Emerging biomarkers for risk stratification in cardiogenic shock: steps closer to precision?

The prognosis of cardiogenic shock (CS) remains poor despite advances in cardiovascular and critical care therapies, with in-hospital mortality remaining 30–50%.1,2 Heterogeneity among patients with CS—including on the basis of shock aetiology and contributing mechanisms, as well as clinical trajectory—may complicate early risk stratification and treatment selection.3–6 Subphenotyping patients with CS using blood-based biomarkers have accordingly been increasingly pursued.7 Biomarkers may provide important clinical information supporting individualized prognosis and treatment decision-making and may provide insight into mechanisms of disease. While markers of peripheral tissue metabolism and organ function are widely used clinical predictors, their use is limited by the fact that (i) they may lag behind clinical state, (ii) they reflect both acute and chronic influences, (iii) they represent final common pathway markers and are not aetiology-specific, and (iv) they may be abnormal due to aetiologies beyond the adequacy of perfusion (e.g. lactate may rise in response to exogenous epinephrine administration). While biomarkers of myocardial injury (troponin) and ventricular overload (brain natriuretic peptide) have significant prognostic ability in other cardiac conditions, their performance in CS is suboptimal.8 Given that inflammation plays key roles in the pathophysiology of CS,9 a number of studies have evaluated the performance of inflammatory and immune biomarkers such as C-reactive protein,10 neutrophil-to-lymphocyte ratio,11 growth-differentiating factor 15,12 and angiopoietin 2.13 The extent of systemic inflammation may vary from patient-to-patient with CS: recent work suggests that a subset of patients with myocardial infarction experience more significant transcriptional upregulation of acute inflammatory pathways,14 potentially contributing to the presence of superimposed inflammatory shock in some patients with CS—a clinically important group given their worse prognosis and differing haemodynamic profile.15 The availability of biomarkers to identify and possibly predict clinical trajectory on the basis of shock mechanism may possibly support early therapeutic decision-making. Patients with CS and enhanced immune response who develop a mixed picture of cardiogenic and vasodilatory shock pose a challenge for mechanical support therapies, as these may further contribute to the dysregulated inflammatory milieu.16,17 Understanding these inflammatory pathways in CS may support an improved understanding of the pathophysiology of CS. They may eventually support more individualized approaches to patient care, although randomized clinical trials are needed.18 Additionally, early high levels of circulating dipeptidyl peptidase 3, a protease involved in the degradation of cardiovascular mediators, were shown to predict worse outcomes in CS.19 Elevated fibroblast growth factor 23, involved in phosphate regulation, showed similar results.20 Emerging novel biomarkers, that may also shed light on specific mechanisms and therapeutic targets, include monoclonal haematopoiesis gene mutations21 and several microRNAs,22 but more research is needed. Several combinations of biomarkers have been evaluated for risk stratification in CS—possibly improving risk discrimination through simultaneous multi-pathway assessment. First, the CS 4 proteins score includes liver-type fatty acid-binding protein, beta-2-microglobulin, fructose-bisphosphate aldolase B and SerpinG1 and was shown to improve prediction of short-term mortality in combination with contemporary risk scores.23 Additionally, the CLIP-score (Cystatin C, Lactate, Interleukin-6 and NT-ProBNP) showed promising 30-day mortality prediction in a cohort of patients with CS after acute myocardial infarction.24 These scores also require further validation. In this issue of European Heart Journal Acute Cardiovascular Care, Hongisto et al.25 investigate risk stratification in CS using soluble urokinase–type plasminogen activator receptor (suPAR), a biomarker reflecting systemic immune system activation that was previously shown to be prognostic in other cardiac and non-cardiac conditions, including acute coronary syndrome (ACS), chronic heart failure, chronic kidney disease, acute respiratory distress syndrome, and sepsis. The investigators conducted an analysis of the CardShock study, a prospective observational multicenter study on CS in nine tertiary centres across eight European countries. The original study included 219 patients and yielded the CardShock risk score, that incorporates 7 clinical variables.26 Seven of the centres also participated in the biomarker substudy. Among 161 patients with available plasma samples (enrolled within the first 6 h of CS diagnosis), 7 serial serum suPAR measurements were collected over 4 days. suPAR was elevated in patients with CS compared with healthy individuals and rose longitudinally in non-survivors, while remaining stable in survivors. suPAR was associated with 90-day all-cause mortality, even after adjusting for the other prognostic variables through the CardShock risk score. suPAR marginally improved the validated CardShock risk score for 90-day mortality prediction, with an optimal cutoff value of 4.4 ng/mL at 12 h, based on an increase in the receiver operating characteristic curves from 0.84 to 0.87. This suggests that biomarkers, in conjunction with clinical traits, may support robust prediction models. Interestingly, suPAR was not correlated with neutrophil-to-lymphocyte ratio but was moderately so with C-reactive protein (r = 0.38).27 Finally, suPAR at 12 h effectively stratified intermediate-risk patients into high- or low-risk groups. Biomarkers in general may hold greatest promise for improved risk discrimination in intermediate-risk patients, as risk in very high- or low-risk patients is often already clinically evident (Figure 1). Biomarkers may support reclassification of intermediate-risk patients into low- or high-risk groups. This may guide clinical decision-making and potentially allow development of pathway-specific therapies (figure created in Wepik). The cohort consisted mainly of ACS-related CS, while non-ACS related CS is common in clinical practice.2 A more general limitation may relate to the possibility for residual confounding, and it is notable that such a study design cannot establish causation. suPAR joins a group of emerging candidate biomarkers that may offer prognostic and mechanistic insight into CS. Directions for future work may involve prospectively incorporating these biomarkers into clinical trial designs to support prognostic and possibly predictive enrichment, as well as for the evaluation of biomarker-stratified group effects.18 Emerging adaptive clinical trial designs—as used to support precision medicine in oncology—may be well-suited to these approaches.28 The possibility for precision medicine for the care of patients with CS may be rapidly moving from a pipedream to our clinical pipeline sooner than may have been previously thought possible. No new data were generated or analyzed in support of this research.

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