A Sepsis Treatment Algorithm to Improve Early Antibiotic De-escalation While Maintaining Adequacy of Coverage (Early-IDEAS): A Prospective Observational Study
Bibliographic record
Abstract
ABSTRACT Background Empiric antibiotic treatment selection should provide adequate coverage for potential pathogens while minimizing unnecessary broad-spectrum antibiotic use. We sought to pilot a rule- and model-based early sepsis treatment algorithm (Early-IDEAS) to make optimal individualized antibiotic recommendations. Methods The Early-IDEAS decision support algorithm was derived from previous Gram-negative and Gram-positive prediction rules and models. The Gram-negative prediction consists of multiple parametric regression models which predict the likelihood of susceptibility for each commonly used antibiotic for Gram-negative pathogens, based on epidemiologic predictors and prior culture results and recommends the narrowest spectrum agent that exceeds a predefined threshold of adequate coverage. The Gram-positive rules direct the addition or cessation of vancomycin based on prior culture results. We applied the algorithm to prospectively identified consecutive adults within 24-hours of suspected sepsis. The primary outcome was the proportion of patients for whom the algorithm recommended de-escalation of the primary antibiotic regimen. Secondary outcomes included: (1) the proportion of patients for whom escalation was recommended; (2) the number of recommended de-escalation steps along a pre-specified antibiotic cascade; and (3) the adequacy of therapy in the subset of patients with culture-confirmed infection. Results We screened 578 patients, of whom 107 eligible patients with sepsis were included. The Early-IDEAS treatment recommendation was informed by Gram-negative models in 76 (71%) of patients, Gram-positive rules in 66 (61.7%), and local guidelines in 27 (25%). Antibiotic de-escalation was recommended by the algorithm in almost half of all patients (n=50, 47%), no treatment change was recommended in 48 patients (45%), and escalation was recommended in 9 patients (8%). Amongst the patients where de-escalation was recommended, the median number of steps down the a priori antibiotic treatment cascade was 2. Among the 17 patients with relevant culture-positive blood stream infection, the clinician prescribed regimen provided adequate coverage in 14 (82%) and the algorithm recommendation would have provided adequate coverage in 13 (76%), p=1. Among the 25 patients with positive relevant (non-blood) cultures, the clinician prescribed regimen provided adequate coverage in 22 (88%) and the algorithm recommendation would have provided adequate coverage in 21 (84%), p=1. Conclusions An individualized decision support algorithm in early sepsis could lead to substantial antibiotic de-escalation without compromising adequate antibiotic coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".