Use of Imaging-guided Decongestion for Reducing Heart Failure Readmission and Death in High-risk Patients: A Multi-site Randomized Trial of a Nurse-led Strategy at the Point of Care.

BACKGROUND: Nurse-led disease management programs (DMPs) decrease readmission after acute decompensated heart failure (HF). We sought whether readmissions could be further reduced by lung ultrasound (LUS)-guided decongestion before discharge and during DMP. METHODS AND RESULTS: Of 290 patients hospitalized with acute decompensated HF, 122 at high risk for readmission or mortality were randomized to receive usual care (UC) (n = 64) or UC plus intervention (DMP-Plus) (n = 58), comprising LUS-guided management before discharge and during at-home follow-up. Residual congestion was identified by ≥10 B-lines detected in 8 lung zones. The outcomes included a composite of readmission and/or mortality at 30 and 90 days, and 90-day HF readmission. Residual congestion was detected equally among the patient groups. The 30-day composite outcome occurred in 28% DMP-plus patients and 22% UC patients (odd ratio [OR], 1.36; 95% confidence interval [CI], 0.59-3.1; P = .5) and the 90-day HF readmission outcome occurred in 22% and 31%, respectively (odds ratio, 0.63; 95% CI, 0.28-1.43; P = .3). Residual congestion, identified at predischarge LUS examination in high-risk patients, was associated with early (<14-day) HF readmission (relative risk, 1.19; 95% CI, 1.06-1.32; P = .002) and multiple (≥2) readmissions over 90 days of follow-up (relative risk, 1.09; 95% CI, 1.01-1.16; P = .012), independent of demographics and comorbidities. CONCLUSIONS: Readmission in patients with incomplete decongestion before discharge occurs within the first 2 weeks. However, our DMP-plus strategy did not improve the primary outcome.

Nurse-Provided Lung and Inferior Vena Cava Assessment in Patients With Heart Failure.

BACKGROUND: Residual congestion detected using handheld ultrasound may be associated with increased risk of readmission and death after hospitalization for acute decompensated heart failure (ADHF). However, effective application necessitates routine use by nonexperts delivering clinical care. OBJECTIVES: The objective of this study was to determine the ability of heart failure (HF) nurses to deliver a predischarge lung and inferior vena cava (IVC) assessment (LUICA) to predict 90-day outcomes. METHODS: In this multisite, prospective, observational study, HF nurses scanned 240 patients with ADHF (median age: 77 years; 56% men) using a 9-zone LUICA protocol. Obtained images were reviewed by independent nurses who were blinded to clinical characteristics and outcomes. Based on a B-line cut-off of 10, patients were dichotomized as congested (n = 115) or not congested (n = 125). RESULTS: Congested patients were more likely to have previous cardiac operations, long-standing HF (>6 months), and renal impairment. At 90 days, HF readmission or mortality occurred in 42 congested patients (37%) compared with 18 noncongested patients (14%). Pulmonary congestion increased at 30-day (OR: 3.86; 95% CI: 1.65-8.99; P < 0.01) and 90-day (OR: 3.42; 95% CI: 1.82-6.4; P < 0.01) HF readmission or mortality risk and 90-day mortality (OR: 5.18; 95% CI: 1.44-18.69; P < 0.01). Pulmonary congestion increased the 90-day odds of HF readmission and/or death by 3.3- to 4.2-fold (P < 0.01), independent of demographics, HF characteristics, comorbidities, and event risk score. Over 90 days, days alive out of hospital were fewer (78.3 ± 21.4 days vs 85.5 ± 12.4 days; P < 0.01) in congested patients. CONCLUSIONS: LUICA can be a powerful tool for detection of predischarge residual congestion. HF nurses can obtain images and provide diagnostic reports that are predictive of ADHF outcomes.

An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation.

Aims: Effective and efficient education and patient engagement are fundamental to improve health outcomes in heart failure (HF). The use of artificial intelligence (AI) to enable more effective delivery of education is becoming more widespread for a range of chronic conditions. We sought to determine whether an avatar-based HF-app could improve outcomes by enhancing HF knowledge and improving patient quality of life and self-care behaviour. Methods and results: In a randomized controlled trial of patients admitted for acute decompensated HF (ADHF), patients at high risk (≥33%) for 30-day hospital readmission and/or death were randomized to usual care or training with the HF-app. From August 2019 up until December 2020, 200 patients admitted to the hospital for ADHF were enrolled in the Risk-HF study. Of the 72 at high-risk, 36 (25 men; median age 81.5 years; 9.5 years of education; 15 in NYHA Class III at discharge) were randomized into the intervention arm and were offered education involving an HF-app. Whilst 26 (72%) could not use the HF-app, younger patients [odds ratio (OR) 0.89, 95% confidence interval (CI) 0.82-0.97; P < 0.01] and those with a higher education level (OR 1.58, 95% CI 1.09-2.28; P = 0.03) were more likely to enrol. Of those enrolled, only 2 of 10 patients engaged and completed ≥70% of the program, and 6 of the remaining 8 who did not engage were readmitted. Conclusions: Although AI-based education is promising in chronic conditions, our study provides a note of caution about the barriers to enrolment in critically ill, post-acute, and elderly patients.