Heart Rate Variability, Clinical and Laboratory Measures to Predict Future Deterioration in Patients Presenting With Sepsis
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Bibliographic record
Abstract
BACKGROUND: Risk stratification of patients presenting to the emergency department (ED) with sepsis can be challenging. We derived and evaluated performance of a predictive model containing clinical, laboratory, and heart rate variability (HRV) measures to quantify risk of deterioration in this population. METHODS: ED patients aged 21 and older satisfying the 1992 consensus conference criteria for sepsis and able to consent (directly or through a surrogate) were enrolled (n = 1,247). Patients had clinical, laboratory, and HRV data recorded within 1 h of ED presentation, and were followed to identify deterioration within 72 h. RESULTS: Eight hundred thirty-two patients had complete data, of whom 68 (8%) reached at least one endpoint. Optimal predictive performance was derived from a combination of laboratory values and HRV metrics with an area under the receiver-operating curve (AUROC) of 0.80 (95% CI, 0.65-0.92). This combination of variables was superior to clinical (AUROC = 0.69, 95% CI, 0.54-0.83), laboratory (AUROC = 0.77, 95% CI, 0.63-0.90), and HRV measures (AUROC = 0.76, 95% CI, 0.61-0.90) alone. The HRV+LAB model identified a high-risk cohort of patients (14% of all patients) with a 4.3-fold (95% CI, 3.2-5.4) increased risk of deterioration (incidence of deterioration: 35%), as well as a low-risk group (61% of all patients) with 0.2-fold (95% CI 0.1-0.4) risk of deterioration (incidence of deterioration: 2%). CONCLUSIONS: A model that combines HRV and laboratory values may help ED physicians evaluate risk of deterioration in patients with sepsis and merits validation and further evaluation.
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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 | 0.001 | 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 it