Development and validation of the INCREMENT-ESBL predictive score for mortality in patients with bloodstream infections due to extended-spectrum-<b>β</b>-lactamase-producing Enterobacteriaceae
Bibliographic record
Abstract
Background: Bloodstream infections (BSIs) due to ESBL-producing Enterobacteriaceae (ESBL-E) are frequent yet outcome prediction rules for clinical use have not been developed. The objective was to define and validate a predictive risk score for 30 day mortality. Methods: A multinational retrospective cohort study including consecutive episodes of BSI due to ESBL-E was performed; cases were randomly assigned to a derivation cohort (DC) or a validation cohort (VC). The main outcome variable was all-cause 30 day mortality. A predictive score was developed using logistic regression coefficients for the DC, then tested in the VC. Results: The DC and VC included 622 and 328 episodes, respectively. The final multivariate logistic regression model for mortality in the DC included age >50 years (OR = 2.63; 95% CI: 1.18-5.85; 3 points), infection due to Klebsiella spp. (OR = 2.08; 95% CI: 1.21-3.58; 2 points), source other than urinary tract (OR = 3.6; 95% CI: 2.02-6.44; 3 points), fatal underlying disease (OR = 3.91; 95% CI: 2.24-6.80; 4 points), Pitt score >3 (OR = 3.04; 95 CI: 1.69-5.47; 3 points), severe sepsis or septic shock at presentation (OR = 4.8; 95% CI: 2.72-8.46; 4 points) and inappropriate early targeted therapy (OR = 2.47; 95% CI: 1.58-4.63; 2 points). The score showed an area under the receiver operating curve (AUROC) of 0.85 in the DC and 0.82 in the VC. Mortality rates for patients with scores of < 11 and ≥11 were 5.6% and 45.9%, respectively, in the DC, and 5.4% and 34.8% in the VC. Conclusions: We developed and validated an easy-to-collect predictive scoring model for all-cause 30 day mortality useful for identifying patients at high and low risk of mortality.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".