Cardiac Rhythm Monitoring After Acute Decompensation for Heart Failure: Results from the CARRYING ON for HF Pilot Study
Bibliographic record
Abstract
BACKGROUND: There's scarce evidence about cardiovascular events (CV) in patients with hospitalization for acute heart failure (HF) and no indication for immediate device implant. OBJECTIVE: The CARdiac RhYthm monitorING after acute decompensatiON for Heart Failure study was designed to assess the incidence of prespecified clinical and arrhythmic events in this patient population. METHODS: In this pilot study, 18 patients (12 (67%) male; age 72±10; 16 (89%) NYHA II-III), who were hospitalized for HF with low left ventricular ejection fraction (LVEF) (<40%) and no immediate indication for device implant received an implantable loop recorder (ILR) before hospital discharge. Follow-up visits were scheduled at 3 and 6 months, and at every 6 months until study closure; device data were remotely reviewed monthly. CV mortality, unplanned CV hospitalization, and major arrhythmic events during follow-up were analyzed. RESULTS: During a median follow-up of 593 days, major CV occurred in 13 patients (72%); of those, 7 patients had at least 1 cardiac arrhythmic event, 2 had at least a clinical event (CV hospitalization or CV death), and 4 had both an arrhythmic and a CV event. Six (33%) patients experienced 10 major clinical events, 5 of them (50%) were HF related. During follow-up, 2 (11%) patients died due to a CV cause and 3 (16%) patients received a permanent cardiac device. CONCLUSIONS: After an acute HF hospitalization, patients with LVEF<40% and who are not readily eligible for permanent cardiac device implant have a known high incidence of major CV event. In these patients, ILR allows early detection of major cardiac arrhythmias and the ability to react appropriately in a timely manner. TRIAL REGISTRATION: ClinicalTrials.gov NCT01216670; https://clinicaltrials.gov/ct2/show/NCT01216670.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".