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.

Optimising the implementation of digital-supported interventions for the secondary prevention of heart disease: a systematic review using the RE-AIM planning and evaluation framework.

BACKGROUND: mHealth technologies are now widely utilised to support the delivery of secondary prevention programs in heart disease. Interventions with mHealth included have shown a similar efficacy and safety to conventional programs with improvements in access and adherence. However, questions remain regarding the successful wider implementation of digital-supported programs. By applying the Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM) framework to a systematic review and meta-analysis, this review aims to evaluate the extent to which these programs report on RE-AIM dimensions and associated indicators. METHODS: This review extends our previous systematic review and meta-analysis that investigated the effectiveness of digital-supported programs for patients with coronary artery disease. Citation searches were performed on the 27 studies of the systematic review to identify linked publications that reported data for RE-AIM dimensions. All included studies and, where relevant, any additional publications, were coded using an adapted RE-AIM extraction tool. Discrepant codes were discussed amongst reviewers to gain consensus. Data were analysed to assess reporting on indicators related to each of the RE-AIM dimensions, and average overall reporting rates for each dimension were calculated. RESULTS: Searches found an additional nine publications. Across 36 publications that were linked to the 27 studies, 24 (89%) of the studies were interventions solely delivered at home. The average reporting rates for RE-AIM dimensions were highest for effectiveness (75%) and reach (67%), followed by adoption (54%), implementation (36%) and maintenance (11%). Eleven (46%) studies did not describe relevant characteristics of their participants or of staff involved in the intervention; most studies did not describe unanticipated consequences of the intervention; the ongoing cost of intervention implementation and maintenance; information on intervention fidelity; long-term follow-up outcomes, or program adaptation in other settings. CONCLUSIONS: Through the application of the RE-AIM framework to a systematic review we found most studies failed to report on key indicators. Failing to report these indicators inhibits the ability to address the enablers and barriers required to achieve optimal intervention implementation in wider settings and populations. Future studies should consider alternative hybrid trial designs to enable reporting of implementation indicators to improve the translation of research evidence into routine practice, with special consideration given to the long-term sustainability of program effects as well as corresponding ongoing costs. REGISTRATION: PROSPERO-CRD42022343030.

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.

Use of novel non-invasive techniques and biomarkers to guide outpatient management of fluid overload and reduce hospital readmission: systematic review and meta-analysis.

AIMS: Fluid congestion is a leading cause of hospital admission, readmission, and mortality in heart failure (HF). We performed a systematic review and meta-analysis to determine the effectiveness of an advanced fluid management programme (AFMP). The AFMP was defined as an intervention providing tailored diuretic therapy guided by intravascular volume assessment, in hospitalized patients or after discharge. The AFMP group was compared with patients who received standard care treatment. The aim of this systematic review and meta-analysis was to determine the effectiveness of an AFMP in improving patient outcomes. METHODS AND RESULTS: A systematic review of randomized controlled trials, case-control studies, and crossover studies using the terms 'heart failure', 'fluid management', and 'readmission' was conducted in PubMed, CINAHL, and Scopus up until November 2020. Studies reporting the association of an AFMP on readmission and/or mortality were included in our meta-analyses. Risk of bias was assessed in non-randomized studies using the Newcastle-Ottawa Scale. From 232 retrieved studies, 12 were included in the data synthesis. The 6040 patients in the included studies had a mean age of 72 ± 4 years and mean left ventricular ejection fraction of 39 ± 8%, there were slightly more men (n = 3022) than women, and the follow-up period was a mean of 4.8 ± 3.1 months. Readmission data were available in 5362 patients; of these, 1629 were readmitted. Mortality data were available in 5787 patients; of these, 584 died. HF patients who had an AFMP in hospital and/or after discharge had lower odds of all-cause readmission (odds ratio-OR 0.64 [95% confidence interval-CI 0.44, 0.92], P = 0.02) with moderate heterogeneity (I2  = 46.5) and lower odds of all-cause mortality (OR 0.82 [95% CI 0.69, 0.98], P = 0.03) with low heterogeneity (I2  = 0). The use of an AFMP was equally effective in reducing readmission and mortality regardless of age and follow-up duration. Effective pre-discharge diuresis was associated with significantly lower readmission odds (OR 0.43 [95% CI 0.26, 0.71], P = 0.001) compared with a fluid management plan as part of post-discharge follow-up. CONCLUSIONS: An effective AFMP is associated with improving readmission and mortality in HF. Our results encourage attainment of optimal volume status at discharge and prescription of optimal diuretic dose. Ongoing support to maintain euvolaemia and effective collaboration between healthcare teams, along with effective patient education and engagement, may help to reduce adverse outcomes in HF patients.

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.

Rationale and design of a risk-guided strategy for reducing readmissions for acute decompensated heart failure: the Risk-HF study.

AIMS: Heart failure (HF) readmission commonly arises owing to insufficient patient knowledge and failure of recognition of the early stages of recurrent fluid congestion. In previous work, we developed a score to predict short-term hospital readmission and showed that higher-risk patients benefit most from a disease management programme (DMP) that included enhancing knowledge and education by a nurse. We aim to evaluate the effectiveness of a novel, nurse-led HF DMP in selected patients at high risk of short-term hospital readmission, using ultrasound-guided diuretic management and artificial intelligence to enhance HF knowledge in an outpatient setting. METHODS AND RESULTS: Risk-HF is a prospective multisite randomized controlled trial that will allocate 404 patients hospitalized with acute decompensated HF, and ≥33% risk of readmission and/or death at 30 days, into risk-guided nurse intervention (DMP-Plus group) compared with usual care. Intervention elements include (i) fluid management with a handheld ultrasound (HHU) device at point of care; (ii) post-discharge follow-up; (iii) optimal programmed drug titration; (iv) better transition of care; (v) intensive self-care education via an avatar-based 'digital health coach'; and (vi) exercise guidance through the digital coach. Usual care involves standard post-discharge hospital care. The primary outcome is reduced death and/or hospital readmissions at 30 days post-discharge, and secondary outcomes include quality of life, fluid management efficacy, and feasibility and patient engagement. Assuming that our intervention will reduce readmissions and/or deaths by 50%, with a 1:1 ratio of intervention vs. usual care, we plan to randomize 404 patients to show a difference at a statistical power of 80%, using a two-sided alpha of 0.05. We anticipate this recruitment will be achieved by screening 2020 hospitalized HF patients for eligibility. An 8 week pilot programme of our digital health coach in 21 HF patients, age > 75 years, showed overall improvements in quality of life (13 of 21), self-care (12 of 21), and HF knowledge (13 of 21). A pilot of the use of HHU by nurses showed that it was feasible and accurate. CONCLUSIONS: The Risk-HF trial will evaluate the effectiveness of a risk-guided intervention to improve HF outcomes and will evaluate the efficacy of trained HF nurses delivering a fluid management protocol that is guided by lung ultrasound with an HHU at point of care.