Phenotyping Cardiogenic Shock

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.

Invasive Hemodynamic Assessment and Classification of In-Hospital Mortality Risk Among Patients With Cardiogenic Shock

Background:

Risk stratifying patients with cardiogenic shock (CS) is a major unmet need. The recently proposed Society for Cardiovascular Angiography and Interventions (SCAI) stages as an approach to identify patients at risk for in-hospital mortality remains under investigation. We studied the utility of the SCAI stages and further explored the impact of hemodynamic congestion on clinical outcomes.

Methods:

The CS Working Group registry includes patients with CS from 8 medical centers enrolled between 2016 and 2019. Patients were classified by the maximum SCAI stage (B–E) reached during their hospital stay according to drug and device utilization. In-hospital mortality was evaluated for association with SCAI stages and hemodynamic congestion.

Results:

Of the 1414 patients with CS, the majority were due to decompensated heart failure (50%) or myocardial infarction (MI; 35%). In-hospital mortality was 31% for the total cohort, but higher among patients with MI (41% versus 26%, MI versus heart failure, P <0.0001). Risk for in-hospital mortality was associated with increasing SCAI stage (odds ratio [95% CI], 3.25 [2.63–4.02]) in both MI and heart failure cohorts. Hemodynamic data was available in 1116 (79%) patients. Elevated biventricular filling pressures were common among patients with CS, and right atrial pressure was associated with increased mortality and higher SCAI Stage.

Conclusions:

Our findings support an association between the proposed SCAI staging system and in-hospital mortality among patient with heart failure and MI. We further identify that venous congestion is common and identifies patients with CS at high risk for in-hospital mortality. These findings provide may inform future management protocols and clinical studies.