Remote Management of Worsening Heart Failure to Avoid Hospitalization in a Real-World Setting
Bibliographic record
Abstract
AIMS: From a patient and health system perspective, managing worsening heart failure (WHF) as an outpatient has become a priority. Remote management allows early detection of WHF, enabling timely intervention with the aim of preventing hospitalization. The objective of the study was to evaluate the feasibility and safety of remotely managing WHF events using a multiparametric platform. METHODS AND RESULTS: All patients enrolled in the heart failure remote management programme of the Bordeaux University Hospital Telemedicine Center between 1 January and 31 December 2021 were included in the study. Follow-up data were collected until 1 March 2022. Inclusion criteria were chronic heart failure (HF) with New York Heart Association ≥II symptoms and an elevated B-type natriuretic peptide (BNP > 100 pg/mL or N-terminal-pro-BNP > 1000 pg/mL). Patient assessments were performed remotely and included measurements of body weight, blood pressure, heart rate, symptoms, biochemical parameters, and data from cardiac implantable electronic devices when available. In total, 161 patients (71 ± 11 years old, 79% male) were followed for a mean of 291 ± 66 days with a mean adherence to the remote monitoring system of 80 ± 20%. Over this period, 52 (32.3%) patients had 105 WHF events, of which 66 (63%) were successfully managed remotely, the remaining requiring hospitalization. Freedom from WHF events and hospitalization at 300 days were 66% and 85%, respectively (P < 0.001 for the difference). Increased level of BNP was associated with an increased risk of WHF event [hazard ratio (HR) per unit increase in BNP: 1.001; 95% confidence interval (CI) 1-1.002; P = 0.001] and hospitalization (HR 1.002; 95% CI 1.002-1.003; P = 0.002). A decrease in the level of glomerular filtration rate was associated with an increased risk of hospitalization (HR per unit decrease in estimated glomerular filtration rate: 0.946; 95% CI 0.906-0.989; P = 0.014). WHF event recurrence and (re)hospitalization rates at 1-month were similar among patients managed remotely (18% and 12%, respectively) and those requiring hospitalization (21% and 10%, respectively). Iatrogenic complications occurred more often during hospitalization than remote management (26% vs. 3%, P < 0.001). CONCLUSIONS: Our study suggests that remote management of WHF events based on a multiparametric approach led by a telemedical centre is feasible and safe. Adopting such a strategy for patients with chronic HF could reduce HF-related hospitalizations with expected benefits for patients, care providers, and health care systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".