Prognostic value of combining 24-hour ASPECTS, and hemoglobin to red cell distribution width ratio on the THRIVE-score in predicting in-hospital mortality among ischemic stroke patients treated with intravenous thrombolysis. v1
Bibliographic record
Abstract
Background:Acute ischemic stroke (AIS) emerges as a significant global health issue, directly impacting mortality and disability. The Total Health Risk In Vascular Events (THRIVE) score, appreciated for its simplicity and ease of use, consistently validates its ability to accurately predict stroke clinical outcomes. However, the THIRVE score is currently lacking laboratory and neuroimaging data, which limits its ability to provide precise prediction of stroke outcomes. Our study evaluates the impact of integrating the 24-hour Alberta Stroke Program Early CT Score (ASPECTS) and hemoglobin-to-red cell distribution width (HB/RDW) ratio into the THRIVE score using the multivariable fractional polynomial (MFP) method (combined THRIVE-MFP) compared to the THRIVE-c model. We aim to assess their added value in predicting in-hospital mortality (IHM) prognosis. Methods: A retrospective study from January 2015 to July 2022 examined consecutive AIS patients receiving intravenous thrombolysis. Data on THRIVE scores, 24-hour ASPECTS, and HB/RDW levels were collected upon admission. The model was constructed using logistic regression and the MFP method. The prognostic value was determined using the augmented area under the receiver operating characteristic (AuROC). Ischemic cerebral lesions within the middle cerebral artery territory were evaluated with non-contrast computed tomography (NCCT) following 24 hours of intravenous thrombolysis completion (24-hour ASPECTS). Results:Among a cohort of 345 patients diagnosed with AIS who received intravenous thrombolysis, 65 individuals (18.8%) experienced IHM. The combined THRIVE-MFP model was significantly superior to the THRIVE-c model (AuROC 0.980 vs. 0.876, P<0.001). Conclusion:The combined THRIVE-MFP model showed excellent predictive performance, offering potential for guiding treatment decisions and enhancing healthcare outcomes. Incorporating 24-hour ASPECTS on NCCT and HB/RDW proved valuable in IHM prediction, particularly for hospitals with limited access to advanced neuroimaging resources.
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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 | 0.006 |
| 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".