Abstract TMP100: Biology of Stroke: Role of ELL2, GLIPR1, MAPKAPK3 Genes in Identifying Atrial Fibrillation Cause of Stroke
Bibliographic record
Abstract
Background: An accurate test to identify atrial fibrillation in ischemic stroke populations would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our goal was to utilize a database of genes appearing in literature determine if gene expression accurately differentiate patients with atrial fibrillation from those with large artery stroke. Methods: BASE enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAXgene tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, LLC, Dayton, OH) using Affymetrix HTA micro arrays. Genes were filtered to those appearing in stroke literature resulting in 543 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 3 gene diagnostic signature with robust performance conserved across literature consisting of ELL2, GLIPR1, MAPKAPK3 genes. Results: Overall, 99 patients were enrolled with NIHSS>5, 68 (69%) with atrial fibrillation cause of stroke and 31 (31%) with large artery stroke; (48%) were male, and median (IQR) age was 74.4 (66.1,81.7). Median (IQR) time from symptoms to blood collection was 420 (322, 472) minutes. Coexistent pathology at presentation included high blood pressure 84 (85%), hyperlipidemia 45 (45%), diabetes 31 (31%), and coronary artery disease 38 (38%). Three genes were able to differentiate atrial fibrillation from large vessel stroke; C-statistic 0.86 (0.52-1.0, 95% CI), sensitivity 0.93 (0.56-1.0, 95% CI) and specificity of 0.58 (0.35-0.81, 95% CI ). Conclusion: RNA expression of ELL2, GLIPR1, MAPKAPK3 genes differentiates atrial fibrillation stroke patients from those with large artery stroke, and may have therapeutic and outcome implications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".