• J. Heart Lung Transplant. · Dec 2008

    Risk score derived from pre-operative data analysis predicts the need for biventricular mechanical circulatory support.

    • J Raymond Fitzpatrick, John R Frederick, Vivian M Hsu, Elliott D Kozin, Mary Lou O'Hara, Elan Howell, Deborah Dougherty, Ryan C McCormick, Carine A Laporte, Jeffrey E Cohen, Kevin W Southerland, Jessica L Howard, Mariell L Jessup, Rohinton J Morris, Michael A Acker, and Y Joseph Woo.
    • Division of Cardiovascular Surgery, Hospital of University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
    • J. Heart Lung Transplant. 2008 Dec 1;27(12):1286-92.

    BackgroundRight ventricular (RV) failure after left ventricular assist device (LVAD) placement is a serious complication and is difficult to predict. In the era of destination therapy and the total artificial heart, predicting post-LVAD RV failure requiring mechanical support is extremely important.MethodsWe reviewed patient characteristics, laboratory values and hemodynamic data from 266 patients who underwent LVAD placement at the University of Pennsylvania from April 1995 to June 2007.ResultsOf 266 LVAD recipients, 99 required RV assist device (BiVAD) placement (37%). We compared 36 parameters between LVAD (n = 167) and BiVAD patients (n = 99) to determine pre-operative risk factors for RV assist device (RVAD) need. By univariate analysis, 23 variables showed statistically significant differences between the two groups (p < or = 0.05). By multivariate logistic regression, cardiac index < or =2.2 liters/min/m(2) (odds ratio [OR] 5.7), RV stroke work index < or =0.25 mm Hg . liter/m(2) (OR 5.1), severe pre-operative RV dysfunction (OR 5.0), pre-operative creatinine > or =1.9 mg/dl (OR 4.8), previous cardiac surgery (OR 4.5) and systolic blood pressure < or =96 mm Hg (OR 2.9) were the best predictors of RVAD need.ConclusionsThe most significant predictors for RVAD need were cardiac index, RV stroke work index, severe pre-operative RV dysfunction, creatinine, previous cardiac surgery and systolic blood pressure. Using these data, we constructed an algorithm that can predict which LVAD patients will require RVAD with >80% sensitivity and specificity.

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