Early inflammatory markers are independent predictors of cardiac allograft vasculopathy in heart-transplant recipients.

backgroundIdentification of risk is essential to prevent cardiac allograft vasculopathy (CAV) and graft failure due to CAV (GFDCAV) in heart transplant patients, which account for 30% of all deaths. Early CAV detection involves invasive, risky, and expensive monitoring approaches. We determined whether prediction of CAV and GFDCAV improves by adding inflammatory markers to a previously validated atherothrombotic (AT) model. METHODS AND

findingsAT and inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP) were measured in heart biopsies and sera of 172 patients followed prospectively for 8.9±5.0 years. Models were estimated for 5- and 10-year risk using (1) the first post-transplant biopsy only, or (2) all biopsies obtained within 3 months. Multivariate models were adjusted for other covariates and cross-validated by bootstrapping. After adding IL-6 and CRP to the AT models, we evaluated the significance of odds ratios (ORs) associated with the additional inflammatory variables and the degree of improvement in the area under the receiver operating characteristic curve (AUROC). When inflammatory markers were tested alone in prediction models, CRP (not IL-6) was a significant predictor of CAV and GFDCAV at 5 (CAV: p<0.0001; GFDCAV: p = 0.005) and 10 years (CAV: p<0.0001; GFDCAV: p = 0.003). Adding CRP (not IL-6) to the best AT models improved discriminatory power to identify patients destined to develop CAV (using 1st biopsy: p<0.001 and p = 0.001; using all 3-month biopsies: p<0.04 and p = 0.008 at 5- and 10-years, respectively) and GFDCAV (using 1st biopsy: 0.92 vs. 0.95 and 0.86 vs. 0.89; using all 3-month biopsies: 0.94 vs. 0.96 and 0.88 vs. 0.89 at 5- and 10-years, respectively), as indicated by an increase in AUROC.

conclusionsEarly inflammatory status, measured by a patient's CRP level (a non-invasive, safe and inexpensive test), independently predicts CAV and GFDCAV. Adding CRP to a previously established AT model improves its predictive power.