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.

Background: Cardiogenic shock occurring in the setting of advanced heart failure (HF-CS) is increasingly common. However, recent studies have focused almost exclusively on acute myocardial infarction-related CS. We sought to define clinical, hemodynamic, metabolic, and treatment parameters associated with clinical outcomes among patients with HF-CS, using data from the Cardiogenic Shock Working Group registry. Methods: Patients with HF-CS were identified from the multicenter Cardiogenic Shock Working Group registry and divided into 3 outcome categories assessed at hospital discharge: mortality, heart replacement therapy (HRT: durable ventricular assist device or orthotopic heart transplant), or native heart survival. Clinical characteristics, hemodynamic, laboratory parameters, drug therapies, acute mechanical circulatory support device (AMCS) utilization, and Society of Cardiovascular Angiography and Intervention stages were compared across the 3 outcome cohorts. Results: Of the 712 patients with HF-CS identified, 180 (25.3%) died during their index admission, 277 (38.9%) underwent HRT (durable ventricular assist device or orthotopic heart transplant), and 255 (35.8%) experienced native heart survival without HRT. Patients who died had the highest right atrial pressure and heart rate and the lowest mean arterial pressure of the 3 outcome groups ( P 1 AMCS device had the highest in-hospital mortality rate irrespective of the number of vasoactive drugs used. Mortality increased with deteriorating Society of Cardiovascular Angiography and Intervention stages (stage B: 0%, stage C: 10.7%, stage D: 29.4%, stage E: 54.5%, 1-way ANOVA=1 AMCS device.

Background: The aim of this systematic review was to describe the prognostic value of patient-reported outcome measures (PROMs) in adult heart-transplant (HT) patients. Methods: A systematic search was performed on Ovid Medline, CINAHL Plus, Web of Science, and PubMed. The study protocol was registered on the PROSPERO database (CRD42021225398), and the last search was performed on January 7, 2021. We included studies of adult HT patients where generic and disease-specific PROMs were used as prognostic indicators for survival, readmissions, HT complications, and the onset of new comorbidities. We excluded studies that used clinician-reported and patient-experience outcomes. The Quality in Prognosis Studies tool (QUIPS) was used to measure the risk of bias of the included studies. Results: We included five observational studies between 1987 and 2015, whose populations’ mean age ranged from 43 to 56 years and presented a higher proportion of males than females. The Kansas City Cardiomyopathy Questionnaire demonstrated a negative correlation with readmissions (coefficient = − 1.177, p = 0.031), and the EQ-5D showed a negative correlation with the onset of neuromuscular disease after HT (coefficient = − 0.158, p < 0.001). The Millon Behavioral Health Inventory and the Nottingham Health Profile demonstrated a statistically significant association as survival predictors (p = 0.002 and p < 0.05, respectively). A moderate overall risk of bias was reported in three studies, one study resulted in a low risk of bias, and a proportion of more than 75% of males in each of the studies. High heterogeneity between the studies impeded establishing a link between PROMs and prognostic value. Conclusion: There is low evidence supporting PROMs usage as prognostic tools in adult HT patients. Comparing outcomes of PROMS to routine prognostic in wider and systematic settings is warranted. Systematic use of PROMs in clinical settings is warranted.