Remote Pulmonary Artery Pressure-Guided Management of Patients with Heart Failure: A Clinical Consensus Statement of the Heart Failure Association (HFA) of the ESC
Bibliographic record
Abstract
Episodes of worsening heart failure (HF) are a major cause of unplanned hospitalizations. Their onset is usually preceded by an early increase in intracardiac pressures with subsequent worsening of symptoms due to congestion. Implantable devices allowing daily remote pulmonary artery pressure (PAP) monitoring are useful to identify early haemodynamic changes so that medical therapy can be adjusted at an early stage, before symptom onset, and HF-related hospitalizations be prevented. Second, the use of these devices may help to maintain clinical stability keeping PAP in the target range on a day-to-day basis. The CardioMEMS system allows remote PAP monitoring, and PAP-guided medical therapy has reduced HF-related hospitalizations in prospective, randomized, controlled clinical trials in symptomatic patients with HF, independent of their left ventricular ejection fraction. The safety and feasibility of other devices, like the Cordella implantable PAP sensor, have also been demonstrated and clinical usefulness in larger patient populations is currently being assessed in several trials. Most of the studies testing remote PAP monitoring were reported after the 2021 European Society of Cardiology HF guidelines. An update of the clinical significance and potential implications for clinical practice of these systems seems therefore warranted. The aim of this clinical consensus statement is to summarize current knowledge on remote PAP-guided management of patients with HF, with a special focus on current evidence from clinical trials, potential impact on clinical practice and management aspects.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".