Abstract 33: Risk Scores And Brain Mri Markers In Distinguishing Ischemic Stroke And Intracerebral Hemorrhage Risk Among Atrial Fibrillation Patients: The Neuro-Afib Study
Bibliographic record
Abstract
Background: Recent studies cast doubt on the accuracy of the most-commonly used risk scores (CHA 2 DS 2 -VASC and HAS-BLED) in differentiating the risk of acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH) among patients with fibrillation (AF). Because of the importance of AIS/ICH risk determination for choice of proper preventive approaches, we aimed to compare the value of these risk scores and brain MRI markers to differentiate the occurrence of AIS and ICH in a large cohort of AF-related strokes. Methods: The Neuro-AFib study is a multicenter effort to elucidate the causes and consequences of AIS and ICH in AF patients. Demographics, CHA 2 DS 2 -VASC and HAS-BLED scores, and ischemic/hemorrhagic brain MRI markers were compared between AF patients admitted with AIS and ICH to 15 academic stroke centers in the US between 1/2018-12/2019. Results: Of 5694 stroke admissions with AF, 4826 (84.8%) had AIS and 868 (15.2%) ICH. Mean age was similar between groups (75.9±11.5 vs 76.6±11.9, p=0.1), more ICH patients were male (57% vs 50%). Pre-index event CHA 2 DS 2 -VASC (4.14±1.6 vs 4.22±1.6) and HAS-BLED (2.71±1.09 vs 2.68±1.13) were similar between groups [both p>0.2]. Cerebral microbleeds (CMB, 56% vs 33.5%), cortical superficial siderosis (cSS, 15% vs 9.4%), and moderate-to-severe leukoaraiosis (41% vs 33.4%) were more commonly found among ICH patients compared to AIS (all p<0.001). Chronic lacunar infarcts (43.5% vs 39.5%, p=0.03) and chronic non-lacunar infarcts (29% vs 18%, p<0.001) were more commonly found in AIS. In a multivariable logistic regression model that included all variables above, male sex, presence of CMBs, cSS, moderate-to-severe leukoaraiosis were associated with ICH, chronic non-lacunar infarcts with AIS (all p<0.005), while CHA 2 DS 2 -VASC (p=0.9) and HAS-BLED (p=0.9) were not related to the stroke type. Conclusions: Data from our multicenter study confirm the lack of specificity of CHA 2 DS 2 -VASC and HAS-BLED to categorize the risk of AIS vs ICH in AF patients. The chronic MRI findings (CMB, cSS, moderate-to-severe leukoaraiosis, chronic infarcts) should be incorporated into risk scores, and their predictive value for AIS and ICH should be investigated in prospective studies to select optimal stroke prevention methods in AF 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 | 0.002 | 0.004 |
| 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".