Clinical experiences in the management of COPD patients receiving long-term home non-invasive ventilation: a European survey
Bibliographic record
Abstract
Introduction: Long-term home non-invasive ventilation (LTH-NIV) is a well-established treatment for patients with chronic obstructive pulmonary disease (COPD) and chronic hypercapnic respiratory failure. However, differences in disease management and modalities of care delivery may exist across Europe. This study aimed to explore European clinical experiences with LTH-NIV for COPD patients. Methods: An online, mixed-methods survey was conducted between January and February 2025 amongst European clinicians with experience of LTH-NIV for COPD patients. Results: A total of 38 clinicians from 11 European countries participated, from centers managing on average more than 150 COPD patients on LTH-NIV per year. Across Europe, LTH-NIV initiation (mean duration 7.2 days) is predominantly performed in hospital settings (67%) and preceded by acute exacerbations (68%). Remote or at-home follow-up was available to some clinicians from Denmark (n=2), Italy (n=4), Norway (n=1), Poland (n=2), Spain (n=1), and the UK (n=1). Although most clinicians (53%) were satisfied with the support available to their patients (highest satisfaction in Denmark and Poland), many reported the need for improved care options, such as supplementary at-home support (34%) and remote patient monitoring (32%). Clinicians were also willing to delegate an additional 39% of hospital-based tasks to non-hospital stakeholders to improve care efficacy and hospital resource management. Conclusion: The management of COPD patients requiring LTH-NIV varies substantially across Europe, reflecting a complex and heterogeneous landscape. A multi-stakeholder, collaborative approach is needed to improve care delivery.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".