Significant Mortality Associated With COVID-19 and Comorbid Cerebrovascular Disease: A Quantitative Systematic Review
Bibliographic record
Abstract
We report the first quantitative systematic review of cerebrovascular disease in coronavirus disease 2019 (COVID-19) to provide occurrence rates and associated mortality. Through a comprehensive search of PubMed we identified 8 cohort studies, 5 case series, and 2 case reports of acute cerebrovascular disease in patients with confirmed COVID-19 diagnosis. Our first meta-analysis utilizing the identified publications focused on comorbid cerebrovascular disease in recovered and deceased patients with COVID-19. We performed 3 additional meta-analyses of proportions to produce point estimates of the mortality and incidence of acute cerebrovascular disease in COVID-19 patients. Patient's with COVID-19 who died were 12.6 times more likely to have a history of cerebrovascular disease. We estimated an occurrence rate of 2.6% (95% confidence interval, 1.2-5.4%) for acute cerebrovascular disease among consecutively admitted patients with COVID-19. While for those with severe COVID-19' we estimated an occurrence rate of 6.5% (95% confidence interval, 4.4-9.6%). Our analysis estimated a rate of 35.5% for in-hospital mortality among COVID-19 patients with concomitant acute cerebrovascular disease. This was consistent with a mortality rate of 34.0% which we obtained through an individual patient analysis of 47 patients derived from all available case reports and case series. COVID-19 patients with either acute or chronic cerebrovascular disease have a high mortality rate with higher occurrence of cerebrovascular disease in patients with severe COVID-19.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".