scholarly journals Aortic pulsatility index predicts clinical outcomes in heart failure: a sub‐analysis of the ESCAPE trial

2021 ◽  
Vol 8 (2) ◽  
pp. 1522-1530
Author(s):  
Mark N. Belkin ◽  
Francis J. Alenghat ◽  
Stephanie A. Besser ◽  
Ann B. Nguyen ◽  
Ben B. Chung ◽  
...  
Keyword(s):  
Heart Failure ◽  
Clinical Outcomes ◽  
Pulsatility Index ◽  
Escape Trial

Abstract 14388: Aortic Pulsatility Index Strongly Predicts Clinical Outcomes in Heart Failure Patients: A Sub-analysis of the Escape Trial

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mark N Belkin ◽  
Francis J Alenghat ◽  
Stephanie Besser ◽  
Ann Nguyen ◽  
Bow Chung ◽  
...  
Keyword(s):  
Heart Failure ◽  
Pulmonary Artery ◽  
Cardiac Function ◽  
Clinical Outcomes ◽  
Pulsatility Index ◽  
Primary Outcome ◽  
Left Ventricular ◽  
Escape Trial ◽  
Heart Failure Patients ◽  
Hemodynamic Measurement

Introduction: Aortic pulsatility index (API), calculated as (systolic - diastolic blood pressure)/pulmonary capillary wedge pressure, is a novel hemodynamic measurement representing cardiac filling pressures and contractility. Hypothesis: API would better predict clinical outcomes than traditional hemodynamic metrics of cardiac function in decompensated heart failure patients. Methods: The Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE) trial individual-level data were used. API, cardiac power output (CPO), Fick cardiac index (CI), and pulmonary artery pulsatility index (PAPI) were calculated after final hemodynamic-monitored optimization. The primary outcome, assessed by univariable analysis, was combined death or need for heart transplant or left ventricular assist device at six months. Receiver operator characteristic (ROC) analyses were used to determine the cutoff value, from which Kaplan-Meier (KM) curves were constructed. Results: A total of 433 patients were enrolled in the ESCAPE trial, of which 155 had accurate final hemodynamic data. Of these, 45 (29%) experienced the primary outcome. Final API measurements predicted the primary outcome, OR 0.45 (95% CI 0.30-0.70, p<0.001), while CI, CPO, and PAPI did not. ROC analyses of final advanced hemodynamic measurements indicated API best predicted the primary outcome with a cutoff (sensitivity, specificity, correctly classified, AUC) of 2.9 (76.2%, 55.3%, 61.4%, 0.71), compared to CPO 0.69 (57.8%, 57.8%, 57.4%, 0.57), CI 2.2 (50.0%, 48.2%, 48.7%, 0.52), and PAPI 2.6 (60.5%, 64.5%, 63.3%, 0.64). KM analyses indicated API (83.5% vs 58.4%, p=0.001) and PAPI (78.3% vs 59.0%, p=0.03) were predictive of freedom from the primary outcome, but not CPO or CI. Conclusions: The novel hemodynamic measurement API better predicted clinical outcomes in the ESCAPE trial when compared to traditional invasive hemodynamic metrics of cardiac function.

Abstract 11924: Phenotypes of Heart Failure Identified via Cluster Analysis Do Not Recapitulate Hemodynamic Profiles: Insights From the ESCAPE Trial

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Tariq Ahmad ◽  
Allison Dunning ◽  
Phillip Schulte ◽  
Joseph Rogers ◽  
Christopher O’Connor ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cluster Analysis ◽  
Clinical Outcomes ◽  
Ischemic Cardiomyopathy ◽  
Proportional Hazards ◽  
Randomized Study ◽  
Cox Proportional Hazards ◽  
Escape Trial ◽  
Clinical Variables ◽  
Cluster 2

Background: Heart failure (HF) classification criteria do not adequately describe disease state. We used cluster analysis to identify distinct phenotypes of patients in the pulmonary artery catheter (PAC) arm of the ESCAPE trial, and examine association of clusters with hemodynamic profiles. Methods: Cluster analysis was performed on baseline clinical variables and PAC measurements in a cohort of 172 subjects from the ESCAPE trial, a randomized study examining PAC versus usual care in advanced systolic HF patients. ANOVA examined association between patient clusters and clinically determined hemodynamic profiles (warm/cold/wet/dry); association with clinical outcomes was assessed using Cox proportional hazards models. Results: Four HF clusters were identified with the following general characteristics: Cluster 1(n=75): Male Caucasians with ischemic cardiomyopathy, multiple comorbidities, lowest BNP levels; Cluster 2 (n=33): Females with non-ischemic cardiomyopathy, few comorbidities, most favorable hemodynamics; Cluster 3 (n=29): Young African American males with non-ischemic cardiomyopathy, most adverse hemodynamics and advanced disease; Cluster 4 (n=25): Older Caucasians with ischemic cardiomyopathy, concomitant renal insufficiency, and highest BNP levels. We noted no association between hemodynamic profiles and clusters (P=0.70). Cluster 4 patients had the highest risk of all adverse clinical outcomes, whereas cluster 2 had the lowest. Compared to patients in cluster 4, patients in other clusters had 45-70% lower risk of all-cause mortality. Conclusions: Using a novel approach that clusters patients according to similar clinical variables and PAC measures, we identified 4 clinically relevant phenotypes of HF patients, with no discernable relationship to hemodynamic profiles. Our analysis suggests that HF classification can be enhanced by simultaneous considerations of etiology, comorbid conditions, and biomarker levels.

Abstract 15111: Aortic Pulsatility Index Predicts Heart Failure Rehospitalization: A Sub-analysis of the Escape Trial

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Mark N Belkin ◽  
Francis J Alenghat ◽  
Stephanie Besser ◽  
Ann Nguyen ◽  
Bow Chung ◽  
...  
Keyword(s):  
Heart Failure ◽  
Blood Pressure ◽  
Pulmonary Artery ◽  
Pulsatility Index ◽  
Negative Binomial ◽  
Diastolic Pressure ◽  
Decompensated Heart Failure ◽  
Escape Trial ◽  
Hemodynamic Measurements ◽  
Hemodynamic Measurement

Introduction: Aortic pulsatility index (API), calculated as (systolic blood pressure - diastolic blood pressure)/pulmonary capillary wedge pressure (PCWP), is a novel hemodynamic measurement representing cardiac filling pressures and contractility. Hypothesis: API would predict heart failure hospitalizations in acutely decompensated heart failure participants in the Evaluation study of congestive heart failure and pulmonary artery catheterization effectiveness (ESCAPE) trial. Methods: From the ESCAPE trial individual-level data API, cardiac power output (CPO), and pulmonary artery pulsatility index (PAPI) were calculated, as well as reported routine invasive hemodynamics at baseline and after final hemodynamic-monitored optimization. Outcomes assessed were need for any rehospitalization and time to any first rehospitalization. Univariable analysis was conducted to assess rehospitalization. Negative binomial regression was used to analyze duration of time from discharge to first rehospitalization. Results: A total of 433 patients were enrolled in the ESCAPE trial. 189 patients had complete, accurate baseline hemodynamic data and were included in this analysis. No baseline hemodynamic measurements were associated with either outcome, except pulmonary artery (PA) diastolic pressure which predicted rehospitalization (OR 1.05 (95% CI 1.00-1.05, p= 0.02). Final API, OR 0.75 (95% CI 0.60-1.00, p= 0.03) and PAPI, OR 0.90 (95% CI 0.80-1.00, p= 0.03) predicted the need for any rehospitalization. Final API, OR 0.84 (95% CI 0.73-0.97, p= 0.02), and PA diastolic pressure, OR 1.03 (95% CI 1.01-1.06, p= 0.02), were associated with duration of time from discharge to any first hospitalization. Conclusions: The novel hemodynamic measurement API better predicted rehospitalization and time to rehospitalization in the ESCAPE trial when compared to routine, and other advanced invasive hemodynamic measurements.

Sign in / Sign up

Export Citation Format

Share Document