Growth differentiation factor-15 predicts mortality and morbidity after cardiac resynchronization therapy

生长分化因子-15 预测心脏再同步治疗后的死亡率和发病率

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作者:Paul W X Foley, Berthold Stegemann, Kelvin Ng, Sud Ramachandran, Anthony Proudler, Michael P Frenneaux, Leong L Ng, Francisco Leyva

Aims

The aim of this study was to determine whether growth differentiation factor-15 (GDF-15) predicts mortality and morbidity after cardiac resynchronization therapy (CRT). Growth differentiation factor-15, a transforming growth factor-beta-related cytokine which is up-regulated in cardiomyocytes via multiple stress pathways, predicts mortality in patients with heart failure treated pharmacologically.

Conclusion

Pre-implant GDF-15 is a strong predictor of mortality and morbidity after CRT, independent of NT pro-BNP. The predictive value of these analytes is enhanced by combined measurement.

Results

Growth differentiation factor-15 was measured before and 360 days (median) after implantation in 158 patients with heart failure [age 68 +/- 11 years (mean +/- SD), left ventricular ejection fraction (LVEF) 23.1 +/- 9.8%, New York Class Association (NYHA) class III (n = 117) or IV (n = 41), and QRS 153.9 +/- 28.2 ms] undergoing CRT and followed up for a maximum of 5.4 years for events. In a stepwise Cox proportional hazards model with bootstrapping, adopting log GDF-15, log NT pro-BNP, LVEF, and NYHA class as independent variables, only log GDF-15 [hazard ratio (HR), 3.76; P = 0.0049] and log NT pro-BNP (HR, 2.12; P = 0.0171) remained in the final model. In the latter, the bias-corrected slope was 0.85, the optimism (O) was -0.06, and the c-statistic was 0.74, indicating excellent internal validity. In univariate analyses, log GDF-15 [HR, 5.31; 95% confidence interval (CI), 2.31-11.9; likelihood ratio (LR) chi(2) = 14.6; P < 0.0001], NT pro-BNP (HR, 2.79; 95% CI, 1.55-5.26; LR chi(2) = 10.4; P = 0.0004), and the combination of both biomarkers (HR, 7.03; 95% CI, 2.91-17.5; LR chi(2) = 19.1; P < 0.0001) emerged as significant predictors. The biomarker combination was associated with the highest LR chi(2) for all endpoints.

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