Hemodynamic‐guided heart‐failure management using a wireless implantable sensor: Infrastructure, methods, and results in a community heart failure disease‐management program
Bibliographic record
Abstract
BACKGROUND: The real-world impact of remote pulmonary artery pressure (PAP) monitoring on New York Heart Association (NYHA) class improvement and heart failure (HF) hospitalization rate is presented here from a single center. HYPOTHESIS: METHODS: Seventy-seven previously hospitalized outpatients with NYHA class III HF were offered PAP monitoring via device implantation in a multidisciplinary HF-management program. Prospective effectiveness analyses compared outcomes in 34 hemodynamically monitored patients to a group of similar patients (n = 32) who did not undergo device implantation but received usual care. NYHA class and 6-minute walk testing were assessed at baseline and 90 days. All hospitalizations were collected after 6 months of the implantation date (average follow-up, 15 months) and compared with the number of hospitalizations experienced prior to hemodynamic monitoring. RESULTS: Patients in both groups had similar distributions of age, sex, and ejection fraction. After 90 days, 61.8% of the monitored patients had NYHA class improvement of ≥1, compared with 12.5% in the controls (P < 0.001). Distance walked in 6 minutes increased by 54.5 meters in the monitored group (253.0 ± 25.6 meters to 307.4 ± 26.3 meters; P < 0.005), whereas no change was seen in the usual-care group. After implantation, 19.4% of the monitored group had ≥1 HF hospitalization, compared with 100% who had been hospitalized in the year prior to implantation. The monitored group had a significantly lower HF hospitalization rate (0.16; 95% confidence interval: 0.06-0.35 hospitalizations/patient-year) compared with the year prior (1.0 hospitalizations/patient-year; P < 0.001). CONCLUSIONS: Hemodynamic-guided HF management leads to significant improvements in NYHA class and HF hospitalization rate in a real-world setting compared with usual care delivered in a comprehensive disease-management program.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".