Assessment of a novel device-based diagnostic algorithm to monitor patient status in moderate-to-severe heart failure: rationale and design of the CLEPSYDRA study


    loading  Checking for direct PDF access through Ovid

Abstract

AimsMonitoring systems integrated into electronic implantable devices for heart failure (HF) have significantly expanded the possibility of obtaining diagnostic information and can be used to enhance patient follow-up. The ability to obtain advance warning of worsening HF is currently being explored using a variety of diagnostic parameters. A novel device-based algorithm, physiological diagnostic (PhD), combines data from minute ventilation and accelerometer sensors to provide an indicator of the overall status of HF patients and detect clinically relevant acute HF events. The objective of this study was to evaluate the effectiveness of the PhD algorithm for detecting HF events in patients with HF.MethodsCLEPSYDRA is a multicentre, prospective, non-randomized, single-arm double-blinded study in 62 centres in Europe, the USA, and Canada. Patients with moderate-to-severe HF, on stable optimal pharmacological therapy, QRS ≥ 120 ms, and ejection fraction ≤0.35% will be included. Patients will be followed at 3-month intervals until study end, or for a minimum of 13 months. The primary endpoint is the sensitivity of the PhD (proportion of HF-related clinical events occurring within a 4-week period after a PhD HF indication). Secondary endpoints include the sensitivity of PhD with regard to HF events related to oral treatment modification, and adverse events. The first patient was included in October 2009. At the time of manuscript submission (Week 26, 2010), 214 patients had been enrolled. Study results are expected in 2012.PerspectiveCLEPSYDRA will provide essential data on the utility of the PhD algorithm in a HF population with blinded investigators and patients. A successful outcome will demonstrate the potential for the algorithm to be implemented in clinical practice. This would improve clinical management and further the ability to generate dynamic and reliable risk profiles for patients with HF.

    loading  Loading Related Articles