Predictive Utility of Remote Pacing Diagnostics in Identifying Non-Responders to Cardiac Resynchronization Therapy (CRT)

Abstract

Background: Despite its proven efficacy, a significant fraction of patients receiving Cardiac Resynchronization Therapy (CRT) remains non-responsive, severely compromising prognosis. Early identification of these non-responders is crucial, but conventional follow-up schedules often delay necessary optimization and therapeutic adjustments. We investigated whether data derived from continuous remote device monitoring (RDM) could provide the necessary granularity to predict CRT non-response significantly earlier than standard clinical metrics.

 Methods: A prospective cohort study enrolled 285 chronic Heart Failure patients with HFrEF and wide QRS complex receiving an implanted CRT device across three regional specialized clinics. All patients underwent continuous RDM, recording pacing percentage (Vp%), atrial fibrillation (AF) burden, and right ventricular (RV) lead impedance stability. The primary endpoint was defined as clinical non-response (failure to achieve a 10% increase in LVEF or reduction in NYHA class) at six months. Univariate and subsequent multivariate Kaplan-Meier survival analysis was employed to assess time-to-non-response prediction.  

 Results: Clinical non-response was observed in 28% (N=80) of the cohort. RDM data analysis revealed that a drop in Vp% below 92% combined with a 2-fold increase in daily AF burden within the first 60 days post-implant was independently associated with non-response. The multivariate model demonstrated that this combined RDM metric predicted non-response with a sensitivity of 88% and specificity of 79%, significantly exceeding the predictive power of baseline clinical or echocardiographic factors alone (p<0.001). Furthermore, the RDM signature identified eventual non-responders a median of 95 days earlier than standard clinical assessment, allowing for timely therapeutic adjustments.

 Conclusion: Integrating these precise metrics into clinical workflows facilitates proactive, personalized device management, drastically shortening the time to necessary intervention. This strategy aligns perfectly with the regional goal of establishing advanced, data-led risk and disease detection, enhancing the Quality of Care in complex HF management.