Ambulatory Pulmonary Artery Pressure Monitoring Reduces Costs and Improves Outcomes in Symptomatic Heart Failure: A Single-Centre Canadian Experience
Bibliographic record
Abstract
Background Pulmonary artery pressure (PAP) monitoring reduces heart failure (HF) hospitalizations (HFHs) and improves quality of life in New York Heart Association (NYHA) class III HF. We evaluated the impact of PAP monitoring on outcomes and health spending in a Canadian ambulatory HF cohort. Methods Twenty NYHA III HF patients underwent wireless PAP implantation at Foothills Medical Centre, Calgary, Alberta. Baseline, and 3-, 6-, 9-, and 12-month assessments of laboratory parameters, hemodynamics, 6-minute walk text and Kansas City Cardiomyopathy Questionnaire scores were collected. Healthcare costs 1 year pre- and post-implantation were collected from administrative databases. Results Mean age was 70.6 years; 45% were female. Results were as follows: an 88% reduction in emergency room visits ( P = 0.0009); an 87% reduction in HFHs ( P < 0.0003); a 29% reduction in heart function clinic visits ( P = 0.033), and a 178% increase in nurse calls ( P < 0.0002). Questionnaire and 6-minute walk test scores at baseline vs last follow-up were 45.4 vs 48.4 ( P = 0.48) and 364.4 vs 402.8 m ( P = 0.58), respectively. Mean PAP at baseline vs follow-up was 31.5 vs 24.8 mm Hg ( P = 0.005). NYHA class improved by at least one class in 85% of patients. Mean measurable HF-related spending preimplantation was CAD$29,814 per patient per year and postimplantation was CAD$25,642 per patient per year (including device cost). Conclusions PAP monitoring demonstrated reductions in HFHs, and emergency room and heart function clinic visits, with improvements in NYHA class. Although further economic evaluation is needed, these results support the use of PAP monitoring as an effective and cost-neutral tool in HF management in appropriately selected patients in a publicly funded healthcare system.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".