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.

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.

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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 metaanalyses. 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 (I 2 = 46.5) and lower odds of all-cause mortality (OR 0.82 [95% CI 0.69, 0.98], P = 0.03) with low heterogeneity (I 2 = 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.

Background: The new Medicare item for heart health checks will likely increase access to conventional coronary risk calculation, identifying more intermediate risk pts. How

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Background Attainment of euvolemia at discharge and maintaining it after discharge are fundamental to avoiding readmission in heart failure (HF). Lung ultrasound (LUS) is potentially of value to detect congestion but the role of sequential LUS is undefined. Purpose To determine the predictive value of discharge and follow-up LUS. Methods 98 pts (mean age 72.8±12.3, mean ejection fraction 41.4%±18.4, gender male 56%) admitted with HF or fluid overload, underwent pre-discharge LUS to evaluate pulmonary (presence of ≥10 B lines) and peripheral (IVC diameter) congestion. LUS was repeated at home follow-up visits at 2 weeks post-discharge. Associations were sought between pre-discharge and follow-up LUS and 90 day outcomes (readmission or mortality). Results Overall, there was an increase in the total number of B-lines from baseline to week 2 [mean change in B-lines 3.82 [95% confidence intervals (CI), 0.30, 7.33) p=0.036] followed by a small decrease between scan 2 and scan 3 [mean change in B-lines −0.25 (95% CI, −0.17, 7.68), p=0.94]. Of 73 with <10 B-lines pre-discharge, 26 (36%) had events by 90 days, compared with 14 of the 25 with ≥10 pre-discharge B-lines (56%, p=0.07). However, all of those with ≥10 B lines at 2 weeks had events, compared with 25% of those with <10 B lines (p=0.04). Conclusions Attainment and preservation of euvolemia after index hospitalization for HF is challenging and requires appropriate patient support. Detection of residual congestion, as well as detection of early re-congestion after hospital discharge. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): The University of MelbourneBaker Heart & Diabetes Institute Readmission risk ratio

Background Inadequate decongestion at index admission for Acute Decompensation of Heart Failure (ADHF) is a common cause of adverse outcomes. A bedside 9-zone Lung and IVC ultrasound assessment (LUICA) may help to guide decongestion and reduce hospital readmission or mortality. Purpose To identify predictors of multiple 90-day hospital representations or mortality based on a bedside handheld 9-zone LUICA volume assessment obtained by HF nurses. Methods Patients admitted for ADHF, enrolled in the RISK-HF registry and undergoing pre-discharge LUICA, were assessed for 90-day readmission and/or mortality. The primary outcome of this observational report was prediction of multiple hospital representations based on pre-discharge volume status. The LUICA was performed with a hand-held ultrasound (HHU) device (Lumify, Philips) by trained HF-nurses. Functional capacity was measured with Duke Activity Status Index (DASI). Paired t-tests were used to compare mean differences. Logistic and linear regression were used to study relationships of outcomes with clinical characteristics. Cox regression was used to analyse time to repeated readmission or death. Analysis conducted with SPSS statistics V27 and STATA SE16. Results Of 302 ADHF patients, 67 readmitted within 30-days (age 76±8.5; men, 60%; HFrEF; 44%) and 235 did not readmit within 30-days (age 72±14; 57% men; 52% HFrEF). Readmission occurred in older patients (p=0.05), with pre-discharge signs of residual congestion that was based on the number of b-lines (p<0.01) (Table 1). Pre-discharge B-lines were predictive of DAOOH (β −0.41, −0.6, −0.22, p<0.01) and of multiple 90-day hospital readmissions (β 0.03, 0.018, 0.05, p<0.01), independently of 30-day event risk score, number of readmissions the preceding 12 months and age at index admission (Table 2). Number of B-lines at discharge was also associated with repeated readmission or death (HR=1.02 [1.01, 1.04]) in time-to-event analysis, independent of any other factors. Conclusion Pre-discharge residual congestion defined by the number B-lines increases the likelihood of multiple 90-day adverse outcomes. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): The University of MelbourneBaker Heart & Diabetes Institute