Detecting Heart Failure Decompensation by Measuring Transthoracic Bioimpedance in the Outpatient Setting: Rationale and Design of the SENTINEL-HF Study
Bibliographic record
Abstract
BACKGROUND: Recurrent hospital admissions are common among patients admitted for acute decompensated heart failure (ADHF), but identification of patients at risk for rehospitalization remains challenging. Contemporary heart failure (HF) management programs have shown modest ability to reduce readmissions, partly because they monitor signs or symptoms of HF worsening that appear late during decompensation. Detecting early stages of HF decompensation might allow for immediate application of effective HF therapies, thereby potentially reducing HF readmissions. One of the earliest indicators of HF decompensation is intrathoracic fluid accumulation, which can be assessed using transthoracic bioimpedance. OBJECTIVE: The SENTINEL-HF study is a prospective observational study designed to test a novel, wearable HF monitoring system as a predictor of HF decompensation among patients discharged after hospitalization for ADHF. METHODS: SENTINEL-HF tests the hypothesis that a decline in transthoracic bioimpedance, as assessed daily with the Philips fluid accumulation vest (FAV) and transmitted using a mobile phone, is associated with HF worsening and rehospitalization. According to pre-specified power calculations, 180 patients admitted with ADHF are enrolled. Participants transmit daily self-assessments using the FAV-mobile phone dyad for 45 days post-discharge. The primary predictor is the deviation of transthoracic bioimpedance for 3 consecutive days from a patient-specific normal variability range. The ADHF detection algorithm is evaluated in relation with a composite outcome of HF readmission, diuretic up-titration, and self-reported HF worsening (Kansas City Cardiomyopathy Questionnaire) during a 90-day follow-up period. Here, we provide the details and rationale of SENTINEL-HF. RESULTS: Enrollment in the SENTINEL-HF study is complete and the 90-days follow-up is currently under way. Once data collection is complete, the study dataset will be used to evaluate our ADHF detection algorithm and the results submitted for publication. CONCLUSION: SENTINEL-HF emerged from our long-term vision that advanced home monitoring technology can improve the management of chronic HF by extending clinical care into patients' homes. Monitoring transthoracic bioimpedance with the FAV may identify patients at risk of recurrent HF decompensation and enable timely preventive measures. TRIAL REGISTRATION: Clinicaltrials.gov NCT01877369: https://clinicaltrials.gov/ct2/show/NCT01877369 (Archived by WebCite at http://www.webcitation.org/6bDYl0dGy).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".