Current Emergency Department Disposition of Patients with Acute Heart Failure: An Opportunity for Improvement

Acute Heart Failure: From The Emergency Department to the Intensive Care Unit

Acute heart failure (AHF) is a frequent cause of hospitalization around the world and is associated with high in-hospital and post-discharge morbidity and mortality. This review summarizes data on diagnosis and management of AHF from the emergency department to the intensive care unit. While more evidence is needed to guide risk stratification and care of patients with AHF, hospitalization is a key opportunity to optimize evidence-based medical therapy for heart failure. Close linkage to outpatient care is essential to improve post-hospitalization outcomes.

Short-Stay Units vs Routine Admission From the Emergency Department in Patients With Acute Heart Failure: The SSU-AHF Randomized Clinical Trial

Importance

More than 80% of patients who present to the emergency department (ED) with acute heart failure (AHF) are hospitalized. With more than 1 million annual hospitalizations for AHF in the US, safe and effective alternatives are needed. Care for AHF in short-stay units (SSUs) may be safe and more efficient than hospitalization, especially for lower-risk patients, but randomized clinical trial data are lacking.

Objective

To compare the effectiveness of SSU care vs hospitalization in lower-risk patients with AHF.

Design, Setting, and Participants

This multicenter randomized clinical trial randomly assigned low-risk patients with AHF 1:1 to SSU or hospital admission from the ED. Patients received follow-up at 30 and 90 days post discharge. The study began December 6, 2017, and was completed on July 22, 2021. The data were analyzed between March 27, 2020, and November 11, 2023.

Intervention

Randomized post-ED disposition to less than 24 hours of SSU care vs hospitalization.

Main Outcomes and Measures

The study was designed to detect at least 1-day superiority for a primary outcome of days alive and out of hospital (DAOOH) at 30-day follow-up for 534 participants, with an allowance of 10% participant attrition. Due to the COVID-19 pandemic, enrollment was truncated at 194 participants. Before unmasking, the primary outcome was changed from DAOOH to an outcome with adequate statistical power: quality of life as measured by the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12). The KCCQ-12 scores range from 0 to 100, with higher scores indicating better quality of life.

Results

Of the 193 patients enrolled (1 was found ineligible after randomization), the mean (SD) age was 64.8 (14.8) years, 79 (40.9%) were women, and 114 (59.1%) were men. Baseline characteristics were balanced between arms. The mean (SD) KCCQ-12 summary score between the SSU and hospitalization arms at 30 days was 51.3 (25.7) vs 45.8 (23.8) points, respectively (P = .19). Participants in the SSU arm had 1.6 more DAOOH at 30-day follow-up than those in the hospitalization arm (median [IQR], 26.9 [24.4-28.8] vs 25.4 [22.0-27.7] days; P = .02). Adverse events were uncommon and similar in both arms.

Conclusions and Relevance

The findings show that the SSU strategy was no different than hospitalization with regard to KCCQ-12 score, superior for more DAOOH, and safe for lower-risk patients with AHF. These findings of lower health care utilization with the SSU strategy need to be definitively tested in an adequately powered study.

Trial Registration

ClinicalTrials.gov Identifier: NCT03302910.

Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department

Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.