Systematic review of home-based care for the management of COPD patients receiving long-term home non-invasive ventilation
Bibliographic record
Abstract
Introduction: Long-term home non-invasive ventilation (LTH-NIV) improves the prognosis of patients with chronic obstructive pulmonary disease (COPD) and chronic hypercapnic respiratory failure. However, differences in care delivery may impact the benefit of LTH-NIV. This systematic review aimed to evaluate how LTH-NIV is delivered to COPD patients and the impact of delivery modalities on health outcomes. Methods: EMBASE and PubMed were systematically searched for studies reporting primary data of LTH-NIV initiation or additional home-based interventions aiming to improve health and organizational outcomes in COPD patients, published between 1990 and 2024 (PROSPERO ID CRD42025648464). Results: We identified 44 eligible articles, predominantly from Europe (n=36). Of these, 7 studies examined the organization of LTH-NIV delivery and 5 explored alternative care modalities, namely outpatient or home initiation (n=2), outpatient follow-up (n=1), and telemonitoring (n=3). Alternative care modalities showed promise; outpatient initiation and adaptation significantly improved health-related quality of life (severe respiratory insufficiency questionnaire after 3 months; +5.7, p=0.001), remote monitoring delayed hospital readmissions (by 151 days, p<0.05) and reduced 12-month readmission rate (-21.7%, p=0.023), and home initiation showed cost-saving (-€4769, p<0.001). Conclusion: Despite increasing use of LTH-NIV for COPD patients and the promising benefits of alternative care delivery models, research in this area is limited. Further studies are needed to optimize home-based care strategies and improve patient outcomes.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".