Pulmonary artery elastance as a predictor of hospital mortality in heart failure cardiogenic shock

Abstract

Abstract Aims The initial bundle of cares strongly affects haemodynamics and outcomes in acute decompensated heart failure cardiogenic shock (ADHF‐CS). We sought to characterize whether 24 h haemodynamic profiling provides superior prognostic information as compared with admission assessment and which haemodynamic parameters best predict in‐hospital death. Methods and results All patients with ADHF‐CS and with available admission and 24 h invasive haemodynamic assessment from two academic institutions were considered for this study. The primary endpoint was in‐hospital death. Regression analyses were run to identify relevant predictors of study outcome. We included 127 ADHF‐CS patients [65 (inter‐quartile range 52–72) years, 25.2% female]. Overall, in‐hospital mortality occurred in 26.8%. Non‐survivors were older, with greater CS severity. Among admission variables, age [odds ratio (OR) = 1.06; 95% confidence interval (CI): 1.02–1.11; Padj = 0.005] and CPIRAP (OR = 0.62 for 0.1 increment; 95% CI: 0.39–0.95; Padj = 0.034) were found significantly associated with in‐hospital death. Among 24 h haemodynamic univariate predictors of in‐hospital death, pulmonary elastance (PaE) was the strongest (area under the curve of 0.77; 95% CI: 0.68–0.86). PaE (OR = 5.98; 95% CI: 2.29–17.48; Padj < 0.001), pulmonary artery pulsatility index (PAPi, OR = 0.77; 95% CI: 0.62–0.92; Padj = 0.013) and age (OR = 1.06; 95% CI: 1.02–1.11; Padj = 0.010) were independently associated with in‐hospital death. Best cut‐off for PaE was 0.85 mmHg/mL and for PAPi was 2.95; cohort phenotyping based on these PaE and PAPi thresholds further increased in‐hospital death risk stratification; patients with 24 h high PaE and low PAPi exhibited the highest in‐hospital mortality (56.2%). Conclusions Pulmonary artery elastance has been found to be the most powerful 24 h haemodynamic predictor of in‐hospital death in patients with ADHF‐CS. Age, 24 h PaE, and PAPi are independently associated with hospital mortality. PaE captures ventricular (RV) afterload mismatch and PAPi provides a metric of RV adaptation, thus their combination generates four distinct haemodynamic phenotypes, enhancing in‐hospital death risk stratification.

Keywords

• Cardiac power index
• CPI
• Cardiac power output
• Cardiogenic shock
• Pulmonary artery catheter
• Haemodynamic monitoring