Cardiac power efficiency as a hemodynamic predictor of outcomes in congestive heart failure
Symalla T, Narang N, Jeevanandam V. Cardiac power efficiency as a hemodynamic predictor of outcomes in congestive heart failure. The Journal of heart and lung transplantation. 2020;39(4):S53.
Copyright © 2020. Published by Elsevier Inc. PURPOSE: Cardiac power index (CPI) has been used as a predictor of outcomes in cardiogenic shock. Cardiac power efficiency (CPE), which is the CPI divided by the PCWP, has been used successfully to predict response to a durable counterpulsation MCS device. The aim of this study is to evaluate the utility of CPE in predicting outcomes in advanced heart failure patients presenting with decompensation. METHODS: This is a single institution, retrospective review of 128 advanced heart failure patients who presented with decompensation. A right heart catheterization was performed upon admission. Patients were separated into two groups: those who survived 30 days and were only on optimal medical therapy and those who experienced "cardiac failure", including death, hospice, transplantation, LVAD implantation, or inotrope dependence. Statistical analysis included multivariate regression controlling for age, race, sex, BMI, BNP, heart failure etiology, ejection fraction, acute or chronic heart failure, and GFR. RESULTS: In this 128 patient cohort, 83 (64.8%) progressed to "cardiac failure". Multiple hemodynamic parameters were statistically significant between the two groups (Table 1). Multivariate regression showed CPE (p=0.006) was the only significant predictor of "cardiac failure". CPI (p=0.052) was not significant when used in the regression model. Sensitivity analysis was performed at multiple CPE cutoffs and the highest sensitivity (75.3%) was obtain at a CPE cutoff of 0.0178. CONCLUSION: Cardiac power efficiency is a novel hemodynamic parameter which has the potential to identify patients in cardiogenic shock who will not survive with medical therapy alone. Larger studies are needed to better identify CPE cutoffs which predict greatest risk.