A Multibiomarker-Based Outcome Risk Stratification Model for Adult Septic Shock*
Bibliographic record
Abstract
OBJECTIVES: Clinical trials in septic shock continue to fail due, in part, to inequitable and sometimes unknown distribution of baseline mortality risk between study arms. Investigators advocate that interventional trials in septic shock require effective outcome risk stratification. We derived and tested a multibiomarker-based approach to estimate mortality risk in adults with septic shock. DESIGN: Previous genome-wide expression studies identified 12 plasma proteins as candidates for biomarker-based risk stratification. The current analysis used banked plasma samples and clinical data from existing studies. Biomarkers were assayed in plasma samples obtained from 341 subjects with septic shock within 24 hours of admission to the ICU. Classification and regression tree analysis was used to generate a decision tree predicting 28-day mortality based on a combination of both biomarkers and clinical variables. The derived tree was first tested in an independent cohort of 331 subjects, then calibrated using all subjects (n = 672), and subsequently validated in another independent cohort (n = 209). SETTING: Multiple ICUs in Canada, Finland, and the United States. SUBJECTS: Eight hundred eighty-one adults with septic shock or severe sepsis. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: The derived decision tree included five candidate biomarkers, admission lactate concentration, age, and chronic disease burden. In the derivation cohort, sensitivity for mortality was 94% (95% CI, 87-97), specificity was 56% (50-63), positive predictive value was 50% (43-57), and negative predictive value was 95% (89-98). Performance was comparable in the test cohort. The calibrated decision tree had the following test characteristics in the validation cohort: sensitivity 85% (76-92), specificity 60% (51-69), positive predictive value 61% (52-70), and negative predictive value 85% (75-91). CONCLUSIONS: We have derived, tested, calibrated, and validated a risk stratification tool and found that it reliably estimates the probability of mortality in adults with septic shock.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".