Protein-based cardiogenic shock patient classifier

Abstract

Aims Cardiogenic shock (CS) is associated with high short-term mortality and a precise CS risk stratification could guide interventions to improve patient outcome. Here, we developed a circulating protein-based score to predict short-term mortality risk among patients with CS. Methods and results Mass spectrometry analysis of 2654 proteins was used for screening in the Barcelona discovery cohort (n = 48). Targeted quantitative proteomics analyses (n = 51 proteins) were used in the independent CardShock cohort (n = 97) to derive and cross-validate the protein classifier. The combination of four circulating proteins (Cardiogenic Shock 4 proteins—CS4P), discriminated patients with low and high 90-day risk of mortality. CS4P comprises the abundances of liver-type fatty acid-binding protein, beta-2-microglobulin, fructose-bisphosphate aldolase B, and SerpinG1. Within the CardShock cohort used for internal validation, the C-statistic was 0.78 for the CardShock risk score, 0.83 for the CS4P model, and 0.84 (P = 0.033 vs. CardShock risk score) for the combination of CardShock risk score with the CS4P model. The CardShock risk score with the CS4P model showed a marked benefit in patient reclassification, with a net reclassification improvement (NRI) of 0.49 (P = 0.020) compared with CardShock risk score. Similar reclassification metrics were observed in the IABP-SHOCK II risk score combined with CS4P (NRI =0.57; P = 0.032). The CS4P patient classification power was confirmed by enzyme-linked immunosorbent assay (ELISA). Conclusion A new protein-based CS patient classifier, the CS4P, was developed for short-term mortality risk stratification. CS4P improved predictive metrics in combination with contemporary risk scores, which may guide clinicians in selecting patients for advanced therapies.

Epidemiology, pathophysiology and contemporary management of cardiogenic shock – a position statement from the Heart Failure Association of the European Society of Cardiology

“…Further, circulating dipeptidyl peptidase 3 (DPP‐3) was associated with worsening haemodynamics, evolution to refractory CS and 90‐day mortality 70,71 . DPP‐3 is a cytosolic enzyme associated with alterations in the inflammation pathway, inducing strong negative inotropic and vasodilatory effect, 71 which can be reversed in animal models 69,70 …”

“…Proteomic research may further assist the understanding of pathophysiology, improve risk stratification and provide an opportunity for treatment 69 . A recent research study identified a complex of four proteins (CS4P) associated with multi‐organ dysfunction, systemic inflammation and immune activation 69 . During the early hours of CS, changes in the expression of CS4P may precede overt multi‐organ failure and identify patients at higher mortality risk 69 …”

Short- and Long-Term Mortality Trends in STEMI-Cardiogenic Shock over Three Decades (1989–2018): The Ruti-STEMI-Shock Registry

“…These scores combine classical clinical variables (age, previous AMI or stroke, ejection fraction, and aetiology of shock) with routine biochemical data (glucose, blood lactate, and renal function). Contemporary research on the CS proteome has identified a four-protein score, the CS4P, which has shown additional value above and beyond conventional CardShock or IABP-Shock II risk scores [ 26 ]. Transcriptomics and other -omics data are under intense scrutiny to support the identification of high- vs. low-risk patients [ 27 , 28 ], but there is still a long way to go.…”

Finding a Needle in a Haystack

“…Several studies have examined MS/MS proteomics to evaluate the disease state of HF ( 9 , 44 , 45 , 46 , 47 ). In a study of ischemic cardiomyopathy (ICM), Yi et al.…”

“…This was noteworthy because a more contemporary liquid chromatography tandem MS/MS study of patients with cardiogenic shock by Rueda et al. ( 46 ) identified SerpinG1 as a predictor of short-term mortality in shock, along with fatty acid binding protein, beta-2-microglobulin, and fructose-bisphosphate aldolase B. Taken together with clinical variables, this biomarker panel had high discrimination (C-statistic 0.84; p < 0.001) for predicting death ( 46 ).…”