Integration of Implantable Device Therapy in Patients with Heart Failure. A Clinical Consensus Statement from the Heart Failure Association (HFA) and European Heart Rhythm Association (EHRA) of the European Society of Cardiology (ESC)
Bibliographic record
Abstract
Implantable devices form an integral part of the management of patients with heart failure (HF) and provide adjunctive therapies in addition to cornerstone drug treatment. Although the number of these devices is growing, only few are supported by robust evidence. Current devices aim to improve haemodynamics, improve reverse remodelling, or provide electrical therapy. A number of these devices have guideline recommendations and some have been shown to improve outcomes such as cardiac resynchronization therapy, implantable cardioverter-defibrillators and long-term mechanical support. For others, more evidence is still needed before large-scale implementation can be strongly advised. Of note, devices and drugs can work synergistically in HF as improved disease control with devices can allow for further optimization of drug therapy. Therefore, some devices might already be considered early in the disease trajectory of HF patients, while others might only be reserved for advanced HF. As such, device therapy should be integrated into HF care programmes. Unfortunately, implementation of devices, including those with the greatest evidence, in clinical care pathways is still suboptimal. This clinical consensus document of the Heart Failure Association (HFA) and European Heart Rhythm Association (EHRA) of the European Society of Cardiology (ESC) describes the physiological rationale behind device-provided therapy and also device-guided management, offers an overview of current implantable device options recommended by the guidelines and proposes a new integrated model of device therapy as a part of HF care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".