Abstract 70: Predicting Recurrent Stroke After TIA And Minor Stroke: Results Of The Prospective CT And MRI In The Triage Of TIA And Minor Cerebrovascular Events To Identify High Risk Patients (CATCH) Study.
Bibliographic record
Abstract
Background. TIA and minor stroke have a substantial risk of recurrent stroke, particularly within the first 48 hours. There is therefore a need to identify the highest risk patients urgently to implement early treatments. Imaging using MRI can identify patients at high risk for a recurrent stroke. However MRI is not available emergently in many institutions. If CT/CTA could identify high-risk patients then this would be more widely applicable. Methods. 510 consecutive TIA and minor stroke patients who were assessed by a stroke neurologist and had a CT/CTA completed within 24 hours of onset were prospectively enrolled. 420 had baseline brain MRI completed also. We used multiple imputation based on a previous meta-analysis of predictors of DWI positivity (motor or speech symptoms, atrial fibrillation, symptomatic carotid stenosis >=50% and symptom duration greater than 60 minutes) to impute MRI results for patients without baseline MRI. We assessed the risk of recurrent stroke within 90 days using standard clinical variables and predefined abnormalities on the CT/CTA at risk metric (acute ischemia on CT and/or intracranial or extracranial occlusion or stenosis >=50%) and MRI (DWI positivity). Results. There were 36 recurrent strokes (7.1% 95%CI: 5.0-9.6). Median time to event was 1 day (IQR 7.5). Median time from symptom onset to CTA was 5.5 hours (IQR: 6.4 hours), median time to MRI was 17.5 hours (IQR: 12 hours). In the univariate analysis; symptoms ongoing at first assessment, HR 2.2 (95%CI: 1.02-4.9), CT/CTA at risk metric, HR 4·0 (95%CI: 2·0-8·5) and DWI positivity 3.2 (1.3-7.6) predicted recurrent stroke. In the multivariable analysis only CT/CTA at risk metric (OR 3.6 (1.7-7.5, p=0.001) and DWI positivity (OR 2.3 (0.9-5.8) predicted recurrent stroke. Diagnostic accuracy of CT/CTA in predicting recurrent stroke was: sensitivity 67%, specificity 68%, PPV 14%, NPV 96%. Diagnostic accuracy of MRI: sensitivity 83%, specificity 40%, PPV 10%, NPV 97%. Using ROC analysis CT/CTA and MRI were not significantly different in their accuracy in prediction of recurrent stroke (0.67 versus 0.61, p=0.18). Conclusions. Early assessment of the intracranial and extracranial vasculature using CT/CTA predicts recurrent stroke and clinical outcome in patients with TIA and minor stroke. There is a trade off between sensitivity and specificity when CTA and MRI are compared. In many institutions CTA is more quickly available than MRI and given a median time to event of one day, physicians should access whichever technique is available quicker in their institution to allow implementation of aggressive secondary prevention treatments in appropriate patients.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".