Role of N-Terminal Pro-B-Type Natriuretic Peptide, High-Sensitivity C-Reactive Protein, and Inteleukin-6 in Predicting a Poor Outcome after a Stroke
Bibliographic record
Abstract
OBJECTIVE: N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hsCRP), and interleukin-6 (IL-6) concentrations can be important biomarkers in the acute stroke setting. In acute ischemic and hemorrhagic stroke patients, we investigated the association of NT-proBNP, hsCRP, and IL-6 serum concentrations with stroke severity and functional and cognitive outcomes at discharge. METHODS: Seventy-eight patients (53 men; median age 72 years) admitted with ischemic or hemorrhagic stroke within 48 h of symptom onset were evaluated for clinical stroke severity (Scandinavian stroke scale; SSS), functional status before the stroke (modified Rankin scale; mRS), and cerebrovascular disease risk factors. Cognitive (Mini Mental State Examination) and functional (mRS) outcomes were evaluated at hospital discharge. Blood samples were drawn for the assessment of NT-proBNP, hsCRP, and IL-6 concentrations within 24 h of admission. RESULTS: Greater NT-proBNP and hsCRP serum concentrations were associated with greater clinical stroke severity, adjusting for the patients' gender, age, stroke type, mRS score on admission, and presence of heart failure (β = -0.292, p = 0.012; β = -0.303, p = 0.009). In multivariate adjusted regression models with IL-6, hsCRP, and NT-proBNP considered together, IL-6 and hsCRP remained associated with worse functional (β = 0.210, p = 0.022) and cognitive (β = -0.269, p = 0.014) outcomes at discharge, respectively. In receiver operating characteristic analyses, the investigated blood biomarkers produced a minimal increase in predictive values for outcomes at discharge above the SSS score, age, and gender. CONCLUSIONS: In acute stroke patients, greater NT-proBNP and hsCRP serum concentrations are independently associated with greater clinical stroke severity. Elevated concentrations of IL-6 and hsCRP are associated with worse functional and cognitive outcomes at discharge, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".