Early economic evaluation of short-term variability of QT intervals as a potential test for predicting the effectiveness of ICD implantation in patients with heart failure

Objectives: Implantable cardioverter-defibrillators (ICD) improve survival in heart failure patients with New York Heart Association Functional Classification (NYHA) class III/IV symptoms and a left ventricular ejection fraction (LVEF) of ≤ 35%. Therefore, the current clinical guidelines recommend the implantation of the ICD in all the patients that fulfill the above conditions. However, as the costs of the ICD devices are relatively high, the prophylactic implantation of ICDs as per the guideline is not particularly cost-effective. A previous study demonstrated that short-term variability of QT (STVQT) may in fact reflect the increased susceptibility to SCD in patients with DC since it was shown that patients with DC and HF had the highest STVQT. The objective of this study was to explore the cost-effectiveness of shortterm variability of QT (STVQT) as a biomarker for implanting ICDs as secondary prevention of SCDs in heart failure patients and assess the societal value (headroom) of a hypothetical perfect STVQT biomarker for risk assessment and subsequent treatment decision in implanting ICDs . Methods: The study uses a decision analytical model. Results: The use of the QT (STVQT) as a biomarker for implanting ICDs as secondary prevention of SCDs in heart failure patients is a cost-effective healthcare intervention. The headroom (societal willingness to pay multiplied by incremental quality-adjusted life years) available for the hypothetical perfect biomarker ranged from € 483 to € 3,868. Conclusions: The use of the QT (STVQT) as a biomarker for implanting ICDs is cost effective. Identification of patients with high-risk for SCD and implantation of ICDs only on those patients could potentially save substantial costs. However, further research of risk stratifying biomarkers test accuracy is needed to support and strengthen the results of this modeling study