Heart failure disease management program, its contribution to established pharmacotherapy and long-term prognosis in real clinical practice - retrospective data analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The prognosis of patients with heart failure (HF) is still generally unfavorable. HF with reduced ejection fraction (HFrEF) patients reach target medication doses in very low percentages in daily clinical practice. HF disease management programs (DMP), including nurse and telemedicine support that facilitate achieving target medication doses, may improve the unfavorable prognosis. METHODS: We retrospectively analyzed the data of 738 patients with HFrEF who were followed in a single HF center during the years 1975-2011, for 6.4 (median) years. DMP, nurse and telemedicine support is established at this center. RESULTS: The group achieved left ventricle (LV) recovery after the HF treatment. The median LV ejection fraction improved from 25.0% at baseline to 50.0% at the time of the latest data collection. The proportion of NYHA II, III and IV classes decreased from 27.6%, 30.2% and 29.7% to 26.6%, 7.2% and 0.1%, respectively while the proportion of NYHA class I increased from 12.5% to 66.1%. Median NT-proBNP decreased from 975.0 to 324.0 pg/mL. The survival of the patient group was favorable; 79.7% survived 18.1 years after diagnosis of HF. A high percentage of the patients received recommended target or higher than target doses of angiotensin-converting enzyme inhibitors (82.0%) and beta-blockers (78.1%). CONCLUSION: The established pharmacotherapy resulted from an effective DMP and this contributed to the favorable prognosis.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".