Subcutaneous sensors for monitoring congestion and to reduce heart failure hospitalizations—a viable middle ground between deep implantable intravascular monitoring devices and wearable technologies?
Bibliographic record
Abstract
Congestive heart failure (CHF) remains a leading cause of hospitalization and mortality worldwide. Continuous monitoring is crucial for early detection of decompensation, potentially reducing hospital admissions and improving outcomes. Cardiac implantable electronic devices (CIEDs) have been established as useful therapeutic interventions that also support continuous monitoring in order to detect early signs of decompensation. However, prior to CIED implantation, effective continuous monitoring solutions are lacking. They exist at two extremes: deep implantable intravascular solutions such as pulmonary artery pressure sensors, which are effective but costly and complex, and wearables, which are inexpensive but lack evidence of their effectiveness and depend on ongoing active patient adherence. Subcutaneous sensors may represent a promising intermediate solution-offering continuous monitoring with lower invasiveness and cost, while maintaining higher adherence compared to wearables. This review explores the role of subcutaneous sensors in CHF management, comparing existing daily trend data to deep implantable sensors measuring direct filling pressure and CIEDs for multi-parametric risk scoring. We discuss their feasibility, limitations, and future integration into routine clinical practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".