Hemodynamic‐guided heart‐failure management using a wireless implantable sensor: Infrastructure, methods, and results in a community heart failure disease‐management program
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it