Phenotyping Cardiogenic Shock

Abstract

Background Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in-hospital mortality. Methods and Results We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG-MI; n=410] and acute-on-chronic heart failure [CSWG-HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG-MI using the consensus k means algorithm and subsequently validated in CSWG-HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: "non-congested (I)", "cardiorenal (II)," and "cardiometabolic (III)" shock. Among the 3 cohorts (CSWG-MI versus DDR versus CSWG-HF), in-hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The "cardiometabolic shock" cluster had the highest risk of developing stage D or E shock as well as in-hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts. Conclusions Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.

Keywords: cardiogenic shock; clusters; heart failure; hemodynamics; machine learning; myocardial infarction; phenotypes.

Figure 1. Derivation of the clusters: consensus k‐means clustering.

A, Representative plots illustrating the method of consensus k‐means clustering in the CSWG (Cargiogenic Shock Working Group Registry) myocardial infarction derivation cohort. Each column represents one patient, whereas each row displays the assigned clusters. Well‐defined (ie, segregated) squares indicate stable clusters. Compared with k (number of clusters)=2 and k=4, k=3 shows highest cluster stability, suggesting that 3 may be a good choice for the number of clusters. B, A t‐distributed stochastic neighbor embedding (TSNE) plot for visual representation of the clusters in a 2‐dimensional space. The algorithm uses probability estimates to calculate the similarity of data points in the high‐dimensional space (ie, identifies the “neighbors”) and then calculates the distance of these “neighbors” in a lower‐dimensional space (in this case, 2 dimensions). The wider the different clusters separate in the plot, the larger is the difference between them.

Figure 2. Metabolic and hemodynamic profiles of the different phenotypes.

Radar plots illustrate the association of each phenotype with hemodynamic and metabolic variables in CSWG( Cargiogenic Shock Working Group Registry) myocardial infarction cohort. Data were normalized across all phenotypes to a mean of 0 and an SD of 1. The dashed black line marks the mean (0), whereas every concentric gray line signifies a 0.1‐SD difference from the overall mean. Values that were higher than the mean are drawn outside, whereas values that were lower than the mean are drawn inside the dashed line for each variable. ALT indicates alanine aminotransferase; BUN, blood urea nitrogen; CI, cardiac index; CO, cardiac output; CPI, cardiac power index; CPO, cardiac power output; DBP, diastolic blood pressure; GFR, glomerular filtration rate; HCO₃, sodium bicarbonate; Hgb, hemoglobin; INR, International Normalized Ratio; LVEDD, left ventricular end‐diastolic dimension; MAP, mean arterial pressure; PADP, pulmonary artery diastolic pressure; PAP, pulmonary artery pressure; PAPI, pulmonary artery pulsatility index; PASP, pulmonary artery systolic pressure; PCWP, pulmonary capillary wedge pressure; RAP, right atrial pressure; RVSWI, right ventricular stroke work index; SBP, systolic blood pressure; and WBC, white blood cell count.

Figure 3. In‐hospital mortality in the 3 distinct phenotypes of cardiogenic shock (CS).

Phenotype I (noncongested), phenotype II (cardiorenal), and phenotype III (cardiometabolic) are associated with in‐hospital mortality across 2 international multicenter registries of CS attributable to acute myocardial infarction (MI) and a multicenter registry of CS attributable to acute‐on‐chronic heart failure. CSWG indicates Cardiogenic Shock Working Group Registry; and DRR, Danish Retroshock MI Registry.

Figure 4. Society for Cardiovascular Angiography and Interventions (SCAI) stages reached during hospital stay by phenotype.

Upper panel: percentage of patients in each phenotype (I: noncongested; II: cardiorenal; III: cardiometabolic) reaching SCAI stage B, C, D, or E during their hospitalization in CSWG (Cardiogenic Shock Working Group Registry) myocardial infarction (MI) cohort, CSWG heart failure (HF) cohort, and both cohorts combined. These graphs can be read as follows: for example, a patient in admission phenotype 3 had a chance of >80% to reach SCAI stage D or E during his/her hospitalization independent of cause of shock (MI or HF). Bottom panel: in‐hospital mortality stratified by phenotype and SCAI stage in percentage for the CSWG‐MI cohort, CSWG‐HF cohort, and both cohorts combined. These graphs can be read as follows: For example, a patient with MI in admission phenotype 2 who only reached SCAI stage C had a probability of 14% for in‐hospital death; however, if the same patient reaches SCAI stage E, his/her probability to die in hospital is 63%. n.a. indicates data insufficient to assign SCAI stage.