Abstract 4369009: Near-Maximal Movement Captured by Accelerometry Offers Superior Mortality Risk Prediction Than Aggregate Movement in Heart Failure
Bibliographic record
Abstract
Background: There is a growing interest in understanding the impact of tested therapies on real-world functional outcomes as measured by wearable accelerometers (ACC) in heart failure (HF). However, there is a need to validate clinically meaningful ACC biomarkers as trials have shown limited efficacy in improving aggregate ACC measures such as daily steps to date. Here, we examine the association between an established aggregate ACC measure and a novel biomarker of near-maximal movement with the risk of all-cause and cardiovascular (CV) mortality in patients with HF. Research Question: How do measures of aggregate vs. near-maximal volitional movement captured via accelerometry compare in their prediction of mortality risk in patients with heart failure? Methods: Participants in the 2011–2012 and 2013–2014 waves of NHANES wore the Actigraph GT3X+ on their wrist for 7 days and were followed up to confirm mortality status and cause of death (ICD-10) in 2019. The sum of vector magnitudes (SVM) was calculated as the mean vector magnitude of acceleration for every 5 minutes. SVM values were ranked numerically and the 90 th percentile value was captured for each participant as a novel biomarker (i.e., SVM-90) of near-maximal movement. The aggregate ACC metric Monitor-independent movement summary (MIMS) was downloaded from the NHANES website. Cox proportional hazards models were employed to evaluate the relationship between SVM-90 and MIMS values above or below the cohort median and time to all-cause or CV mortality. Results: 291 participants with self-reported HF (50.9% female, mean (SD) age 66.8 (12.6), mean 5.4 years of follow-up) were included. Participants with SVM-90 below the median were older than their counterparts (age 70.6 (10.9) vs. 63.0 (13.1), p<0.001) while other demographics did not differ between groups. In models adjusted for MIMS above or below the median, the high SVM-90 group presented reduced risk of all-cause (HR 0.56 (0.38-0.84), p=0.00444, Fig.1A) and CV mortality (HR 0.43 (0.23-0.79), p=0.00676, Fig.1B) compared to the low SVM-90 group, while MIMS was not associated with risk of either outcome (all-cause: HR 0.72 (0.49-1.06), p=0.0959; CV: HR 0.81 (0.45-1.45), p=0.478). Conclusion: Here we provide evidence that a measure of near-maximal movement captured by ACC offers superior mortality prediction in HF compared to aggregate movement. ACC provides objective and patient-centric biomarkers that may serve as outcomes in HF trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".