Predicting Heart Failure Decompensation Using Cardiac Implantable Electronic Devices: A Review of Practices and Challenges
Bibliographic record
Abstract
Cardiac implantable electronic devices include remote monitoring tools intended to guide heart failure management. The monitoring focus has been on averting hospitalizations by predicting worsening heart failure. However, although device measurements including intrathoracic impedance correlate with risk of decompensation, they individually predict hospitalizations with limited accuracy. Current 'crisis detection' methods involve repeatedly screening for impending decompensation, and do not adhere to the principles of diagnostic testing. Complex substrate, limited test performance, low outcome incidence, and long test to outcome times inevitably generate low positive and high negative predictive values. When combined with spectrum bias, the generalizability, incremental value, and cost-effectiveness of device algorithms are questionable. To avoid these pitfalls, remote monitoring may need to shift from crisis detection to health maintenance, keeping the patient within an ideal physiological range through continuous 'closed loop' interaction and dynamic therapy adjustment. Test performance must also improve, possibly through combination with physiological sensors in different dimensions, static baseline characteristics, and biomarkers. Complex modelling may tailor monitoring to individual phenotypes, and thus realize a personalized medicine approach. Future randomized controlled trials should carefully consider these issues, and ensure that the interventions tested are generalizable to 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".