Defining potential to benefit from implantable cardioverter defibrillator therapy: the role of biomarkers

Scott, Paul A., Townsend, Paul A., Ng, Leong L., Zeb, Mehmood, Harris, Scott, Roderick, Paul J., Curzen, Nick P. and Morgan, John M. (2011) Defining potential to benefit from implantable cardioverter defibrillator therapy: the role of biomarkers. Europace, 13 (10), 1419-1427. (doi:10.1093/europace/eur147). (PMID:21784745)

Abstract

Aims: Implantable cardioverter defibrillator (ICD) therapy improves survival in patients at high sudden cardiac death (SCD) risk. However, some patient groups fulfilling indications for ICD therapy may not gain significant benefit: patients whose absolute risk of SCD is low and patients whose risk of death even with an ICD is high. The value of biomarkers in identifying patients’ potential for survival benefit from ICD therapy is unknown. We performed a pilot study to investigate this.

Methods and results: Five established cardiovascular biomarkers were measured in patients with ICDs on the background of left ventricular dysfunction: N-terminal pro-brain natriuretic peptide [NT-proBNP], soluble ST2 [sST2], growth differentiation factor-15, C-reactive protein, and interleukin-6. The endpoints were all-cause mortality and survival with appropriate ICD therapy. One hundred and fifty-six patients were enrolled (age 69 years [Q1–Q3 62–77], 85% male, 76% ischaemic aetiology). During a follow-up of 15 ± 3 months, 12 patients died and 43 survived with appropriate ICD therapy. In a Cox proportional hazards model, the strongest predictors of death were Log sST2 (P< 0.001), serum creatinine (P< 0.001), and Log NT-proBNP (P= 0.002). The strongest predictor of survival with appropriate ICD therapy was Log NT-proBNP (P= 0.01).

Conclusion: The biomarkers NT-proBNP and sST2 are promising biomarkers for identifying patients with little potential to gain significant survival benefit from ICD therapy. However, their incremental benefit, in addition to currently available clinical risk prediction models, remains unclear. These results demand a confirmatory prospective cohort study, designed and powered to derive and validate prediction algorithms incorporating these markers.

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Contributors

Author: Paul J. Roderick

Author: Nick P. Curzen

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