Impact of Timeliness of Appropriateness of Initial Antimicrobials on Mortality in Neutropenic Septic Shock
Bibliographic record
Abstract
Abstract Rationale: Previous studies have demonstrated that prompt antibiotic treatment is associated with improved outcomes in septic shock. However, the precise impact of the timeliness of appropriate of initial antimicrobials in the subset of septic shock patients with neutropenia is not well defined. This study aims to evaluate the relationship between antibiotic timing and patient mortality in neutropenic septic shock. Methods: A retrospective cohort study was conducted for periods between July 1989 toJune 2018 in 29 academic and community hospitals in Canada, the United States, and Saudi Arabia. The primary outcome of this study was in-hospital mortality. Logistic regression was used to evaluate the association between antimicrobial timing in relation to the onset of persistent/recurrent hypotension with mortality, while multivariable regression analyses were performed to adjust for confounding factors, including clinical and treatment variables. Results: Among the 508 adult septic shock patients with neutropenia assessed, the overall mortality rate was 27.0%. The median time to effective antimicrobial administration from the onset of hypotension was 6.33 hours (IQR: 2.7-15.75 hours). Mortality risk was lowest, at 32.3%, for patients who received antibiotics within the first 2 hours. Delays in antibiotic administration were significantly associated with increased mortality, with an adjusted ratio of 1.045 per hour of delay (95% CI: 1.035-1.056; p < 0.0001). Compared to the 1st hrs, delays of >4-6 hrs were associated with increased mortality (p<0.001)(Fig 1).Delays extending beyond 24 hours were associated with a substantial increase in mortality risk, reaching 98.4% (p < 0.001). Conclusions: Delays in appropriate antibiotic treatment are strongly associated with increased mortality in septic shock patients with neutropenia. Figure 1: The association between delayed antimicrobial initiation and risk of mortality, demonstrates a significantly higher mortality rate for patients treated after delays of 4-6 hours compared to those treated within the first hour of hypotension.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".