COST-UTILITY ANALYSIS OF NT-PROBNP-GUIDED MULTIDISCIPLINARY CARE IN CHRONIC HEART FAILURE
Bibliographic record
Abstract
OBJECTIVES: A recent randomized, controlled trial in chronic heart failure patients showed that NT-proBNP-guided, intensive patient management (BMC) on top of multidisciplinary care reduced all-cause mortality and heart failure hospitalizations compared with multidisciplinary care (MC) or usual care (UC). We now performed a cost-utility analysis of these interventions from a payer's perspective. METHODS: Costs related to hospitalizations, ambulatory physician and nurse visits, and NT-proBNP testing for the three management strategies were acquired for both Austria (€) and Canada ($) and combined with the survival and quality of life data from the clinical trial for cost-effectiveness analysis. Data on long-term survival, costs, and quality-adjusted life-years (QALY) were extrapolated for a 20-year time horizon using a Markov model, which simulated the progression of disease through beta-blocker use, hospitalizations, and mortality. RESULTS: BMC was the most cost-effective strategy as it was dominant (cost-saving with improved health outcome) over both MC and UC based on both Austrian and Canadian costs. Incremental cost-effectiveness ratios for MC relative to UC were €3,746 and $5,554 per QALY gained for Austrian and Canadian costs, respectively. The probabilities for BMC being the most cost-effective strategy were 92 percent at a threshold value of Austrian €40,000 and 93 percent at a threshold value of Canadian $50,000. CONCLUSIONS: NT-proBNP-guided, intensive HF patient management in addition to multidisciplinary care not only reduces death and hospitalization but also proves to be cost-effective.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".