Impact of different consensus definition criteria on sepsis diagnosis in a cohort of critically ill patients—Insights from a new mathematical probabilistic approach to mortality-based validation of sepsis criteria
Bibliographic record
Abstract
BACKGROUND: Sepsis-3 definition uses SOFA score to discriminate sepsis from uncomplicated infection, replacing SIRS criteria that were criticized for being inaccurate. Eligibility of sepsis-3 criteria for sepsis diagnosis and the applied validation methodology using mortality as endpoint are topic of ongoing debate. We assessed the impact of different criteria on sepsis diagnosis in our ICU and devised a mathematical approach for mortality-based validation of sepsis criteria. As infectious status is often unclear at clinical deterioration, we integrated non-infected patients into analysis. METHODS: Suspected infection, SOFA and SIRS were captured for an ICU cohort of a university center over one year. For raw scores (SIRS/SOFA) and sepsis criteria (SIRS≥2/SOFA≥2/SOFA_change≥2) frequencies and associations with in-hospital mortality were assessed. Using a mathematical approach, we estimated the correlation between sepsis and in-hospital mortality serving as reference for evaluation of observed mortality correlations of sepsis criteria. RESULTS: Of 791 patients, 369 (47%) were infected and 422 (53%) non-infected, with an in-hospital mortality of 39% and 15%. SIRS≥2 indicated sepsis in 90% of infected patients, SOFA≥2 in 99% and SOFA_change≥2 in 77%. In non-infected patients, SIRS, SOFA and SOFA_change were ≥2 in 78%, 88% and 58%. In AUROC analyses neither SOFA nor SIRS displayed superior mortality discrimination in infected compared to non-infected patients. The mathematically estimated correlation of sepsis and in-hospital mortality was 0.10 in infected and 0 in non-infected patients. Among sepsis criteria, solely SIRS≥2 agreed with expected correlations in both subgroups (infected: r = 0.19; non-infected: r = 0.02). CONCLUSIONS: SOFA≥2 yielded a more liberal sepsis diagnosis than SIRS≥2. None of the criteria showed an infection specific occurrence that would be essential for reliable sepsis detection. However, SIRS≥2 matched the mortality association pattern of a valid sepsis criterion, whereas SOFA-based criteria did not. With this study, we establish a mathematical approach to mortality-based evaluation of sepsis criteria.
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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 | 0.108 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".