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Background Patients with heart failure (HF) in skilled nursing facilities (SNFs) have 30-day hospital readmission rates as high as 43%. A virtual cardiovascular care program, consisting of patient selection, initial televisit, postconsultation care planning, and follow-up televisits, was developed and delivered by Heartbeat Health, Inc., a cardiovascular digital health company, to 11 SNFs (3510 beds) in New York. The impact of this program on the expected SNF 30-day HF readmission rate is unknown, particularly in the COVID-19 era. Objective The aim of the study was to assess whether a virtual cardiovascular care program could reduce the 30-day hospital readmission rate for patients with HF discharged to SNF relative to the expected rate for this population. Methods We performed a retrospective case review of SNF patients who received a virtual cardiology consultation between August 2020 and February 2021. Virtual cardiologists conducted 1 or more telemedicine visit via smartphone, tablet, or laptop for cardiac patients identified by a SNF care team. Postconsult care plans were communicated to SNF clinical staff. Patients included in this analysis had a preceding index admission for HF. Results We observed lower hospital readmission among patients who received 1 or more virtual consultations compared with the expected readmission rate for both cardiac (3% vs 10%, respectively) and all-cause etiologies (18% vs 27%, respectively) in a population of 3510 patients admitted to SNF. A total of 185/3510 patients (5.27%) received virtual cardiovascular care via the Heartbeat Health program, and 40 patients met study inclusion criteria and were analyzed, with 26 (65%) requiring 1 televisit and 14 (35%) requiring more than 1. Cost savings associated with this reduction in readmissions are estimated to be as high as US $860 per patient. Conclusions The investigation provides initial evidence for the potential effectiveness and efficiency of virtual and digitally enabled virtual cardiovascular care on 30-day hospital readmissions. Further research is warranted to optimize the use of novel virtual care programs to transform delivery of cardiovascular care to high-risk populations.

Introduction: CRT optimizations has the potential to improve the 30% non-response rate. Randomized trials (RCTs) comparing device and echo-guided CRT optimization have been overall small and results have been conflicting. Methods: We searched the EMBASE and MEDLINE databases from January 2008 through September 2020 for RCTs and prospective observational studies that randomized patients to either echo-guided or device-based CRT optimization. Included studies had to report on at least one of the following outcomes of interest: all-cause mortality, heart failure hospitalization (HFH), NYHA class improvement, mean EF at follow up, LVEDD, 6MWD, and QoL. A meta-analyses on the outcomes of interest was performed using random effects models. Results: We included seven RCTs representing a total of 2,730 patients. Of those, 1,113 patients were randomized to echo-guided optimization, and 1,617 were randomized to device-based optimization. The mean age of patients was 64.5 years, and they were predominantly men (60%). In the device-based optimization group, there was 93 deaths (5.7%) and 142 HFH (5.1%). In the echo-guided optimization group, there was 57 deaths (10.2%) and 112 HFH (12.4%). On random effects meta-analysis, the device-based optimization group had a lower rate of HFH compared with the echo-guided optimization group (OR 0.81, 95% CL 0.70-0.95, Figure). However, differences in the following outcomes between the two groups were not statistically significant: all-cause mortality, mean follow up EF, LVEDD, NYHA class improvement, 6MWD improvement range or QoL. Conclusions: This meta-analysis suggests that echo-guided optimization of CRT is equivalent to device-based optimization in relation to the clinical outcomes of death, EF, LVEDD, NYHA and 6MWD but suggests that device-related optimization could reduce HFH. The latter merits further investigation in relation to different device-base optimization algorithms.

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