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Standardized Team-Based Care for Cardiogenic Shock

Behnam N. Tehrani, Alexander George Truesdell, Matthew William Sherwood, Shashank Desai, Henry Tran, Kelly Epps, Ramesh Singh, Mitchell A. Psotka, Palak Mayur Shah, Lauren Beth Cooper, Carolyn Marie Rosner, Anika Raja, Scott Douglas Barnett, Patricia Saulino, Christopher R. deFilippi, Paul Alfred Gurbel, Charles E. Murphy, Christopher M. O’Connor

📄 Abstract

BACKGROUND: Cardiogenic shock (CS) is a multifactorial, hemodynamically complex syndrome associated with high mortality. Despite advances in reperfusion and mechanical circulatory support, management remains highly variable and outcomes poor. OBJECTIVES: This study investigated whether a standardized team-based approach can improve outcomes in CS and whether a risk score can guide clinical decision making. METHODS: A total of 204 consecutive patients with CS were identified. CS etiology, patient demographic characteristics, right heart catheterization, mechanical circulatory support use, and survival were determined. Cardiac power output (CPO) and pulmonary arterial pulsatility index (PAPi) were measured at baseline and 24 h after the CS diagnosis. Thresholds at 24 h for lactate (0.6 W), and PAPi (>1.0) were determined. Using logistic regression analysis, a validated risk stratification score was developed. RESULTS: Compared with 30-day survival of 47% in 2016, 30-day survival in 2017 and 2018 increased to 57.9% and 76.6%, respectively (p < 0.01). Independent predictors of 30-day mortality were age ≥71 years, diabetes mellitus, dialysis, ≥36 h of vasopressor use at time of diagnosis, lactate levels ≥3.0 mg/dl, CPO <0.6 W, and PAPi <1.0 at 24 h after diagnosis and implementation of therapies. Either 1 or 2 points were assigned to each variable, and a 3-category risk score was determined: 0 to 1 (low), 2 to 4 (moderate), and ≥5 (high). CONCLUSIONS: This observational study suggests that a standardized team-based approach may improve CS outcomes. A score incorporating demographic, laboratory, and hemodynamic data may be used to quantify risk and guide clinical decision-making for all phenotypes of CS.

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