Sepsis in the Neurologic Intensive Care Unit: Epidemiology and Outcome
Bibliographic record
Abstract
BACKGROUND: Sepsis is a major contributor to mortality in patients admitted to a general intensive care unit (ICU). Early recognition and treatment of sepsis is key in improving outcomes. The epidemiology and outcome of sepsis in neurologic ICU (NeuroICU) has not been evaluated. METHODS: We retrospectively identified all patients admitted to our 16-bed NeuroICU between June 2009 and December 2013 using the acute physiologic and chronic health evaluation (APACHE) outcomes database. We excluded patients admitted with an infection, such as meningitis, encephalitis, brain or spinal abscess, or with any other infection. We compared NeuroICU patients who did to NeuroICU patients who did not develop sepsis after ICU admission. The diagnosis of sepsis was based on the SCCM/ACCP consensus conference definition. RESULTS: There were a total of 2,025 patients, out of which 29 patients (1.4%) developed sepsis. Patients who developed sepsis had a trend towards older age (67 ± 13 vs. 61 ± 11 years, P = 0.07), a trend towards more male gender (69.0% vs. 51.5%, P = 0.07), significantly higher APACHE III scores (58 ± 17 vs. 43 ± 21, P = 0.0001), and significantly higher acute physiologic scores (APS) (43 ± 16 vs. 32 ± 18, P = 0.001) than patients who did not develop sepsis. Patients who developed sepsis had higher ICU mortality (41.4% vs. 5.1%, odds ratio (OR) = 13.1; 95% confidence interval (CI), 6.1 - 28.2, P < 0.0001), and higher hospital mortality (44.8% vs. 8.2%, OR = 9.0; 95% CI, 4.3 - 19.0, P < 0.0001). CONCLUSIONS: Sepsis developed in 1.4% of patients admitted to a NeuroICU. Predictors of sepsis development were comorbidities and worsening acute physiologic variables. Patients who developed sepsis had significantly higher mortality. Vigilance to development of sepsis in NeuroICU is paramount, especially in this era when early recognition and intervention of sepsis significantly improves outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".