Autonomic heart rate variability trends predict outcome in disorders of consciousness
Bibliographic record
Abstract
Disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS/VS) and minimally conscious state (MCS), pose significant diagnostic challenges due to their complexity and high misdiagnosis rates. This study investigates the prognostic potential of heart rate variability (HRV) ratios between stimulation and baseline, combined with support vector machine (SVM) classification, to predict outcomes in DoC patients. Fifty patients were enrolled within the first 10 days of hospitalization; 40 were used to train and optimize the SVM model, while 10 served as an independent test group. HRV analysis employed ratios of high-frequency and low-frequency components along with Sample Entropy to capture dynamic autonomic changes. Assessments were conducted weekly over three weeks. The SVM achieved 97% overall accuracy (misclassification 3%), 96% sensitivity, 100% specificity, and 97% balanced accuracy during training (10-fold cross-validation: 0% misclassification). In the independent test set (N = 10), performance was 80% overall accuracy (misclassification 20%), 80% sensitivity, 80% specificity, and 80% balanced accuracy. These results highlight the value of the HRV ratio approach, particularly the early recovery of vagal response followed by sympathetic activation, in predicting patient trajectories. Although the sample size is small, our findings support the integration of HRV analysis with machine learning as a promising tool for enhancing prognostic assessments in DoC. Future research should replicate these findings in larger cohorts and incorporate longitudinal data.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".