A novel algorithm to assess risk of heart failure exacerbation using ICD diagnostics: Validation from RAFT
Bibliographic record
Abstract
BackgroundThe integrated diagnostics (ID) algorithm is an implantable device-based tool that collates data pertaining to heart rhythm, heart rate, intrathoracic fluid status, and activity, producing a risk score that correlates with 30-day risk of heart failure (HF) hospitalization.ObjectiveWe sought to validate the ID algorithm using the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial.MethodsDiagnostic measures of the algorithm include OptiVol fluid index, nighttime heart rate, minutes of patient activity, heart rate variability, and combined measure of cardiac rhythm and biventricular pacing. Monthly evaluations of ID parameters were assessed for the development of HF symptoms and hospitalization for HF.ResultsA total of 1224 patients were included: 741 (61%) with cardiac resynchronization therapy with defibrillator devices and 483 (39%) with implanted cardioverter-defibrillator only. The mean age was 66 ± 9 years, and 1013 (83%) were men. A total of 37,861 months of follow-up data were available, with 258 HF hospitalizations (event rate 0.68% per month). There were 33 HF hospitalizations during low-risk months (0.21% per month), 123 during medium-risk months (0.66% per month), and 102 during high-risk months (2.61% per month). Compared with low-risk months, and 95% confidence intervals) of HF hospitalizations during medium-risk months was 2.9 (2.0–4.4) and during high-risk months was 10.7 (6.9–16.6). Multivariable analysis demonstrated that each ID variable had independent association with HF hospitalization.ConclusionThe risk of HF as determined by the ID algorithm correlated with HF hospitalization and several HF signs and symptoms among patients in the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. This may present a useful adjunct to detect early signs of HF and adjust therapy to reduce morbidity and costs involved with hospital admission. The integrated diagnostics (ID) algorithm is an implantable device-based tool that collates data pertaining to heart rhythm, heart rate, intrathoracic fluid status, and activity, producing a risk score that correlates with 30-day risk of heart failure (HF) hospitalization. We sought to validate the ID algorithm using the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. Diagnostic measures of the algorithm include OptiVol fluid index, nighttime heart rate, minutes of patient activity, heart rate variability, and combined measure of cardiac rhythm and biventricular pacing. Monthly evaluations of ID parameters were assessed for the development of HF symptoms and hospitalization for HF. A total of 1224 patients were included: 741 (61%) with cardiac resynchronization therapy with defibrillator devices and 483 (39%) with implanted cardioverter-defibrillator only. The mean age was 66 ± 9 years, and 1013 (83%) were men. A total of 37,861 months of follow-up data were available, with 258 HF hospitalizations (event rate 0.68% per month). There were 33 HF hospitalizations during low-risk months (0.21% per month), 123 during medium-risk months (0.66% per month), and 102 during high-risk months (2.61% per month). Compared with low-risk months, and 95% confidence intervals) of HF hospitalizations during medium-risk months was 2.9 (2.0–4.4) and during high-risk months was 10.7 (6.9–16.6). Multivariable analysis demonstrated that each ID variable had independent association with HF hospitalization. The risk of HF as determined by the ID algorithm correlated with HF hospitalization and several HF signs and symptoms among patients in the Resynchronization-Defibrillation for Ambulatory Heart Failure Trial. This may present a useful adjunct to detect early signs of HF and adjust therapy to reduce morbidity and costs involved with hospital admission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".