Association between cardiogenic shock phenotype, vasopressor requirements and mortality

Abstract Background Greater vasopressor requirements predict higher mortality in patients with cardiogenic shock (CS).(1) The extent to which CS phenotype modifies the relationship between vasopressor dose and mortality risk is unknown. Purpose We investigated the association between in-hospital mortality and vasopressor dosing (expressed as norepinephrine equivalents [NEE]) during the first 24 hours of hospitalization across distinct CS phenotypes. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. Peak combined vasopressor doses during the first 24 hours of hospitalization were recorded as NEE. High dose vasopressor (HDV) use was defined as peak 24 hour NEE >0.3 mcg/kg/min. Unsupervised machine learning was used to assign patients to one of three phenotypes (non-congested, cardiorenal, and haemometabolic), as reported previously by our group and others.(2,3) The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant covariates. Results A total of 1078 patients with CS (median age 69 years, 37% female, 59% with acute myocardial infarction) were admitted to our cardiac intensive care unit (CICU) between 2017 and 2015. The number of patients assigned to each CS phenotype were: haemometabolic (n=325, 30%), cardiorenal (n=314, 29%), non-congested (439, 41%). There was a strong incremental association between in-hospital mortality and NEE (Fig. 1, adjusted OR 1.104 per 0.1 mcg/kg/min, 95% CI 1.134–1.231, P<0.001), and patients receiving HDV (n=234, 22%) had significantly higher in-hospital mortality (57% vs 28%, p<0.001). Among patients who did not receive HDV, mortality was higher in haemometabolic CS (49%) compared to 24% in cardiorenal CS and 21% in non-congested CS (adjusted OR 3.58 relative to non-congested CS, 95% CI 2.46-5.23, p<0.001, Fig. 1). Among patients receiving HDV, hemometabolic CS was also associated with higher mortality (68%) than those with cardiorenal (46.2%) or non-congested CS (39.3%), (adjusted OR 3.26 relative to non-congested CS, 95% CI 1.75-6.18, P<0.001, Fig. 1). Conclusion Integration of CS phenotype with vasopressor requirements may provide incremental prognostic information in CS during the first 24 hours of admission, with haemometabolic CS portending a greater mortality risk even at lower NEE. Patients with HDV (particularly those requiring >=1 mcg/kg/min NEE) had high mortality irrespective of CS phenotype.Figure 1

Authors

Institutions

Publication Details

Journal
European Heart Journal
Published
2024-10-01
DOI
https://doi.org/10.1093/eurheartj/ehae666.1751
Citations
1
Primary Topic
Cardiac Arrest and Resuscitation
Type
article
Field-Weighted Citation Impact
0.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association between cardiogenic shock phenotype, vasopressor requirements and mortality

Jacob Colin Jentzer, Dhruv Sarma, Kianoush Kashani, Benjamin Hibbert
1 citations
European Heart Journal
Cardiac Arrest and Resuscitation
0.69
article

Association between cardiogenic shock phenotype, vasopressor requirements and mortality

Jacob Colin Jentzer, Dhruv Sarma, Kianoush Kashani, Benjamin Hibbert
article en
1 citations

Abstract

Abstract Background Greater vasopressor requirements predict higher mortality in patients with cardiogenic shock (CS).(1) The extent to which CS phenotype modifies the relationship between vasopressor dose and mortality risk is unknown. Purpose We investigated the association between in-hospital mortality and vasopressor dosing (expressed as norepinephrine equivalents [NEE]) during the first 24 hours of hospitalization across distinct CS phenotypes. Methods We included patients with CS admitted to our cardiac intensive care unit (CICU) between 2007 and 2015. Peak combined vasopressor doses during the first 24 hours of hospitalization were recorded as NEE. High dose vasopressor (HDV) use was defined as peak 24 hour NEE >0.3 mcg/kg/min. Unsupervised machine learning was used to assign patients to one of three phenotypes (non-congested, cardiorenal, and haemometabolic), as reported previously by our group and others.(2,3) The primary outcome was in-hospital mortality. Multivariable logistic regression was used to adjust for relevant covariates. Results A total of 1078 patients with CS (median age 69 years, 37% female, 59% with acute myocardial infarction) were admitted to our cardiac intensive care unit (CICU) between 2017 and 2015. The number of patients assigned to each CS phenotype were: haemometabolic (n=325, 30%), cardiorenal (n=314, 29%), non-congested (439, 41%). There was a strong incremental association between in-hospital mortality and NEE (Fig. 1, adjusted OR 1.104 per 0.1 mcg/kg/min, 95% CI 1.134–1.231, P<0.001), and patients receiving HDV (n=234, 22%) had significantly higher in-hospital mortality (57% vs 28%, p<0.001). Among patients who did not receive HDV, mortality was higher in haemometabolic CS (49%) compared to 24% in cardiorenal CS and 21% in non-congested CS (adjusted OR 3.58 relative to non-congested CS, 95% CI 2.46-5.23, p<0.001, Fig. 1). Among patients receiving HDV, hemometabolic CS was also associated with higher mortality (68%) than those with cardiorenal (46.2%) or non-congested CS (39.3%), (adjusted OR 3.26 relative to non-congested CS, 95% CI 1.75-6.18, P<0.001, Fig. 1). Conclusion Integration of CS phenotype with vasopressor requirements may provide incremental prognostic information in CS during the first 24 hours of admission, with haemometabolic CS portending a greater mortality risk even at lower NEE. Patients with HDV (particularly those requiring >=1 mcg/kg/min NEE) had high mortality irrespective of CS phenotype.Figure 1

European Heart JournalVol. 45(Supplement_1)
Mayo Clinic (US)
Openalex Percentile: Top 25%
Cardiac Arrest and Resuscitation
0.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.