Early Intervention with Impedance-guided Heart Failure Management Improves Long-term Outcome: Insights from the IMPEDANCE-HF Trial
Bibliographic record
Abstract
Background: Lung-impedance (LI) guided treatment of heart failure (HF) patients was shown to improve clinical outcomes. Objectives: To perform a post-hoc analysis of the IMPEDANCE-HF extended trial in order to explore the mechanism underlying the improved outcome of the LI-guided compared with conventional therapy of HF patients. Methods: The study included 290 HF patients with LVEF≤ 45% randomized 1:1 to LI-guided or conventional therapy. The normal LI (NLI), representing the dry lung status, was calculated upon enrollment. The level of pulmonary congestion (LPC) was represented by ΔLIR= [(measured LI/NLI)-1] × 100%. Results: There were 11473 outpatient visits in the LI-guided group and 10245 visits in the control group during follow-up, or 15.5 and 15.9 visits/patient×year, respectively (p=0.74). The LI-guided patients were on average less congested during follow-up than those in the control group (by 20 %, p<0.01). Multivariate regression analysis showed that the likelihood of hospitalization for HF [hazard ratio (HR): 0.62, 95% confidence interval (CI): 0.52-0.72, p<0.01) and of all-cause mortality (HR: 0.83, 95%CI: 0.70-0.98, p=0.03] were lower in the LI-guided group than in the control group. In the LI-guided group, diuretic up-titration was 2-fold more frequent and at an earlier timepoint and at a 21% lower LPC (p<0.01). In both groups the diuretic response was more prominent when up-titration was done at a lower LPC (p<0.01). Conclusion LI-guided diuretic titration prompted earlier, and more frequent diuretic dose increase when the LPC was only beginning to increase and this resulted in a greater decongestive response with better clinical outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".