consortium contrasted leukocyte gene expression at three different time-points (days 1, 2 and 7) in 21 patients with septic shock and 11 patients with CS [97]. Patients were matched for demographics and illness severity and inclusion required survival at seven days which may have enriched for a cohort with a favourable trajectory. Overall gene expression features were found to be similar across the 2 shock sub-phenotypes. The major source of between-sample variation in this dataset corresponded to the time from disease onset to sampling, suggesting that patients with either septic shock or CS follow broadly similar host response trajectories from the first to the seventh day of intensive care unit admission.

As the most prevalent aetiology of CS, study of the ‘host response’ to AMI may provide insights into the pathobiology and heterogeneity of patients who progress towards CS. Hence, study of leukocyte gene expression in over 100 patients with AMI identified 2 sub-phenotypes which coded for proteins related to platelet function. Patients with sub-phenotype 2 exhibited higher CRP on admission than those with sub-phenotype 1. Gene set enrichment analysis of 139 patients in eleven datasets of PBMCs from AMI patients highlighted extensive changes characterised by pro-inflammatory activation and enhanced leukocyte-platelet interactions with one-third of patients classified into a hyper-inflammatory group [98]. Stratification by consensus clustering suggested AMI patients differ in the severity of inflammatory activation; however, there were no data to associate this with progression to or severity of CS. Speculatively, these data suggest that differential pathway activation may contribute to different CS sub-phenotypes. The Prospective Observational Study Investigating Genomic Determinants of Outcome From Cardiogenic Shock (GOlDilOCS, ClinicalTrials.gov Identifier: NCT05728359) and VANQUISH Shock [99] will analyse these associations at a gene expression and proteome level in prospective study of 300 and 600 CS patients, respectively.

Whilst conceptually appealing, the approach of whole blood sampling may not, however, be a relevant proxy measure for host response patterns in remote organs or cells [100]. In the context of CS, targeted collection of coronary endothelial or even myocardial/endocardial samples may be a useful addition to the more accessible peripheral blood samples which remain the mainstay of studies of the host response. Because these tissue samples are difficult to acquire, emerging modalities which provide information on damage occurring in remote organs are likely to be of interest.

One example of this is cell-free DNA methylation sequencing that is a developing technique which can provide information on damage occurring in remote tissues [101]. This extracellular DNA is released by dying cells and then passes into the peripheral blood compartment, where it circulates with a half-life of 1–2 h. DNA methylation patterns are tissue-specific, in most instances due to conserved epigenetic enhancer usage across cell types. Sequencing and deconvolution of the circulating cell-free DNA provides an indirect index of the degree of cell death occurring in organs which are difficult to sample. This approach may complement the use of existing clinical tests for organ-specific damage in CS such as cardiomyocyte troponin and hepatocellular aminotransferases. It also offers an opportunity to investigate the inconsistently observed association between increased levels of circulating cell-free DNA and adverse outcomes in critical illness [102,103,104], which could be related to differences in the tissue-of-origin of the circulating DNA. Proof-of-concept studies have demonstrated cardiomyocyte-specific release in heart failure [105] and after MI [106]. Investigation of the dynamic release of cfDNA specifically derived from the vascular endothelial cells (VECs) of different organs demonstrated that organ-specific VEC cfDNA can be detected in plasma during clinical illness—for example, lung-derived VEC dfDNA during sepsis, exacerbations of chronic respiratory disease and after cardiac catheterisation [107].

Cell-free DNA can be readily isolated from frozen plasma samples and can be amplified inexpensively for a small number of cell-type defining methylation sites or sequenced in toto to assess for a wide range of cell-type-specific patterns. As this method matures, it is conceivable that this may become a useful tool for dynamic assessment of organ function, for example before and after administration of a drug or initiation of MCS support in CS. This could be of particular interest in CS, where understanding the effect of tissue hypoperfusion on specific organs is likely to be a useful tool for patient phenotyping.

Parallels with other critical illness syndromes: exotyping

Given the absence of therapies that improve outcome in CS and the observation that restoration of the low cardiac output that is the conditio sine qua non for CS has not improved clinical outcomes [108, 109], it serves to reason that the current concepts of CS may not adequately capture the complexity of what is essentially a critical illness syndrome.

Critical illness states such as sepsis and CS have historically been defined by the primary organ dysfunction combined with constellations of non-specific clinical, biochemical and physiological abnormalities. It is apparent that many of these abnormalities are shared across critical illness syndromes regardless of the initial insult [10, 22]. Whilst there is clearly heterogeneity both within critical illness syndromes and between them, this apparent homogeneity raises the prospect that despite disparate insults, similar underlying biological mechanisms or molecular signatures exist across critical illness syndromes as drivers of a common physiological derangement. This is the concept of exotypes, defined as endotypes conserved across different syndromes and sub-phenotypes [26].

Hence, the genomic response to trauma, severe burns and endotoxin exposure differed largely only in the duration rather than the response itself [110, 111]. The observed transcriptional up-regulation of inflammatory mediators mirrors that in ARDS and pancreatitis [111]. Comprehensive, longitudinal immune profiling in patients with infectious (sepsis) and sterile (traumatic injury and post-surgery) injury demonstrated common signatures of pro-/anti-inflammatory, innate and adaptive immune responses [62]. Whilst coronary ischaemia–reperfusion will be exclusive to AMI-CS, the pathobiological drivers of the systemic endothelial dysfunction and organ dysfunction observed in sepsis may be contributory in CS. For example, perturbations of the angiopoietin-2 -Tie 2 axis, a regulator of capillary permeability, have been observed in both sepsis [112, 113] and AMI-CS [114] with elevations of angiopoietin-2 associated with both poor outcome and coronary reperfusion success. It is therefore conceivable that other mechanistic drivers of sepsis, namely immune activation/dysfunction, mitochondrial dysfunction, complement activation, renin–angiotensin–aldosterone system activation and microcirculatory dysfunction [115,116,117], represent sub-phenotypes of AMI-CS. The identification and validation of biomarkers of AMI-CS sub-phenotypes, such as bio-adrenomedullin for severe or refractory vasoplegia [118], raises the enticing prospect of future targeted therapies across a range of shock states including AMI-CS. Future multi-omic preclinical and clinical study should be undertaken to identify conserved molecular responses across sub-phenotypes of differing shock states, specifically those patients with refractory shock who appear to be at highest risk of death (Graphical Abstract).

Future directions

Precision medicine in cardiogenic shock

Secondary analyses from prior clinical trials in CS have failed to identify subgroups with clear differential treatment effects. This may reflect limitations in conventional one-at-a-time subgroup analysis which may be mitigated by machine learning methods such as causal forests or risk-based modelling, as recently demonstrated [119,120,121]. This may also reflect the choice to partition based on clinical groups as opposed to either molecular sub-phenotypes or endotypes described herein. Whilst advances continue to be made in clinical phenotyping of CS, greater precision, specifically linking clinical traits with pathobiology, is required to identify populations who will predictably respond to existing or emerging therapies. The identification of CS endotypes and treatable traits [122] offers the potential to enrich future clinical trial design and interventions through a more personalised or precision medicine approach (Fig. 2).

Fig. 2
figure 2

Endotype enrichment of future clinical trials in AMI-CS. AMI-CS = Acute myocardial infarction cardiogenic shock. Future research into the host response to cardiogenic shock should aim to identify endotypes which can then be used to enrich clinical trials to increase the likelihood of positive trial results

Full size image

An endotype or treatable trait could be incorporated into a clinical trial as either a stratifying variable at recruitment, or in the case of dynamic and especially quantitative endotype systems, as a response measure. The first scenario is oftentimes divided into ‘prognostic’ and ‘predictive’ enrichment [123]. In prognostic enrichment, a set of predictors thought to have prognostic value, generally for mortality, are used as a screening mechanism at trial recruitment. Limiting recruitment to the cohort at highest risk of adverse outcome leverages the assumption that individuals with the most extreme risk may experience differential treatment effects. This assumption should be subject to some scrutiny given how frequently the opposite interpretation, that a trial has returned negative results because its population is too unwell to have the possibility to benefit (e.g. later-stage, unmodifiable risk), is also offered in the literature [124]. Risk-based heterogeneity of treatment response may be nonlinear, with greatest benefit in those at sufficient risk of the outcome to benefit, but not so sick as to be past the point where treatment is beneficial. Nonetheless, recent work in sepsis has demonstrated the potential for endotype-based stratification and quantitative scoring of patients with acute infection at point of care [92] by providing a framework that can be used with existing rapid turnaround methods (real-time quantitative reverse transcription PCR, qRT-PCR), as well as full-transcriptome technologies. This opens up the potential for future bedside, prognostic enrolment into clinical trials and even personalized therapeutic decision-making.

An alternative endotype-based strategy is so-called predictive enrichment, where a measurable trait, thought to have a biological relationship with experimental treatment, is used to select patients with a higher expected likelihood of benefit. Endotype stratification has been used in post hoc analyses of sepsis clinical trials. Whilst the VANISH randomised controlled trial [125] showed no mortality effect associated with corticosteroid treatment in sepsis, an interaction was found between sepsis endotype at baseline and mortality, with patients assigned to the lower-risk ‘immunocompetent’ endotype shown to have poorer survival [85]. Similar results were produced in a separate re-analysis using a separate endotype classification [126]. These associations are yet to be tested prospectively but suggest a route for developing endotype assignments as a stratifying factor for clinical trial design. Although the initial trial showed no mortality benefit with the use of polymyxin B haemofiltration versus sham [127], the post hoc division of patients by endotoxin activity showed differential effects on ventilatory-free days, mean arterial pressure (MAP) and mortality [128]. A large ongoing sepsis/ARDS trial is using 2 endotype classifications as pre-specified randomisation strata [129], will test these tools in the prospective setting.

The major challenge is finding an appropriate stratifying tool; to a large extent, traits which robustly predict treatment response are unknown before they are tested in trials. Post hoc stratification of trials with broad recruitment criteria can provide clues, but findings should be replicated in additional cohorts and ideally in preclinical model systems [130].

Caution should be exercised when estimating the likelihood that a null overall treatment response is masking unobserved heterogeneity of treatment effect across the trial population, and that high-dimensional assays can consequently be used to distinguish individual ‘responders’ from ‘non-responders’. Most applications in critical care allow only a single measure of treatment response for each patient [131]. When observing statistical heterogeneity in a response variable after a single episode of treatment, this may potentially be explained by statistical noise rather than a true biological difference and can easily be compounded by arbitrary dichotomization of a continuous variable to distinguish ‘responders’ from ‘non-responders’ [132]. The often-made statement that null overall treatment responses in critical illness therapeutic trials could reflect unmeasured heterogeneity in either the clinical phenotype or in the treatment response should be tested, not simply assumed to be true [133]. Emerging applications of adaptive clinical trial designs are being deployed in acute and critical illnesses that may be more amenable than conventional designs to the prospective identification of heterogeneity of treatment effect [134].

Conclusion

New therapies and new approaches to clinical trial design are an urgent and unmet need if we are to improve the current lethality of CS. AMI-CS is increasingly recognised as encompassing features of systemic inflammation in addition to the systemic hypoperfusion due to pump failure. The holy grail of CS management is a granular understanding of disease heterogeneity as it relates to specific disease mechanisms and physiologic responses that would afford the opportunity to identify bespoke treatments with predictable responses that improve patient outcomes. Using the more nuanced approaches outlined herein may offer insights into the underlying molecular mechanisms of AMI-CS, with potential parallels to other critical illness syndromes and allow a transition from the current risk-based approach towards a mechanistic approach that embraces the heterogeneity within the CS population.

Abbreviations

ACS:

Acute coronary syndrome

AMI:

Acute myocardial infarction

ARDS:

Acute respiratory distress syndrome

CICU:

Cardiac critical care unit

CO:

Cardiac output

CS:

Cardiogenic shock

cfDNA:

Cell-free DNA

cDPP3:

Circulating dipeptidyl peptidase

CH:

Clonal haematopoiesis

CRP:

C-Reactive protein

DNA:

Deoxyribonucleic acid

DPP3:

Dipeptidyl peptidase 3

ECLS:

Extra-corporeal life support

ECMO:

Extra-corporeal membrane oxygenation

G-CSF:

Granulocyte-colony-stimulating factor

GDF-15:

Growth differentiation factor-15

HSC:

Haematopoietic stem cells

HSPCs:

Haematopoietic stem and progenitor cells

HF:

Heart failure

HTE:

Heterogeneity of treatment effect

IFN-g:

Interferon gamma

ICU:

Intensive care unit

IL-1:

Interleukin-1

IL-1b:

Interleukin-1 beta

IL-1Ra:

Interleukin-1 receptor antagonist

IL-5:

Interleukin-5

IL-6:

Interleukin-6

IL-7:

Interleukin-7

IL-8:

Interleukin-8

IL-10:

Interleukin-10

MIP-1b:

Macrophage inflammatory protein-1 beta

MCS:

Mechanical circulatory support

MCP-1:

Monocyte chemoattractant protein-1

MCP-1b:

Monocyte chemoattractant protein-1 beta

MOF:

Multi-organ failure

MDSCs:

Myeloid-derived suppressor cells

MI:

Myocardial infarction

NLR:

Neutrophil-to-leucocyte ratio

NSTEMI:

Non-ST elevation myocardial infarction

NT-pro-BNP:

N-terminal pro-brain natriuretic peptide

PTX 3:

Pentraxin 3

PCI:

Percutaneous coronary intervention

PBMCs:

Peripheral blood mononuclear cells

Prdx-1:

Peroxiredoxin-1

PLR:

Platelet-to-lymphocyte ratio

PCAS:

Post-cardiac arrest syndrome

PCT:

Procalcitonin

RANTES:

Regulated on activation, normal T cell expressed and secreted

RNA:

Ribonucleic acid

SelP:

Selenoprotein P

SS:

Septic shock

SCAI:

Society for Cardiovascular Angiography and Interventions

STEMI:

ST-elevation myocardial infarction

TET2:

Tet methylcytosine dioxygenase 2

TNF-a:

Tumour necrosis factor alpha

US:

United States

VECs:

Vascular endothelial cells

WBCs:

White blood cells

References

  1. Lawler PR, Berg DD, Park JG, Katz JN, Baird-Zars VM, Barsness GW, et al. The range of cardiogenic shock survival by clinical stage: data from the critical care cardiology trials network registry. Crit Care Med. 2021;49(8):1293–302.

    Article  PubMed  Google Scholar 

  2. Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, et al. 2023 ESC guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44:3720–826.

    Article  PubMed  Google Scholar 

  3. van Diepen S, Katz JN, Albert NM, Henry TD, Jacobs AK, Kapur NK, et al. Contemporary management of cardiogenic shock: a scientific statement from the american heart association. Circulation. 2017. https://doi.org/10.1161/CIR.0000000000000525.

    Article  PubMed  Google Scholar 

  4. Jung C, Fuernau G, De Waha S, Eitel I, Desch S, Schuler G, et al. Intraaortic balloon counterpulsation and microcirculation in cardiogenic shock complicating myocardial infarction: an IABP-SHOCK II substudy. Clin Res Cardiol. 2015;104(8):679–87.

    Article  PubMed  Google Scholar 

  5. Ostadal P, Rokyta R, Kruger A, Vondrakova D, Janotka M, Smíd O, et al. Extra corporeal membrane oxygenation in the therapy of cardiogenic shock (ECMO-CS): rationale and design of the multicenter randomized trial: extra corporeal membrane oxygenation in the therapy of cardiogenic Shock (ECMO-CS). Eur J Heart Fail. 2017;19:124–7.

    Article  CAS  PubMed  Google Scholar 

  6. Ouweneel DM, Eriksen E, Sjauw KD, Van Dongen IM, Hirsch A, Packer EJS, et al. Percutaneous mechanical circulatory support versus intra-aortic balloon pump in cardiogenic shock after acute myocardial infarction. J Am Coll Cardiol. 2017;69(3):278–87.

    Article  PubMed  Google Scholar 

  7. Thiele H, Zeymer U, Akin I, Behnes M, Rassaf T, Mahabadi AA, et al. Extracorporeal life support in infarct-related cardiogenic shock. N Engl J Med. 2023;389:1286–97.

    Article  CAS  PubMed  Google Scholar 

  8. Zeymer U, Freund A, Hochadel M, Ostadal P, Belohlavek J, Rokyta R, et al. Venoarterial extracorporeal membrane oxygenation in patients with infarct-related cardiogenic shock: an individual patient data meta-analysis of randomised trials. The Lancet. 2023;402:1338–46.

    Article  Google Scholar 

  9. Lawler PR, Fan E. Heterogeneity and phenotypic stratification in acute respiratory distress syndrome. Lancet Respir Med. 2018;6(9):651–3.

    Article  PubMed  Google Scholar 

  10. Maslove DM, Tang B, Shankar-Hari M, Lawler PR, Angus DC, Baillie JK, et al. Redefining critical illness. Nat Med. 2022;28(6):1141–8.

    Article  CAS  PubMed  Google Scholar 

  11. Zweck E, Thayer KL, Helgestad OKL, Kanwar M, Ayouty M, Garan AR, et al. Phenotyping cardiogenic shock. JAHA. 2021;10(14): e020085.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Baran DA, Grines CL, Bailey S, Burkhoff D, Hall SA, Henry TD, et al. SCAI clinical expert consensus statement on the classification of cardiogenic shock: this document was endorsed by the American College of Cardiology (ACC), the American Heart Association (AHA), the Society of Critical Care Medicine (SCCM), and the Society of Thoracic Surgeons (STS) in April 2019. Catheter Cardiovasc Interv. 2019;94:29–37.

    Article  PubMed  Google Scholar 

  13. Lim HS. Phenotyping and hemodynamic assessment in cardiogenic shock: from physiology to clinical application. Cardiol Ther. 2022;11(4):509–22.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Toma A, Dos Santos C, Burzyńska B, Góra M, Kiliszek M, Stickle N, et al. Diversity in the expressed genomic host response to myocardial infarction. Circ Res. 2022;131(1):106–8.

    Article  CAS  PubMed  Google Scholar 

  15. Lawler PR, Bhatt DL, Godoy LC, Lüscher TF, Bonow RO, Verma S, et al. Targeting cardiovascular inflammation: next steps in clinical translation. Eur Heart J. 2021;42(1):113–31.

    Article  CAS  PubMed  Google Scholar 

  16. Baran DA, Long A, Badiye AP, Stelling K. Prospective validation of the SCAI shock classification: single center analysis. Catheter Cardiovasc Interv. 2020;96(7):1339–47.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jentzer JC, Soussi S, Lawler PR, Kennedy JN, Kashani KB. Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population. Cathet Cardio Intervent. 2022;99(4):1006–14.

    Article  Google Scholar 

  18. Naidu SS, Baran DA, Jentzer JC, Hollenberg SM, van Diepen S, Basir MB, et al. SCAI SHOCK stage classification expert consensus update: a review and incorporation of validation studies. J Am Coll Cardiol. 2022;79(9):933–46.

    Article  PubMed  Google Scholar 

  19. Zweck E, Kanwar M, Li S, Sinha SS, Garan AR, Hernandez-Montfort J, et al. Clinical course of patients in cardiogenic shock stratified by phenotype. JACC Heart Failure. 2023

  20. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42(36):3599–726.

    Article  CAS  PubMed  Google Scholar 

  21. Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022. https://doi.org/10.1161/CIR.0000000000001063

  22. Soussi S, Dos Santos C, Jentzer JC, Mebazaa A, Gayat E, Pöss J, et al. Distinct host-response signatures in circulatory shock: a narrative review. ICMx. 2023;11(1):50.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Jentzer JC. Understanding cardiogenic shock severity and mortality risk assessment. Circ Heart Failure. 2020;13(9):e007568.

    Article  PubMed  Google Scholar 

  24. Burstein B, Van Diepen S, Wiley BM, Anavekar NS, Jentzer JC. Biventricular function and shock severity predict mortality in cardiac ICU patients. Chest. 2022;161(3):697–709.

    Article  PubMed  Google Scholar 

  25. Thayer KL, Zweck E, Ayouty M, Garan AR, Hernandez-Montfort J, Mahr C, et al. Invasive hemodynamic assessment and classification of in-hospital mortality risk among patients with cardiogenic shock. Circ Heart Failure. 2020;13(9):1007099.

    Article  Google Scholar 

  26. Jentzer JC, Rayfield C, Soussi S, Berg DD, Kennedy JN, Sinha SS, et al. Advances in the staging and phenotyping of cardiogenic shock. JACC Adv. 2022;1(4):100120.

    Article  Google Scholar 

  27. Kohsaka S. Systemic inflammatory response syndrome after acute myocardial infarction complicated by cardiogenic shock. Arch Intern Med. 2005;165(14):1643.

    Article  PubMed  Google Scholar 

  28. Jentzer JC, Lawler PR, Van Diepen S, Henry TD, Menon V, Baran DA, et al. Systemic inflammatory response syndrome is associated with increased mortality across the spectrum of shock severity in cardiac intensive care patients. Circ Cardiovasc Qual Outcomes. 2020;13(12):1006956.

    Article  Google Scholar 

  29. Merdji H, Curtiaud A, Aheto A, Studer A, Harjola VP, Monnier A, et al. Performance of early capillary refill time measurement on outcomes in cardiogenic shock: an observational, prospective multicentric study. Am J Respir Crit Care Med. 2022;206(10):1230–8.

    Article  PubMed  Google Scholar 

  30. Lawler PR, van Diepen S. Toward a broader characterization of macro- and microcirculatory uncoupling in cardiogenic shock. Am J Respir Crit Care Med. 2022;206(10):1192–3.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Boyalla V, Gallego-Colon E, Spartalis M. Immunity and inflammation in cardiovascular disorders. BMC Cardiovasc Disord. 2023;23(1):148.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zlatanova I, Pinto C, Silvestre JS. Immune modulation of cardiac repair and regeneration: the art of mending broken hearts. Front Cardiovasc Med. 2016. https://doi.org/10.3389/fcvm.2016.00040/full.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gentek R, Hoeffel G. The innate immune response in myocardial infarction, repair, and regeneration. In: Sattler S, Kennedy-Lydon T, editors. The immunology of cardiovascular homeostasis and pathology. Cham: Springer International Publishing; 2017 [cited 2023 Jul 7]. p. 251–72. (Advances in Experimental Medicine and Biology; vol. 1003). https://doi.org/10.1007/978-3-319-57613-8_12

  34. Geppert A, Steiner A, Zorn G, Delle-Karth G, Koreny M, Haumer M, et al. Multiple organ failure in patients with cardiogenic shock is associated with high plasma levels of interleukin-6. Crit Care Med. 2002;30(9):1987–94.

    Article  CAS  PubMed  Google Scholar 

  35. Kataja A, Tarvasmäki T, Lassus J, Sionis A, Mebazaa A, Pulkki K, et al. Kinetics of procalcitonin, C-reactive protein and interleukin-6 in cardiogenic shock—Insights from the CardShock study. Int J Cardiol. 2020; https://www.internationaljournalofcardiology.com/article/S0167-5273(20)33652-4/fulltext

  36. Andrié RP, Becher UM, Frommold R, Tiyerili V, Schrickel JW, Nickenig G, et al. Interleukin-6 is the strongest predictor of 30-day mortality in patients with cardiogenic shock due to myocardial infarction. Crit Care. 2012;16(4):R152.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Geppert A, Steiner A, Delle-Karth G, Heinz G, Huber K. Usefulness of procalcitonin for diagnosing complicating sepsis in patients with cardiogenic shock. Intensive Care Med. 2003;29(8):1384–9.

    Article  PubMed  Google Scholar 

  38. Debrunner M, Schuiki E, Minder E, Straumann E, Naegeli B, Mury R, et al. Proinflammatory cytokines in acute myocardial infarction with and without cardiogenic shock. Clin Res Cardiol. 2008;97(5):298–305.

    Article  CAS  PubMed  Google Scholar 

  39. Picariello C, Lazzeri C, Chiostri M, Gensini G, Valente S. Procalcitonin in patients with acute coronary syndromes and cardiogenic shock submitted to percutaneous coronary intervention. Intern Emerg Med. 2009;4(5):403–8.

    Article  PubMed  Google Scholar 

  40. Prondzinsky R, Unverzagt S, Lemm H, Wegener NA, Schlitt A, Heinroth KM, et al. Interleukin-6, -7, -8 and -10 predict outcome in acute myocardial infarction complicated by cardiogenic shock. Clin Res Cardiol. 2012;101(5):375–84.

    Article  CAS  PubMed  Google Scholar 

  41. Prondzinsky R, Unverzagt S, Lemm H, Wegener N, Heinroth K, Buerke U, et al. Acute myocardial infarction and cardiogenic shock: Prognostic impact of cytokines: INF-γ, TNF-α, MIP-1β, G-CSF, and MCP-1β. Med Klin Intensivmed Notfmed. 2012;107(6):476–84.

    Article  CAS  PubMed  Google Scholar 

  42. Fuernau G, Poenisch C, Eitel I, De Waha S, Desch S, Schuler G, et al. Growth-differentiation factor 15 and osteoprotegerin in acute myocardial infarction complicated by cardiogenic shock: a biomarker substudy of the IABP-SHOCK II-trial. Eur J Heart Fail. 2014;16(8):880–7.

    Article  CAS  PubMed  Google Scholar 

  43. Lipkova J, Parenica J, Duris K, Helanova K, Tomandl J, Kubkova L, et al. Association of circulating levels of RANTES and −403G/A promoter polymorphism to acute heart failure after STEMI and to cardiogenic shock. Clin Exp Med. 2015;15(3):405–14.

    Article  CAS  PubMed  Google Scholar 

  44. Liu CH, Kuo SW, Hsu LM, Huang SC, Wang CH, Tsai PR, et al. Peroxiredoxin 1 induces inflammatory cytokine response and predicts outcome of cardiogenic shock patients necessitating extracorporeal membrane oxygenation: an observational cohort study and translational approach. J Transl Med. 2016;14(1):114.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Parenica J, Jarkovsky J, Malaska J, Mebazaa A, Gottwaldova J, Helanova K, et al. Infectious complications and immune/inflammatory response in cardiogenic shock patients: a prospective observational study. Shock. 2017;47(2):165–74.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Takagi K, Blet A, Levy B, Deniau B, Azibani F, Feliot E, et al. Circulating dipeptidyl peptidase 3 and alteration in haemodynamics in cardiogenic shock: results from the OptimaCC trial. Eur J Heart Fail. 2020;22(2):279–86.

    Article  CAS  PubMed  Google Scholar 

  47. Cuinet J, Garbagnati A, Rusca M, Yerly P, Schneider AG, Kirsch M, et al. Cardiogenic shock elicits acute inflammation, delayed eosinophilia, and depletion of immune cells in most severe cases. Sci Rep. 2020;10(1):7639.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Büttner P, Obradovic D, Wunderlich S, Feistritzer HJ, Holzwirth E, Lauten P, et al. Selenoprotein P in myocardial infarction with cardiogenic shock. Shock. 2020;53(1):58–62.

    Article  PubMed  Google Scholar 

  49. Jentzer JC, Szekely Y, Burstein B, Ballal Y, Kim EY, van Diepen S, et al. Peripheral blood neutrophil-to-lymphocyte ratio is associated with mortality across the spectrum of cardiogenic shock severity. J Crit Care. 2022;68:50–8.

    Article  PubMed  Google Scholar 

  50. Roth S, M’Pembele R, Stroda A, Jansen C, Lurati Buse G, Boeken U, et al. Neutrophil-lymphoycyte-ratio, platelet-lymphocyte-ratio and procalcitonin for early assessment of prognosis in patients undergoing VA-ECMO. Sci Rep. 2022;12(1):542.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dudda J, Schupp T, Rusnak J, Weidner K, Abumayyaleh M, Ruka M, et al. C-reactive protein and white blood cell count in cardiogenic shock. JCM. 2023;12(3):965.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wenzl FA, Bruno F, Kraler S, Klingenberg R, Akhmedov A, Ministrini S, et al. Dipeptidyl peptidase 3 plasma levels predict cardiogenic shock and mortality in acute coronary syndromes. Eur Heart J. 2023;44(38):3859–71. https://doi.org/10.1093/eurheartj/ehad545.

    Article  PubMed  Google Scholar 

  53. Kohsaka S, Menon V, Iwata K, Lowe A, Sleeper LA, Hochman JS. Microbiological profile of septic complication in patients with cardiogenic shock following acute myocardial infarction (from the SHOCK study). Am J Cardiol. 2007;99(6):802–4.

    Article  PubMed  Google Scholar 

  54. Nair RM, Kumar S, Saleem T, Chawla S, Vural A, Abdelghaffar B, et al. Characteristics and impact of bloodstream infections in cardiogenic shock patients on temporary mechanical circulatory support. JACC Cardiovasc Intervent. 2022; https://www.sciencedirect.com/science/article/pii/S1936879822014029

  55. Jentzer JC, Bhat AG, Patlolla SH, Sinha SS, Miller PE, Lawler PR, et al. Concomitant sepsis diagnoses in acute myocardial infarction-cardiogenic shock: 15-year national temporal trends, management, and outcomes. Crit Care Explor. 2022;4(2): e0637.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Schmidt M, Brechot N, Hariri S, Guiguet M, Luyt CE, Makri R, et al. Nosocomial infections in adult cardiogenic shock patients supported by venoarterial extracorporeal membrane oxygenation. Clin Infect Dis. 2012;55(12):1633–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Suffredini AF, Fromm RE, Parker MM, Brenner M, Kovacs JA, Wesley RA, et al. The cardiovascular response of normal humans to the administration of endotoxin. N Engl J Med. 1989;321(5):280–7.

    Article  CAS  PubMed  Google Scholar 

  58. Krack A, Sharma R, Figulla HR, Anker SD. The importance of the gastrointestinal system in the pathogenesis of heart failure. Eur Heart J. 2005;26(22):2368–74.

    Article  CAS  PubMed  Google Scholar 

  59. Attanà P, Lazzeri C, Chiostri M, Picariello C, Gensini GF, Valente S. Endotoxin role in cardiogenic shock: a brief report. Int J Cardiol. 2013;167(6):3031–2.

    Article  PubMed  Google Scholar 

  60. Ramirez P, Villarreal E, Gordon M, Gómez MD, de Hevia L, Vacacela K, et al. Septic participation in cardiogenic shock: exposure to bacterial endotoxin. Shock. 2017;47(5):588–92.

    Article  CAS  PubMed  Google Scholar 

  61. Lee CT, Wang CH, Chan WS, Tsai YY, Wei TJ, Lai CH, et al. Endotoxin activity in patients with extracorporeal membrane oxygenation life support: an observational pilot study. Front Med. 2021;8: 772413.

    Article  Google Scholar 

  62. Venet F, Textoris J, Blein S, Rol ML, Bodinier M, Canard B, et al. Immune profiling demonstrates a common immune signature of delayed acquired immunodeficiency in patients with various etiologies of severe injury*. Crit Care Med. 2022;50(4):565–75.

    Article  PubMed  Google Scholar 

  63. Kumar A, Thota V, Dee L, Olson J, Uretz E, Parrillo JE. Tumor necrosis factor alpha and interleukin 1beta are responsible for in vitro myocardial cell depression induced by human septic shock serum. J Exp Med. 1996;183(3):949–58.

    Article  CAS  PubMed  Google Scholar 

  64. Pathan N, Hemingway CA, Alizadeh AA, Stephens AC, Boldrick JC, Oragui EE, et al. Role of interleukin 6 in myocardial dysfunction of meningococcal septic shock. The Lancet. 2004;363(9404):203–9.

    Article  CAS  Google Scholar 

  65. Levy RJ, Piel DA, Acton PD, Zhou R, Ferrari VA, Karp JS, et al. Evidence of myocardial hibernation in the septic heart*. Crit Care Med. 2005;33(12):2752–6.

    Article  PubMed  Google Scholar 

  66. Matkovich SJ, Al Khiami B, Efimov IR, Evans S, Vader J, Jain A, et al. Widespread down-regulation of cardiac mitochondrial and sarcomeric genes in patients with sepsis*. Crit Care Med. 2017;45(3):407–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hollenberg SM, Singer M. Pathophysiology of sepsis-induced cardiomyopathy. Nat Rev Cardiol. 2021;18(6):424–34.

    Article  PubMed  Google Scholar 

  68. Sato A, Ogita H. Pathophysiological implications of dipeptidyl peptidases. CPPS. 2017;18(8):843–9.

    Article  CAS  Google Scholar 

  69. Wattiaux R, Wattiaux-De Coninck S, Thirion J, Gasingirwa MC, Jadot M. Lysosomes and Fas-mediated liver cell death. Biochem J. 2007;403(1):89–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Rehfeld L, Funk E, Jha S, Macheroux P, Melander O, Bergmann A. Novel methods for the quantification of dipeptidyl peptidase 3 (DPP3) concentration and activity in human blood samples. J Appl Lab Med. 2019;3(6):943–53.

    Article  CAS  PubMed  Google Scholar 

  71. Lier D, Kox M, Pickkers P. Promotion of vascular integrity in sepsis through modulation of bioactive adrenomedullin and dipeptidyl peptidase 3. J Intern Med. 2021;289(6):792–806.

    Article  PubMed  Google Scholar 

  72. Baral PK, Jajčanin-Jozić N, Deller S, Macheroux P, Abramić M, Gruber K. The first structure of dipeptidyl-peptidase III provides insight into the catalytic mechanism and mode of substrate binding. J Biol Chem. 2008;283(32):22316–24.

    Article  CAS  PubMed  Google Scholar 

  73. Deniau B, Rehfeld L, Santos K, Dienelt A, Azibani F, Sadoune M, et al. Circulating dipeptidyl peptidase 3 is a myocardial depressant factor: dipeptidyl peptidase 3 inhibition rapidly and sustainably improves haemodynamics. Eur J Heart Fail. 2020;22(2):290–9.

    Article  CAS  PubMed  Google Scholar 

  74. Levy B, Clere-Jehl R, Legras A, Morichau-Beauchant T, Leone M, Frederique G, et al. Epinephrine versus norepinephrine for cardiogenic shock after acute myocardial infarction. J Am Coll Cardiol. 2018;72(2):173–82.

    Article  CAS  PubMed  Google Scholar 

  75. Innelli P, Lopizzo T, Paternò G, Bruno N, Radice RP, Bertini P, et al. Dipeptidyl amino-peptidase 3 (DPP3) as an early marker of severity in a patient population with cardiogenic shock. Diagnostics. 2023;13(7):1350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Nordin H, Picod A, Hartmann O, Santos K, Azibani F, Bergmann A, et al. Circulating DPP3 is an early predictor of mortality and organ support in patients with cardiogenic shock: post-hoc analyses of the ACCOST-HH trial. Arch Cardiovasc Diseases Suppl. 2023;15(2):208.

    Article  Google Scholar 

  77. Böhme M, Desch S, Rosolowski M, Scholz M, Krohn K, Büttner P, et al. Impact of clonal hematopoiesis in patients with cardiogenic shock complicating acute myocardial infarction. J Am Coll Cardiol. 2022;80(16):1545–56.

    Article  PubMed  Google Scholar 

  78. Scolari FL, Abelson S, Brahmbhatt DH, Medeiros JJF, Fan CPS, Fung NL, et al. Clonal haematopoiesis is associated with higher mortality in patients with cardiogenic shock. Eur J Heart Fail. 2022;24(9):1573–82.

    Article  CAS  PubMed  Google Scholar 

  79. Jaiswal S, Libby P. Clonal haematopoiesis: connecting ageing and inflammation in cardiovascular disease. Nat Rev Cardiol. 2020;17(3):137–44.

    Article  PubMed  Google Scholar 

  80. Cook EK, Luo M, Rauh MJ. Clonal hematopoiesis and inflammation: partners in leukemogenesis and comorbidity. Exp Hematol. 2020;83:85–94.

    Article  CAS  PubMed  Google Scholar 

  81. Thiele H, Akin I, Sandri M, Fuernau G, de Waha S, Meyer-Saraei R, et al. PCI strategies in patients with acute myocardial infarction and cardiogenic shock. N Engl J Med. 2017;377(25):2419–32.

    Article  PubMed  Google Scholar 

  82. Everett BM, Cornel JH, Lainscak M, Anker SD, Abbate A, Thuren T, et al. Anti-inflammatory therapy with canakinumab for the prevention of hospitalization for heart failure. Circulation. 2019;139(10):1289–99.

    Article  CAS  PubMed  Google Scholar 

  83. Bozkurt B, Torre-Amione G, Warren MS, Whitmore J, Soran OZ, Feldman AM, et al. Results of targeted anti-tumor necrosis factor therapy with etanercept (ENBREL) in patients with advanced heart failure. Circulation. 2001;103(8):1044–7.

    Article  CAS  PubMed  Google Scholar 

  84. Chung ES, Packer M, Lo KH, Fasanmade AA, Willerson JT. Randomized, double-blind, placebo-controlled, pilot trial of infliximab, a chimeric monoclonal antibody to tumor necrosis factor-α, in patients with moderate-to-severe heart failure: results of the anti-TNF therapy against congestive heart failure (ATTACH) trial. Circulation. 2003;107(25):3133–40.

    Article  CAS  PubMed  Google Scholar 

  85. Antcliffe DB, Burnham KL, Al-Beidh F, Santhakumaran S, Brett SJ, Hinds CJ, et al. Transcriptomic signatures in sepsis and a differential response to steroids. From the VANISH randomized trial. Am J Respir Crit Care Med. 2019;199(8):980–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Sweeney TE, Azad TD, Donato M, Haynes WA, Perumal TM, Henao R, et al. Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters. Crit Care Med. 2018;46(6):915.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Davenport EE, Burnham KL, Radhakrishnan J, Humburg P, Hutton P, Mills TC, et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir Med. 2016;4(4):259–71.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Scicluna BP, van Vught LA, Zwinderman AH, Wiewel MA, Davenport EE, Burnham KL, et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir Med. 2017;5(10):816–26.

    Article  PubMed  Google Scholar 

  89. Tsalik EL, Langley RJ, Dinwiddie DL, Miller NA, Yoo B, van Velkinburgh JC, et al. An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med. 2014;6(11):111.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Baghela A, Pena OM, Lee AH, Baquir B, Falsafi R, An A, et al. Predicting sepsis severity at first clinical presentation: the role of endotypes and mechanistic signatures. EBioMedicine. 2022;75:103776.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Fiorino C, Liu Y, Henao R, Ko ER, Burke TW, Ginsburg GS, et al. Host gene expression to predict sepsis progression. Crit Care Med. 2022;50(12):1748–56.

    Article  CAS  PubMed  Google Scholar 

  92. Cano-Gamez E, Burnham KL, Goh C, Allcock A, Malick ZH, Overend L, et al. An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression. Sci Transl Med. 2022;14(669):eabq4433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Silva KAS, Emter CA. Large animal models of heart failure. JACC Basic Trans Sci. 2020;5(8):840–56.

    Article  Google Scholar 

  94. Tanamati C, Monachini M, Cantarelli M, Khouri P, Amarante G, Martins P, et al. Cardiogenic shock: an experimental animal model. Crit Care. 2005;9(Suppl 2):P10.

    Article  PubMed Central  Google Scholar 

  95. Wang Y, Polten F, Jäckle F, Korf-Klingebiel M, Kempf T, Bauersachs J, et al. A mouse model of cardiogenic shock. Cardiovasc Res. 2021;117(12):2414–5.

    Article  CAS  PubMed  Google Scholar 

  96. Rienzo M, Imbault J, El Boustani Y, Beurton A, Carlos Sampedrano C, Pasdois P, et al. A total closed chest sheep model of cardiogenic shock by percutaneous intracoronary ethanol injection. Sci Rep. 2020;10(1):12417.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Braga D, Barcella M, Herpain A, Aletti F, Kistler EB, Bollen Pinto B, et al. A longitudinal study highlights shared aspects of the transcriptomic response to cardiogenic and septic shock. Crit Care. 2019;23(1):414.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Wang Y, Chen Y, Zhang T. Integrated whole-genome gene expression analysis reveals an atlas of dynamic immune landscapes after myocardial infarction. Front Cardiovasc Med. 2023;10:1087721.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Tehrani BN, Drakos SG, Billia F, Batchelor WB, Luk A, Stelling K, et al. The multicenter collaborative to enhance biologic understanding, quality, and outcomes in cardiogenic shock (VANQUISH Shock): rationale and design. Can J Cardiol. 2022;38(8):1286–95.

    Article  PubMed  Google Scholar 

  100. Heijnen NFL, Hagens LA, Smit MR, Schultz MJ, van der Poll T, Schnabel RM, et al. Biological subphenotypes of acute respiratory distress syndrome may not reflect differences in alveolar inflammation. Physiol Rep. 2021;9(3): e14693.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Moss J, Magenheim J, Neiman D, Zemmour H, Loyfer N, Korach A, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun. 2018;9(1):5068.

    Article  PubMed  PubMed Central  Google Scholar 

  102. Rhodes A, Cecconi M. Cell-free DNA and outcome in sepsis. Crit Care. 2012;16(6):170.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Jackson Chornenki NL, Coke R, Kwong AC, Dwivedi DJ, Xu MK, McDonald E, et al. Comparison of the source and prognostic utility of cfDNA in trauma and sepsis. Intensive Care Med Exp. 2019;7(1):29.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Garnacho-Montero J, Huici-Moreno MJ, Gutiérrez-Pizarraya A, López I, Márquez-Vácaro JA, Macher H, et al. Prognostic and diagnostic value of eosinopenia, C-reactive protein, procalcitonin, and circulating cell-free DNA in critically ill patients admitted with suspicion of sepsis. Crit Care. 2014;18(3):R116.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Yokokawa T, Misaka T, Kimishima Y, Shimizu T, Kaneshiro T, Takeishi Y. Clinical significance of circulating cardiomyocyte-specific cell-free DNA in patients with heart failure: a proof-of-concept study. Can J Cardiol. 2020;36(6):931–5.

    Article  PubMed  Google Scholar 

  106. Zemmour H, Planer D, Magenheim J, Moss J, Neiman D, Gilon D, et al. Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA. Nat Commun. 2018;9(1):1443.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Peretz A, Loyfer N, Piyanzin S, Ochana BL, Neiman D, Magenheim J, et al. The DNA methylome of human vascular endothelium and its use in liquid biopsies. Med. 2023;4(4):263-281.e4.

    Article  CAS  PubMed  Google Scholar 

  108. Lim N, Dubois MJ, De Backer D, Vincent JL. Do all nonsurvivors of cardiogenic shock die with a low cardiac index?*. Chest. 2003;124(5):1885–91.

    Article  PubMed  Google Scholar 

  109. El Sibai R, Bachir R, El Sayed M. ECMO use and mortality in adult patients with cardiogenic shock: a retrospective observational study in U.S. hospitals. BMC Emerg Med. 2018;18(1):20.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Xiao W, Mindrinos MN, Seok J, Cuschieri J, Cuenca AG, Gao H, et al. A genomic storm in critically injured humans. J Exp Med. 2011;208(13):2581–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Neyton LPA, Zheng X, Skouras C, Doeschl-Wilson A, Gutmann MU, Uings I, et al. Molecular patterns in acute pancreatitis reflect generalizable endotypes of the host response to systemic injury in humans. Ann Surg. 2022;275(2):e453–62.

    Article  PubMed  Google Scholar 

  112. Orfanos SE, Kotanidou A, Glynos C, Athanasiou C, Tsigkos S, Dimopoulou I, et al. Angiopoietin-2 is increased in severe sepsis: correlation with inflammatory mediators. Crit Care Med. 2007;35(1):199–206.

    Article  CAS  PubMed  Google Scholar 

  113. Ricciuto DR, Dos Santos CC, Hawkes M, Toltl LJ, Conroy AL, Rajwans N, et al. Angiopoietin-1 and angiopoietin-2 as clinically informative prognostic biomarkers of morbidity and mortality in severe sepsis*. Crit Care Med. 2011;39(4):702–10.

    Article  CAS  PubMed  Google Scholar 

  114. Pöss J, Fuernau G, Denks D, Desch S, Eitel I, De Waha S, et al. Angiopoietin-2 in acute myocardial infarction complicated by cardiogenic shock-a biomarker substudy of the IABP-SHOCK II-Trial: angiopoietin-2 in acute myocardial infarction complicated by cardiogenic shock. Eur J Heart Fail. 2015;17(11):1152–60.

    Article  PubMed  Google Scholar 

  115. Frydland M, Ostrowski SR, Møller JE, Hadziselimovic E, Holmvang L, Ravn HB, et al. Plasma concentration of biomarkers reflecting endothelial cell- and glycocalyx damage are increased in patients with suspected ST-elevation myocardial infarction complicated by cardiogenic shock. Shock. 2018;50(5):538–44.

    Article  CAS  PubMed  Google Scholar 

  116. Zarbock A, Nadim MK, Pickkers P, Gomez H, Bell S, Joannidis M, et al. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol. 2023;19(6):401–17.

    Article  PubMed  Google Scholar 

  117. Merdji H, Levy B, Jung C, Ince C, Siegemund M, Meziani F. Microcirculatory dysfunction in cardiogenic shock. Ann Intensive Care. 2023;13(1):38.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Tolppanen H, Rivas-Lasarte M, Lassus J, Sans-Roselló J, Hartmann O, Lindholm M, et al. Adrenomedullin: a marker of impaired hemodynamics, organ dysfunction, and poor prognosis in cardiogenic shock. Ann Intensive Care. 2017;7(1):6.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Dahabreh IJ, Kazi DS. Toward personalizing care: assessing heterogeneity of treatment effects in randomized trials. JAMA. 2023;329(13):1063.

    Article  PubMed  Google Scholar 

  120. Goligher EC, Lawler PR, Jensen TP, Talisa V, Berry LR, Lorenzi E, et al. Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19. JAMA. 2023;329(13):1066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Writing Committee for the REMAP-CAP Investigators, Florescu S, Stanciu D, Zaharia M, Kosa A, Codreanu D, et al. Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19: a randomized clinical trial. JAMA. 2023;329(14):1183.

    Article  PubMed Central  Google Scholar 

  122. Russell CD, Baillie JK. Treatable traits and therapeutic targets: Goals for systems biology in infectious disease. Curr Opin Syst Biol. 2017;2:140–6.

    Article  PubMed  PubMed Central  Google Scholar 

  123. Stanski NL, Wong HR. Prognostic and predictive enrichment in sepsis. Nat Rev Nephrol. 2020;16(1):20–31.

    Article  PubMed  Google Scholar 

  124. Shankar-Hari M, Rubenfeld GD. The use of enrichment to reduce statistically indeterminate or negative trials in critical care. Anaesthesia. 2017;72(5):560–5.

    Article  CAS  PubMed  Google Scholar 

  125. Gordon AC, Mason AJ, Thirunavukkarasu N, Perkins GD, Cecconi M, Cepkova M, et al. Effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock: the VANISH randomized clinical trial. JAMA. 2016;316(5):509–18.

    Article  CAS  PubMed  Google Scholar 

  126. Wong HR, Hart KW, Lindsell CJ, Sweeney TE. External corroboration that corticosteroids may be harmful to septic shock endotype a patients. Crit Care Med. 2021;49(1): e98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Dellinger RP, Bagshaw SM, Antonelli M, Foster DM, Klein DJ, Marshall JC, et al. Effect of targeted polymyxin B Hemoperfusion on 28-day mortality in patients with septic shock and elevated endotoxin level: the EUPHRATES randomized clinical trial. JAMA. 2018;320(14):1455.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Klein DJ, Foster D, Walker PM, Bagshaw SM, Mekonnen H, Antonelli M. Polymyxin B hemoperfusion in endotoxemic septic shock patients without extreme endotoxemia: a post hoc analysis of the EUPHRATES trial. Intensive Care Med. 2018;44(12):2205–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Fleuriet J, Heming N, Meziani F, Reignier J, Declerq PL, Mercier E, et al. Rapid rEcognition of COrticosteRoiD resistant or sensitive Sepsis (RECORDS): study protocol for a multicentre, placebo-controlled, biomarker-guided, adaptive Bayesian design basket trial. BMJ Open. 2023;13(3): e066496.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Santhakumaran S, Gordon A, Prevost AT, O’Kane C, McAuley DF, Shankar-Hari M. Heterogeneity of treatment effect by baseline risk of mortality in critically ill patients: re-analysis of three recent sepsis and ARDS randomised controlled trials. Crit Care. 2019;23(1):156.

    Article  PubMed  PubMed Central  Google Scholar 

  131. Senn S. Individual response to treatment: is it a valid assumption? BMJ. 2004;329(7472):966–8.

    Article  PubMed  PubMed Central  Google Scholar 

  132. Senn S. Statistical pitfalls of personalized medicine. Nature. 2018;563(7733):619–21.

    Article  CAS  PubMed  Google Scholar 

  133. Gewandter JS, McDermott MP, He H, Gao S, Cai X, Farrar JT, et al. Demonstrating heterogeneity of treatment effects among patients: an overlooked but important step toward precision medicine. Clin Pharmacol Ther. 2019;106(1):204–10.

    Article  PubMed  Google Scholar 

  134. Lawler PR, Hochman JS, Zarychanski R. What are adaptive platform clinical trials and what role may they have in cardiovascular medicine? Circulation. 2022;145(9):629–32.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

None

Funding

AGP is funded by a Medical Research Council Clinical Academic Research Partnership Award. Ref: MR/W03011X/1 and the Barts Charity. MB is funded by Barts Charity.

Author information

Author notes

  1. Marie Buckel, Patrick Maclean have authors contributed equally.

Authors and Affiliations

  1. Department of Perioperative Medicine, Bart’s Heart Centre, St. Bartholomew’s Hospital, London, UK

    Marie Buckel & Alastair G. Proudfoot

  2. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    Patrick Maclean & Julian C. Knight

  3. Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK

    Julian C. Knight

  4. Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, ON, Canada

    Patrick R. Lawler

  5. McGill University Health Centre, McGill University, Montreal, QC, Canada

    Patrick R. Lawler

  6. Queen Mary University of London, London, UK

    Alastair G. Proudfoot

Authors
  1. Marie Buckel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Maclean
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julian C. Knight
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick R. Lawler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alastair G. Proudfoot
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AGP conceived the original review. MB and PM contributed equally to writing the first draft. All authors reviewed subsequent and final drafts.

Corresponding author

Correspondence to Alastair G. Proudfoot.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buckel, M., Maclean, P., Knight, J.C. et al. Extending the ‘host response’ paradigm from sepsis to cardiogenic shock: evidence, limitations and opportunities. Crit Care 27, 460 (2023). https://doi.org/10.1186/s13054-023-04752-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13054-023-04752-8

Keywords

Critical Care

ISSN: 1364-8535