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BackgroundDue to the complexity and chronicity of heart failure, engaging yet simple patient self-management tools are needed. ObjectiveThis study aimed to assess the feasibility and patient engagement with a smartphone app designed for heart failure. MethodsPatients with heart failure were randomized to intervention (smartphone with the Habits Heart App installed and Bluetooth-linked scale) or control (paper education material) groups. All intervention group patients were interviewed and monitored closely for app feasibility while receiving standard of care heart failure management by cardiologists. The Atlanta Heart Failure Knowledge Test, a quality of life survey (Kansas City Cardiomyopathy Questionnaire), and weight were assessed at baseline and final visits. ResultsPatients (N=28 patients; intervention: n=15; control: n=13) with heart failure (with reduced ejection fraction: 15/28, 54%; male: 20/28, 71%, female: 8/28, 29%; median age 63 years) were enrolled, and 82% of patients (N=23; intervention: 12/15, 80%; control: 11/13, 85%) completed both baseline and final visits (median follow up 60 days). In the intervention group, 2 out of the 12 patients who completed the study did not use the app after study onboarding due to illnesses and hospitalizations. Of the remaining 10 patients who used the app, 5 patients logged ≥1 interaction with the app per day on average, and 2 patients logged an interaction with the app every other day on average. The intervention group averaged 403 screen views (per patient) in 56 distinct sessions, 5-minute session durations, and 22 weight entries per patient. There was a direct correlation between duration of app use and improvement in heart failure knowledge (Atlanta Heart Failure Knowledge Test score; ρ=0.59, P=.04) and quality of life (Kansas City Cardiomyopathy Questionnaire score; ρ=0.63, P=.03). The correlation between app use and weight change was ρ=–0.40 (P=.19). Only 1 out of 11 patients in the control group retained education material by the follow-up visit. ConclusionsThe Habits Heart App with a Bluetooth-linked scale is a feasible way to engage patients in heart failure management, and barriers to app engagement were identified. A larger multicenter study may be warranted to evaluate the effectiveness of the app. Trial RegistrationClinicalTrials.gov NCT03238729; http://clinicaltrials.gov/ct2/show/NCT03238729

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.

AimsWhile heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.Methods and resultsWe pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49%], HFpEF (EF ≥50%), and HFrEF (EF ≤40%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P ≤ 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P ≤ 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95% CI 1.20-1.90, P = 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95% CI 1.41-1.73, P = 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P = 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P = 0.78).ConclusionsWe found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

Aims: Patients with HFrEF and worsening HF events (WHFE) are at particularly high risk and urgently need disease‐modifying therapy. CHART‐HF assessed treatment patterns and reasons for medication decisions among HFrEF patients with and without WHFE. Methods and results: CHART‐HF collected retrospective electronic medical records of outpatients with HF and EF < 45% between 2017–2019 from a nationwide panel of 238 cardiologists (458 patients) and the Geisinger Health System (GHS) medical record (1000 patients). The index visit in the WHFE cohort was the first outpatient cardiologist visit ≤6 months following the WHFE, and in the reference cohort was the last visit in a calendar year without WHFE. Demographic characteristics were similar between patients with and without WHFE in both the nationwide panel and GHS. In the nationwide panel, the proportion of patients with versus without WHFE receiving ≥50% of guideline‐recommended dose on index visit was 35% versus 40% for beta blocker, 74% versus 83% for ACEI/ARB/ARNI, and 48% versus 49% for MRA. The proportion of patients receiving ≥50% of guideline‐recommended dose was lower in the GHS: 29% versus 34% for beta‐blocker, 16% versus 31% for ACEI/ARB/ARNI, and 18% versus 22% for MRA. For patients with and without WHFE, triple therapy on index date was 42% and 44% of patients from the nationwide panel, and 14% and 17% in the GHS. Comparing end of index clinic visit with 12‐month follow‐up in the GHS, the proportion of patients on no GDMT increased from 14% to 28% in the WHFE cohort and from 14 to 21% in the non‐WHFE group. Conclusions: Major gaps in use of GDMT, particularly combination therapy, remain among US HFrEF patients. These gaps persist during longitudinal follow‐up and are particularly large among patients with recent WHFE. [ABSTRACT FROM AUTHOR]

ObjectiveThe objective of the study was to assess urinary biomarkers of renal injury for their individual or collective ability to predict Worsening renal function (WRF) in patients with acutely decompensated heart failure (ADHF).MethodsIn a prospective, blinded international study, 87 emergency department (ED) patients with ADHF were evaluated with biomarkers of cardiac stretch (B type natriuretic peptide [BNP] and its amino terminal equivalent [NT-proBNP], ST2), biomarkers of renal function (creatinine, estimated glomerular filtration rate [eGFR]) and biomarkers of renal injury (plasma neutrophil gelatinase associated lipocalin [pNGAL], urine kidney injury molecule-1 [KIM-1], urine N-acetyl-beta-D-glucosaminidase [NAG], urine Cystatin C, urine fibrinogen). The primary endpoint was WRF.Results26% developed WRF; baseline characteristics of subjects who developed WRF were generally comparable to those who did not. Biomarkers of renal function and urine biomarkers of renal injury were not correlated, while urine biomarkers of renal injury correlated between each other. Biomarker concentrations were similar between patients with and without WRF except for baseline BNP. Although plasma NGAL was associated with the combined endpoint, none of the biomarker showed predictive accuracy for WRF.ConclusionsIn ED patients with ADHF, urine biomarkers of renal injury did not predict WRF. Our data suggest that a weak association exists between renal dysfunction and renal injury in this setting (Clinicaltrials.gov NCT#0150153).