Mortality Risk Profiles for Sepsis: A Novel Longitudinal and Multivariable Approach
Why this work is in the frame
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Bibliographic record
Abstract
To determine if a set of time-varying biological indicators can be used to: 1) predict the sepsis mortality risk over time and 2) generate mortality risk profiles. DESIGN: Prospective observational study. SETTING: Nine Canadian ICUs. SUBJECTS: Three-hundred fifty-six septic patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical data and plasma levels of biomarkers were collected longitudinally. We used a complementary log-log model to account for the daily mortality risk of each patient until death in ICU/hospital, discharge, or 28 days after admission. The model, which is a versatile version of the Cox model for gaining longitudinal insights, created a composite indicator (the daily hazard of dying) from the "day 1" and "change" variables of six time-varying biological indicators (cell-free DNA, protein C, platelet count, creatinine, Glasgow Coma Scale score, and lactate) and a set of contextual variables (age, presence of chronic lung disease or previous brain injury, and duration of stay), achieving a high predictive power (conventional area under the curve, 0.90; 95% CI, 0.86-0.94). Including change variables avoided misleading inferences about the effects of day 1 variables, signifying the importance of the longitudinal approach. We then generated mortality risk profiles that highlight the relative contributions among the time-varying biological indicators to overall mortality risk. The tool was validated in 28 nonseptic patients from the same ICUs who became septic later and was subject to 10-fold cross-validation, achieving similarly high area under the curve. CONCLUSIONS: Using a novel version of the Cox model, we created a prognostic tool for septic patients that yields not only a predicted probability of dying but also a mortality risk profile that reveals how six time-varying biological indicators differentially and longitudinally account for the patient's overall daily mortality risk.
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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 | 0.000 | 0.001 |
| 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