Prospective Evaluation of Integrated Device Diagnostics for Heart Failure Management: Results of the TRIAGE-HF Study
Bibliographic record
Abstract
AIMS: The primary aim of the TRIAGE-HF trial was to correlate cardiac implantable electronic device-generated heart failure risk status (HFRS) with signs, symptoms, and patient behaviours classically associated with worsening heart failure (HF). METHODS AND RESULTS: TRIAGE-HF enrolled 100 subjects with systolic HF implanted with a Medtronic high-performance device and followed up at three Canadian HF centres. Study follow-up was up to 8 months. The HFRS assigned each subject's overall risk of HF hospitalization in the next 30 days and also highlighted abnormal device parameters contributing to a patient's risk status at the time of remote data transmission. Subjects with a high HFRS were contacted by telephone to assess symptoms, and compliance with prescribed therapies, nutrition, and exercise. Clinician-assessed risk and HFRS-calculated risk were correlated at both study baseline and exit. Twenty-four high HFRS occurrences were observed among 100 subjects. Device parameters associated with increased risk of HF hospitalization included OptiVol index (n = 20), followed by low patient activity (n = 18) and elevated night heart rate (n = 12). High HFRS was associated with symptoms of worsening HF in 63% of cases (n = 15) increasing to 83% of cases (n = 20) when non-compliance with pharmacological therapies and lifestyle was considered. CONCLUSIONS: TRIAGE-HF is the first study to provide prospective data on the distribution of abnormal device parameters contributing to high HFRS. High HFRS has good predictive accuracy for patient-reported signs, symptoms, and behaviours associated with worsening HF status. As such, HFRS may be a useful tool for ambulatory HF monitoring to improve both patient-centred and health system level 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".