Increased Risk of Recurrent Stroke in Symptomatic Large Vessel Disease With Impaired BOLD Cerebrovascular Reactivity
Bibliographic record
Abstract
BACKGROUND: Impaired cerebrovascular reactivity (CVR) has been correlated with recurrent ischemic stroke. However, for clinical purposes, most CVR techniques are rather complex, time-consuming, and lack validation for quantitative measurements. The recent adaptation of a standardized hypercapnic stimulus in combination with a blood-oxygenation-level-dependent (BOLD) magnetic resonance imaging signal as a surrogate for cerebral blood flow offers a potential universally comparable CVR assessment. We investigated the association between impaired BOLD-CVR and risk for recurrent ischemic events. METHODS: We conducted a retrospective analysis of patients with symptomatic cerebrovascular large vessel disease who had undergone a prospective hypercapnic-challenged BOLD-CVR protocol at a single tertiary stroke referral center between June 2014 and April 2020. These patients were followed up for recurrent acute ischemic events for up to 3 years. BOLD-CVR (%BOLD signal change per mm Hg CO 2 ) was calculated on a voxel-by-voxel basis. Impaired BOLD-CVR of the affected (ipsilateral to the vascular pathology) hemisphere was defined as an average BOLD-CVR, falling 2 SD below the mean BOLD-CVR of the right hemisphere in a healthy age-matched reference cohort (n=20). Using a multivariate Cox proportional hazards model, the association between impaired BOLD-CVR and ischemic stroke recurrence was assessed and Kaplan-Meier survival curves to visualize the acute ischemic stroke event rate. RESULTS: Of 130 eligible patients, 28 experienced recurrent strokes (median, 85 days, interquartile range, 5–166 days). Risk factors associated with an increased recurrent stroke rate included impaired BOLD-CVR, a history of atrial fibrillation, and heart insufficiency. After adjusting for sex, age group, and atrial fibrillation, impaired BOLD-CVR exhibited a hazard ratio of 10.73 (95% CI, 4.14–27.81; P <0.001) for recurrent ischemic stroke. CONCLUSIONS: Among patients with symptomatic cerebrovascular large vessel disease, those exhibiting impaired BOLD-CVR in the affected hemisphere had a 10.7-fold higher risk of recurrent ischemic stroke events compared with individuals with nonimpaired BOLD-CVR.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".