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Revised screening scale to predict survival after insertion of a left ventricular assist device
Authors:Rao Vivek  Oz Mehmet C  Flannery Margaret A  Catanese Katharine A  Argenziano Michael  Naka Yoshifumi
Institution:Division of Cardiothoracic Surgery, New York Presbyterian Hospital and Columbia University, New York, NY, USA. vivek.rao@uhn.on.ca
Abstract:BACKGROUND: We previously calculated a risk factor summation score that successfully predicted survival after insertion of a left ventricular assist device. We sought to validate our previous score by using a single center's clinical experience and to determine emerging risk factors for mortality after device insertion. METHODS: The clinical records of 130 consecutive patients who received the HeartMate VE left ventricular assist device (Thoratec Corp, Pleasanton, Calif) at our institution between June 1996 and March 2001 as a bridge to transplantation were reviewed. Univariate and multivariable analyses were performed to determine the predictors of operative mortality after device insertion. Using the relative risks for each identified variable, we devised a new risk factor summation score. The new and old scores were then compared by using linear regression analyses to determine whether the revised score improved statistical accuracy. RESULTS: Overall operative mortality was 25% (n = 33). The old score successfully predicted operative mortality in the current patient population (operative mortality of 38% for score >5 vs 13% for score < or =5). However, the revised score improved risk discrimination (operative mortality of 46% for a score >5 vs 12% for a score < or = to 5). Statistical accuracy was comparable between scores, but the relationship between observed and predicted outcomes was improved with the revised score. CONCLUSIONS: The changing demographic profile and management of patients presenting for mechanical circulatory support has led to a change in the predictors of mortality after device insertion. Periodic remodeling and recalibration of risk indices helps to accurately predict outcomes in high-risk patient groups and identifies emerging risk factors for mortality.
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