The Effect of COVID-19 Hospitalisation on the Occurrence of Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background The relationship between COVID-19 hospitalisation and stroke occurrence remains incompletely understood, with reported incidence rates varying significantly across studies. This systematic review and meta-analysis aimed to comprehensively evaluate the association between COVID-19 hospitalisation and stroke occurrence, while examining key subgroup variations and potential risk factors. Methods A systematic search was conducted across PubMed, Scopus, and Web of Science databases from December 2019 to 2023. Studies reporting stroke occurrence in hospitalised COVID-19 patients were included. The Newcastle-Ottawa Scale was used for quality assessment. Random-effects meta-analysis was performed to calculate pooled odds ratios (OR) and risk ratios (RR) with 95% confidence intervals (CI). Results Nineteen studies met the inclusion criteria. The primary meta-analysis of 17 studies, comprising 98,297 patients, revealed a pooled stroke occurrence of 1% (95% CI: 1%,2%). A comparative analysis of three studies showed no significant difference in stroke risk between hospitalised COVID-19 patients and non-COVID-19 hospitalised controls (OR: 1.13, 95% CI: 0.49,2.65). However, within the COVID-19 hospitalised population, stroke risk was strongly associated with disease severity, including the need for mechanical ventilation (OR: 3.59, 95% CI: 2.21,5.83) and ICU admission (OR: 6.33, 95% CI: 4.95, 8.09). Significant pre-existing comorbidities included hypertension (OR: 2.35, 95% CI: 1.12,4.92) and atrial fibrillation (OR: 1.98, 95% CI: 1.18,3.31). Ischemic stroke was the predominant subtype, accounting for 81% of cases (95% CI: 73%,90%). Quality assessment of the 19 studies identified 9 as high quality and 10 as moderate quality. Conclusions This meta-analysis indicates that while the overall occurrence of stroke in hospitalised COVID-19 patients is approximately 1%, the risk is not significantly elevated compared to non-COVID-19 hospitalised patients. Instead, stroke occurrence is powerfully driven by the severity of the illness and the presence of traditional vascular risk factors. These findings underscore the critical need for vigilant neurological monitoring and targeted stroke prevention strategies, particularly for COVID-19 patients who require intensive care or mechanical ventilation.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".