An Algorithm for Systemic Inflammatory Response Syndrome Criteria–Based Prediction of Sepsis in a Polytrauma Cohort*
Bibliographic record
Abstract
OBJECTIVES: Lifesaving early distinction of infectious systemic inflammatory response syndrome, known as "sepsis," from noninfectious systemic inflammatory response syndrome is challenging in the ICU because of high systemic inflammatory response syndrome prevalence and lack of specific biomarkers. The purpose of this study was to use an automatic algorithm to detect systemic inflammatory response syndrome criteria (tachycardia, tachypnea, leukocytosis, and fever) in surgical ICU patients for ICU-wide systemic inflammatory response syndrome prevalence determination and evaluation of algorithm-derived systemic inflammatory response syndrome descriptors for sepsis prediction and diagnosis in a polytrauma cohort. DESIGN: Cross-sectional descriptive study and retrospective cohort study. SETTING: Electronic medical records of a tertiary care center's surgical ICU, 2006-2011. PATIENTS: All ICU admissions and consecutive polytrauma admissions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Average prevalence of conventional systemic inflammatory response syndrome (≥ 2 criteria met concomitantly) from cross-sectional application of the algorithm to all ICU patients and each minute of the study period was 43.3%. Of 256 validated polytrauma patients, 85 developed sepsis (33.2%). Three systemic inflammatory response syndrome descriptors summarized the 24 hours after admission and before therapy initiation: 1) systemic inflammatory response syndrome criteria average for systemic inflammatory response syndrome quantification over time, 2) first-to-last minute difference for trend detection, and 3) change count reflecting systemic inflammatory response syndrome criteria fluctuation. Conventional systemic inflammatory response syndrome for greater than or equal to 1 minute had 91% sensitivity and 19% specificity, whereas a systemic inflammatory response syndrome criteria average cutoff value of 1.72 had 51% sensitivity and 77% specificity for sepsis prediction. For sepsis diagnosis, systemic inflammatory response syndrome criteria average and first-to-last minute difference combined yielded 82% sensitivity and 71% specificity compared with 99% sensitivity and only 31% specificity of conventional systemic inflammatory response syndrome from a nested case-control analysis. CONCLUSIONS: Dynamic systemic inflammatory response syndrome descriptors improved specificity of sepsis prediction and particularly diagnosis, rivaling established biomarkers, in a polytrauma cohort. They may enhance electronic sepsis surveillance once evaluated in other patient populations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".