Related Experiment Video
Updated: Jan 23, 2026

Utilizing Percutaneous Ventricular Assist Devices in Acute Myocardial Infarction Complicated by Cardiogenic Shock
Published on: June 12, 2021
Protein-based cardiogenic shock patient classifier
Ferran Rueda1,2, Eva Borràs3,4, Cosme García-García1,2
1Heart Institute, Hospital Universitari Germans Trias i Pujol, c/ Canyet SN, 08916 Badalona, Spain.
Insights
A new protein score, Cardiogenic Shock 4 proteins (CS4P), accurately predicts short-term mortality in patients with cardiogenic shock. Combining CS4P with existing scores significantly improves risk stratification for better patient management.
Area of Science:
- Cardiovascular Medicine
- Proteomics
- Biomarker Discovery
Background:
- Cardiogenic shock (CS) has a high short-term mortality rate.
- Accurate risk stratification is crucial for guiding interventions and improving patient outcomes in CS.
Purpose of the Study:
- To develop and validate a circulating protein-based score for predicting short-term mortality in patients with CS.
- To assess the performance of the novel score alone and in combination with existing risk scores.
Main Methods:
- Mass spectrometry was used for initial protein screening in a discovery cohort.
- Targeted quantitative proteomics identified and validated a classifier in an independent cohort.
- The classifier, Cardiogenic Shock 4 proteins (CS4P), comprises four specific circulating proteins.
Main Results:
- The CS4P classifier demonstrated significant discrimination between low and high 90-day mortality risk groups.
- Combining CS4P with the CardShock risk score improved predictive accuracy (C-statistic 0.84) and reclassification (NRI 0.49).
- Similar improvements were observed when CS4P was combined with the IABP-SHOCK II risk score (NRI 0.57).
Conclusions:
- The CS4P is a novel, validated protein-based classifier for short-term mortality risk stratification in CS patients.
- CS4P enhances the predictive power of existing risk scores, aiding clinical decision-making for advanced therapies.
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.
Related Concept Videos
Classifying Matter by Composition
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures.
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
A mixture is composed of two or...
Classifying Matter by State
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Shock Waves
When the source's speed approaches the speed of sound, constructive interference between successive wavefronts emitted by the source occurs immediately behind it. Initially, scientists believed that this constructive interference would result in such high...
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Factors Affecting Protein-Drug Binding: Patient-Related Factors
Age stands as a key determinant in protein-drug binding. Neonates, characterized by low albumin content, experience heightened concentrations of unbound drugs such as phenytoin and...

