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Utilizing Percutaneous Ventricular Assist Devices in Acute Myocardial Infarction Complicated by Cardiogenic Shock
Published on: June 12, 2021
Risk score derived from pre-operative data analysis predicts the need for biventricular mechanical circulatory
J Raymond Fitzpatrick1, John R Frederick, Vivian M Hsu
1Division of Cardiovascular Surgery, Hospital of University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Summary
Predicting right ventricular failure after left ventricular assist device (LVAD) implantation is crucial. Key predictors include low cardiac index, RV dysfunction, high creatinine, and previous cardiac surgery.
Area of Science:
- Cardiology
- Cardiovascular Surgery
- Medical Device Technology
Background:
- Right ventricular (RV) failure is a significant complication following left ventricular assist device (LVAD) implantation.
- Accurate prediction of post-LVAD RV failure is critical, especially for destination therapy and total artificial heart applications.
Purpose of the Study:
- To identify pre-operative risk factors for the need for RV assist device (RVAD) placement in patients undergoing LVAD implantation.
- To develop a predictive algorithm for RVAD requirement post-LVAD.
Main Methods:
- Retrospective review of 266 patients who underwent LVAD placement between April 1995 and June 2007.
- Comparison of 36 pre-operative parameters between patients who did and did not require RVAD (BiVAD).
- Univariate and multivariate logistic regression analyses to identify significant predictors.
Main Results:
- 37% (99/266) of LVAD recipients required RVAD placement.
- Multivariate analysis identified cardiac index ≤2.2 L/min/m², RV stroke work index ≤0.25 mm Hg·L/m², severe pre-operative RV dysfunction, creatinine >1.9 mg/dL, prior cardiac surgery, and systolic blood pressure ≤96 mm Hg as significant predictors.
- These factors demonstrated high odds ratios for predicting RVAD need.
Conclusions:
- Cardiac index, RV stroke work index, pre-operative RV dysfunction, creatinine, previous cardiac surgery, and systolic blood pressure are key predictors of RVAD need.
- An algorithm incorporating these factors can predict RVAD requirement with >80% sensitivity and specificity.