Abstract 20672: Dynamic Phenommapping in Heart Transplantation Predicts Clinical Outcomes
Introduction: A major limitation of long-term survival in Heart Transplantation (HTx) is determined by the effects of the immune system on the cardiac allograft as well as the consequences of the biological changes related to immunosuppression. Thus, a major goal of post-transplant management is to minimize the risk of complications related to immune deficiency, alloreactivity and side effects of therapies.
Hypothesis: We hypothesized that the time dependent change in the post transplant immunophenomap is associated with differential clinical outcomes in HTx.
Methods: We prospectively collected and retrospectively analyzed data from all 101 adult HTx patients with a total of 1557 encounters who underwent HTx between January 2010 and April 2013. Data collected included clinical and immune variables such as BNP, CD4 T-Cell ATP content (Cylex), Gene expression (Allomap test), anti HLA Class I and II antibodies and immunosuppressive drugs levels. Multivariate analysis examined the slopes and intercepts of the medications and biomarkers using Welch’s t-test, and a cox proportional hazards model was used to assess the association of the slopes and intercepts with the combined endpoint of rejection, vasculopathy, allograft failure or death.
Results: Association between slopes of immunosuppressant and biomarkers on combined endpoints showed that for every 1 unit increase in the slope of CNI level, there was19 % decrease in the risk of developing a combined endpoint (hazard ratio 0.81 (0.68, 0.97) p=0.021). Evaluation of Cylex, BNP, and WBC slopes suggested for every 1unit increase in the slope, there was a 19, 39, and 25% lower risk (hazard ratio Cylex 0.81 (0.69, 0.95) p=0.008; hazard ratio BNP, 0.61 (0.42, 0.88) p=0.009; hazard ratio WBC, 0.75 (0.63, 0.88) p=0.001). Clustered immunosuppression trajectories identified different risk groups (p<0.001).
Conclusions: Understanding the time dependent immunophenomap on HTx outcomes has the potential to improve clinical care and guide decision making. Our study demonstrate the value of big data implementations to improve evaluation and management of patients post HTx.
Author Disclosures: M. Bakir: None. N. Jackson: None. C. Tseng: None. E. Chang: None. D. Henriquez-Ticas: None. M. Deng: None. M. Cadeiras: None.
- © 2016 by American Heart Association, Inc.