Age and Acute Ischemic Stroke Outcome in North American Patients With COVID‐19
Bibliographic record
Abstract
Background Acute ischemic stroke (AIS) in the context of COVID‐19 has received considerable attention for its propensity to affect patients of all ages. We aimed to evaluate the effect of age on functional outcome and mortality following an acute ischemic event. Methods and Results A prospectively maintained database from comprehensive stroke centers in Canada and the United States was analyzed for patients with AIS from March 14 to September 30, 2020 who tested positive for SARS‐CoV‐2. The primary outcome was Modified Rankin Scale score at discharge, and the secondary outcome was mortality. Baseline characteristics, laboratory values, imaging, and thrombectomy workflow process times were assessed. Among all 126 patients with COVID‐19 who were diagnosed with AIS, the median age was 63 years (range, 27–94). There were 35 (27.8%) patients with AIS in the aged ≤55 years group, 47 (37.3%) in the aged 56 to 70 group, and 44 (34.9%) in the aged >70 group. Intravenous tissue plasminogen activator and thrombectomy rates were comparable across these groups, ( P =0.331 and 0.212, respectively). There was a significantly lower rate of mortality between each group favoring younger age (21.9% versus 45.0% versus 48.8%, P =0.047). After multivariable adjustment for possible confounders, a 1‐year increase in age was significantly associated with fewer instances of a favorable outcome of Modified Rankin Scale 0 to 2 (odds ratio [OR], 0.95; 95 CI%, 0.90–0.99; P =0.048) and higher mortality (OR, 1.06; 95 CI%, 1.02–1.10; P =0.007). Conclusions AIS in the context of COVID‐19 affects young patients at much greater rates than pre‐pandemic controls. Nevertheless, instances of poor functional outcome and mortality are closely tied to increasing age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".