Development of a biomarker panel to predict cardiac resynchronization therapy response: Results from the SMART-AV
Francis G Spinale1, Timothy E Meyer2, Craig M Stolen2
1Cardiovascular Translational Research Center, University of South Carolina School of Medicine and Dorn VA Medical Center, Columbia, South Carolina; Medical University of South Carolina, Charleston, South Carolina.
Background:
Predicting a favorable cardiac resynchronization therapy (CRT) response holds great clinical importance.
Objective:
The purpose of this study was to examine proteins from broad biological pathways and develop a prediction tool for response to CRT.
Methods:
Plasma was collected from patients before CRT (SMART-AV [SmartDelay Determined AV Optimization: A Comparison to Other AV Delay Methods Used in Cardiac Resynchronization Therapy] trial). A CRT response was prespecified as a ≥15-mL reduction in left ventricular end-systolic volume at 6 months, which resulted in a binary CRT response (responders 52%, nonresponders 48%; n = 758).
Results:
Candidate proteins (n = 74) were evaluated from the inflammatory, signaling, and structural domains, which yielded 12 candidate biomarkers, but only a subset of these demonstrated predictive value for CRT response: soluble suppressor of tumorgenicity-2, soluble tumor necrosis factor receptor-II, matrix metalloproteinase-2, and C-reactive protein. These biomarkers were used in a composite categorical scoring algorithm (Biomarker CRT Score), which identified patients with a high/low probability of a response to CRT (P <.001) when adjusted for a number of clinical covariates. For example, a Biomarker CRT Score of 0 yielded 5 times higher odds of a response to CRT compared to a Biomarker CRT Score of 4 (P <.001). The Biomarker CRT Score demonstrated additive predictive value when considered against a composite of clinical variables.
Conclusion:
These unique findings demonstrate that developing a biomarker panel for predicting individual response to CRT is feasible and holds potential for point-of-care testing and integration into evaluation algorithms for patients presenting for CRT.
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