Date: Monday, May 1, 2017
Session Time: 2:30pm-4:00pm
Presentation Time: 3:06pm-3:18pm
Introduction: Cardiac allograft vasculopathy (CAV) continues to limit the long-term success of heart transplantation. Although the progression of CAV can be slowed by tailored immunosuppression, the ability to predict which patients will develop CAV remains an unmet need. We have previously developed a prediction model for CAV using registry data. Here we sought to validate this model using angiographic data from our center.
Methods: A prediction model for CAV was developed using a cohort of 11,255 heart transplant recipients in the International Society for Heart and Lung Transplant Registry transplanted between 2000 and 2010. The model was developed using a penalized Cox proportional hazards method and contained 6 variables. No weighting was used. The study cohort included 406 heart transplant recipients transplanted between 01/01/2010 and 12/31/2014 at our center. Follow-up was assessed in August 2016. The presence of CAV was determined by coronary angiography performed yearly from post-heart transplantation years 1 through 6 then every two years. Model performance was assessed by the area under the receiver operating curve (AUC).
Results: The study cohort included 406 heart transplant recipients with a median follow-up time of 2.8 years (range 0 to 6.5 years) and a total follow-up of 1,177 person-years. A total of 68 patients (16.7%) developed CAV at a median of 2.2 years post-heart transplantation. The prediction model contained 6 variables: donor age, recipient body mass index, recipient diagnosis, donor/recipient sex, calcineurin-inhibitor agent and anti-proliferative agent at discharge. The predicted risk of CAV using the model was significantly higher for recipients that developed CAV as compared to those that did not (p = 0.003). The AUC for the CAV prediction model was 0.63 at 1 year post-heart transplantation and 0.74 at 5 years post-heart transplantation.
Conclusions: A six variable model for CAV predicts the development of CAV as determined using coronary angiography. Implementation of this straightforward model may help clinicians individualize therapies to slow CAV progression.
CITATION INFORMATION: Kransdorf E, Aintablian T, Kittleson M, Patel J, Kobashigawa J. Validation of a Prediction Model for Cardiac Allograft Vasculopathy. Am J Transplant. 2017;17 (suppl 3).
To cite this abstract in AMA style:Kransdorf E, Aintablian T, Kittleson M, Patel J, Kobashigawa J. Validation of a Prediction Model for Cardiac Allograft Vasculopathy. [abstract]. Am J Transplant. 2017; 17 (suppl 3). https://atcmeetingabstracts.com/abstract/validation-of-a-prediction-model-for-cardiac-allograft-vasculopathy/. Accessed August 3, 2021.
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