Abstract 061: Identification of Proteins Predictive of Post‐Thrombectomy Outcome Based on an Appalachian vs. Non‐Appalachian Cohort
Bibliographic record
Abstract
Introduction The Appalachia region of North America is known to have significant health disparities, specifically, worse risk factors and outcomes for stroke. Appalachians are more likely to have comorbidities related to stroke, such as diabetes, obesity, and tobacco use, and are often less likely to have stroke interventions such as mechanical thrombectomy (MT) for emergent large vessel occlusion (ELVO). As our Comprehensive Stroke Center directly serves stroke subjects from both Appalachian and non‐Appalachian areas, we set out to identify proteomic biomarkers predictive of stroke outcomes specific to subjects residing in Appalachia. Methods Eighty‐one subjects met inclusion criteria for this study. These subjects underwent MT for ELVO, and during the procedure, carotid arterial blood samples were acquired and subsequently sent for proteomic analysis. Samples were processed in accordance with the Blood And Clot Thrombectomy Registry And Collaboration (BACTRAC; clinicaltrials.gov; NCT 03153683). Statistical analyses were utilized to examine whether relationships between protein expression and outcomes differed by Appalachian status for functional outcomes (NIH Stroke Scale; NIHSS and Modified Rankin Score; mRS), cognitive outcomes (Montreal Cognitive Assessment; MoCA), and mortality. Results No significant differences were found in demographic data nor co‐morbidities when comparing Appalachia to non‐Appalachia subjects. However, time from stroke onset to treatment (last known normal) was significantly longer in patients from Appalachia, so this datapoint was entered as a co‐variate in all predictive models. A comprehensive analysis of 184 cardiometabolic and inflammatory proteins revealed seven Appalachia‐specific proteins predictive of NIHSS, fourteen predictive of MoCA, six predictive of mRS, and seven proteins related to mortality. Specifically, within the Appalachian group, the protein multiple epidermal growth factor‐like domains protein 9 (MEGF9) was positively correlated to discharge NIHSS, elevated levels of the proteins coagulation factor XI (F11) and mannose binding protein (MBL2) were found to have an increased likelihood of worse mRS, but there were no proteins identified from the Appalachian cohort that were predictive of worse MoCA score, nor worse mortality. Conclusion Appalachia is an underserved population with significantly worse health disparities, specifically related to ischemic stroke. Our study found that patients who presented from Appalachian regions have a different proteomic response at time of MT when compared to otherwise similar subjects presenting from non‐Appalachian communities. These differentially expressed proteins could be used as prognostic biomarkers as well as novel therapies targeted at an underserved population. Lastly, expression differences may be in part related to environmental exposures, such as coal pollution, which will require additional studies moving forward.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".