Risk Score for In-Hospital Ischemic Stroke Mortality Derived and Validated Within the Get With The Guidelines–Stroke Program
Bibliographic record
Abstract
BACKGROUND: There are few validated models for prediction of in-hospital mortality after ischemic stroke. We used Get With the Guidelines-Stroke Program data to derive and validate prediction models for a patient's risk of in-hospital ischemic stroke mortality. METHODS AND RESULTS: Between October 2001 and December 2007, there were 1036 hospitals that contributed 274,988 ischemic stroke patients to this study. The sample was randomly divided into a derivation (60%) and validation (40%) sample. Logistic regression was used to determine the independent predictors of mortality and to assign point scores for a prediction model. We also separately derived and validated a model in the 109,187 patients (39.7%) with a National Institutes of Health Stroke Scale (NIHSS) score recorded. Model discrimination was quantified by calculating the C statistic from the validation sample. In-hospital mortality was 5.5% overall and 5.2% in the subset in which NIHSS score was recorded. Characteristics associated with in-hospital mortality were age, arrival mode (eg, via ambulance versus other mode), history of atrial fibrillation, previous stroke, previous myocardial infarction, carotid stenosis, diabetes mellitus, peripheral vascular disease, hypertension, history of dyslipidemia, current smoking, and weekend or night admission. The C statistic was 0.72 in the overall validation sample and 0.85 in the model that included NIHSS score. A model with NIHSS score alone provided nearly as good discrimination (C statistic 0.83). Plots of observed versus predicted mortality showed excellent model calibration in the validation sample. CONCLUSIONS: The Get With the Guidelines-Stroke risk model provides clinicians with a well-validated, practical bedside tool for mortality risk stratification. The NIHSS score provides substantial incremental information on a patient's short-term mortality risk and is the strongest predictor of mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".