Eligibility and Potential Benefit of Transcatheter Edge-to-Edge Repair in a Contemporary Cohort With Heart Failure: Evidence From a Large Integrated Health Care Delivery System.

Background: The eligibility and potential benefit of transcatheter edge-to-edge repair (TEER) in addition to guideline-directed medical therapy to treat moderate-severe or severe secondary mitral regurgitation (MR) has not been reported in a contemporary heart failure (HF) population. Methods: Eligibility for TEER based on Food and Drug Administration (FDA) labeling: (1) HF symptoms, (2) moderate-severe or severe MR, (3) left ventricular ejection fraction (LVEF) 20% to 50%, (4) left ventricular end-systolic dimension 7.0 cm, and (5) receiving GDMT (blocker + angiotensin-converting enzyme inhibitor/angiotensin receptor blocker). The proportion (%) of patients eligible for TEER. The hypothetical number needed to treat to prevent or postpone adverse outcomes was estimated using relative risk reductions from published hazard ratios in the registration trial and the observed event rates. Results: We identified 50,841 adults with HF and known LVEF. After applying FDA criteria, 2461 patients (4.8%) were considered eligible for transcatheter mitral valve replacement (FDA+), with the vast majority of patients excluded (FDA-) based on a lack of clinically significant MR (N = 47,279). FDA+ patients had higher natriuretic peptide levels and were more likely to have a prior HF hospitalization compared to FDA- patients. Although FDA+ patients had a more dilated left ventricle and lower LVEF, median (25th-75th) left ventricular end-systolic dimension (cm) was low at 4.4 (3.7-5.1) and only 30.8% had severely reduced LVEF. FDA+ patients were at higher risk of HF-related morbidity and mortality. The estimated number needed to treat to potentially prevent or postpone all-cause hospitalization was 4.4, 8.8 for HF hospitalization, and 5.3 for all-cause death at 24 months in FDA+ patients. Conclusions: There is a low prevalence of TEER eligibility based on FDA criteria primarily due to absence of moderate-severe or severe MR. FDA+ patients are a high acuity population and may potentially derive a robust clinical benefit from TEER based on pivotal studies. Additional research is necessary to validate the scope of eligibility and comparative effectiveness of TEER in real-world populations.

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction.

Background There is a need to develop electronic health record-based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Results We studied adults with heart failure (HF) from 2011 to 2019 within an integrated health care delivery system. WHF encounters were ascertained using natural language processing and structured data. We conducted boosted decision tree ensemble models to predict 1-year hospitalizations, emergency department visits/observation stays, and outpatient encounters for WHF and all-cause death within each LVEF category: HF with reduced ejection fraction (EF) (LVEF <40%), HF with mildly reduced EF (LVEF 40%-49%), and HF with preserved EF (LVEF ≥50%). Model discrimination was evaluated using area under the curve and calibration using mean squared error. We identified 338 426 adults with HF: 61 045 (18.0%) had HF with reduced EF, 49 618 (14.7%) had HF with mildly reduced EF, and 227 763 (67.3%) had HF with preserved EF. The 1-year risks of any WHF event and death were, respectively, 22.3% and 13.0% for HF with reduced EF, 17.0% and 10.1% for HF with mildly reduced EF, and 16.3% and 10.3% for HF with preserved EF. The WHF model displayed an area under the curve of 0.76 and mean squared error of 0.13, whereas the model for death displayed an area under the curve of 0.83 and mean squared error of 0.076. Performance and predictors were similar across WHF encounter types and LVEF categories. Conclusions We developed risk prediction models for 1-year WHF events and death across the LVEF spectrum using structured and unstructured electronic health record data and observed no substantial differences in model performance or predictors except for death, despite differences in underlying HF cause.

Association Between Participation in a Heart Failure Telemonitoring Program and Health Care Utilization and Death Within an Integrated Health Care Delivery System.

BACKGROUND: The clinical usefulness of remote telemonitoring to reduce postdischarge health care use and death in adults with heart failure (HF) remains controversial. METHODS AND RESULTS: Within a large integrated health care delivery system, we matched patients enrolled in a postdischarge telemonitoring intervention from 2015 to 2019 to patients not receiving telemonitoring at up to a 1:4 ratio on age, sex, and calipers of a propensity score. Primary outcomes were readmissions for worsening HF and all-cause death within 30, 90, and 365 days of the index discharge; secondary outcomes were all-cause readmissions and any outpatient diuretic dose adjustments. We matched 726 patients receiving telemonitoring to 1985 controls not receiving telemonitoring, with a mean age of 75 ± 11 years and 45% female. Patients receiving telemonitoring did not have a significant reduction in worsening HF hospitalizations (adjusted rate ratio [aRR] 0.95, 95% confidence interval [CI] 0.68-1.33), all-cause death (adjusted hazard ratio 0.60, 95% CI 0.33-1.08), or all-cause hospitalization (aRR 0.82, 95% CI 0.65-1.05) at 30 days, but did have an increase in outpatient diuretic dose adjustments (aRR 1.84, 95% CI 1.44-2.36). All associations were similar at 90 and 365 days postdischarge. CONCLUSIONS: A postdischarge HF telemonitoring intervention was associated with more diuretic dose adjustments but was not significantly associated with HF-related morbidity and mortality.

Virtual Care Team Guided Management of Patients With Heart Failure During Hospitalization.

BACKGROUND: Scalable and safe approaches for heart failure guideline-directed medical therapy (GDMT) optimization are needed. OBJECTIVES: The authors assessed the safety and effectiveness of a virtual care team guided strategy on GDMT optimization in hospitalized patients with heart failure with reduced ejection fraction (HFrEF). METHODS: In a multicenter implementation trial, we allocated 252 hospital encounters in patients with left ventricular ejection fraction ≤40% to a virtual care team guided strategy (107 encounters among 83 patients) or usual care (145 encounters among 115 patients) across 3 centers in an integrated health system. In the virtual care team group, clinicians received up to 1 daily GDMT optimization suggestion from a physician-pharmacist team. The primary effectiveness outcome was in-hospital change in GDMT optimization score (+2 initiations, +1 dose up-titrations, -1 dose down-titrations, -2 discontinuations summed across classes). In-hospital safety outcomes were adjudicated by an independent clinical events committee. RESULTS: Among 252 encounters, the mean age was 69 ± 14 years, 85 (34%) were women, 35 (14%) were Black, and 43 (17%) were Hispanic. The virtual care team strategy significantly improved GDMT optimization scores vs usual care (adjusted difference: +1.2; 95% CI: 0.7-1.8; P < 0.001). New initiations (44% vs 23%; absolute difference: +21%; P = 0.001) and net intensifications (44% vs 24%; absolute difference: +20%; P = 0.002) during hospitalization were higher in the virtual care team group, translating to a number needed to intervene of 5 encounters. Overall, 23 (21%) in the virtual care team group and 40 (28%) in usual care experienced 1 or more adverse events (P = 0.30). Acute kidney injury, bradycardia, hypotension, hyperkalemia, and hospital length of stay were similar between groups. CONCLUSIONS: Among patients hospitalized with HFrEF, a virtual care team guided strategy for GDMT optimization was safe and improved GDMT across multiple hospitals in an integrated health system. Virtual teams represent a centralized and scalable approach to optimize GDMT.