Use of Analytics to Identify Patients with Sub-Optimal Cardiac Resynchronization Therapy Pacing

Abstract

Introduction Heart failure (HF) hospitalizations are of growing concern for patients and payers. Cardiac resynchronization therapy patients that achieve high CRT pacing (>90%) are at lower risk of HF hospitalization. There has been no way to identify patients with suboptimal CRT pacing outside of routine office or remote follow up. Analytic software has been developed to identify programming optimization opportunities at a clinic level. Hypothesis Cardiac device analytic software can identify opportunities for programming optimization and enable improvements in CRT pacing percentage. Methods Cardiac device analytic software (Medtronic PaceartTM Analytics) was used to identify CRT patients with suboptimal CRT pacing at a single center. Patients actively followed within the clinic were included. Patients with CRT pacing programmed off, LV leads off due to diaphragmatic stimulation, or who declined further procedures or more aggressive medical therapy were not included. Reduction in HF related healthcare utilization was modeled based on expected reduction in the risk of HF hospitalizations with increased CRT pacing percentage. Cost savings were estimated using national claims payment data. Results A total of 88 out of 642 patients were identified with suboptimal CRT pacing through the analytic tool. There were 42 patients not eligible for improvement for reasons outlined above; 46 patients were considered eligible for intervention. The mean (standard error) baseline CRT pacing was 71.6% (5.5%). With intervention, 21 patients (45.6%) improved CRT pacing to 94.1% (0.6%). 11 patients improved with control of atrial fibrillation, 9 with treatment of PVC's, and 1 with device reprogramming. This improvement of 22.5% was modeled to prevent 7 HF hospitalizations in 1 year, equating to $117,268 in potential savings to the healthcare system from a payer perspective. Conclusions Analytics data can be used to successfully identify patients that have suboptimal CRT pacing and may lead to cost savings through reducing unnecessary HF hospitalizations