Effect of COVID-19 on Acute Ischemic Stroke Severity and Mortality in 2020: Results From the 2020 National Inpatient Sample
Bibliographic record
Abstract
Background: There is a paucity of nationally representative data regarding the impact of COVID-19 on acute ischemic stroke (AIS) outcome. Methods: We created a cross-sectional cohort of nationally weighted National Inpatient Sample nonelective hospital discharges aged ≥18 years with a diagnosis of ischemic stroke from 2016 to 2020. The outcome was in-hospital mortality and exposure was COVID-19 status. To understand the effect of COVID-19 on AIS severity, we report National Institutes of Health Stroke Scale by exposure status. In a final analysis, we used a nationally weighted logistic regression and marginal effects to compare April to December 2020 to the same period in 2019 to understand how the pandemic modified the effect of race and ethnicity and median household income on in-hospital AIS mortality. Results: We observed significantly higher AIS mortality in 2020 than prior years (2020 versus 2016-19, 7.3% versus 6.3%, P <0.001) and higher National Institutes of Health Stroke Scale in those with COVID-19 than those without (mean: 9.7±9.1 versus 6.6±7.4, P <0.001), but patients with AIS without COVID in 2020 had only marginally higher mortality (2020 versus 2016–2019, 6.6% versus 6.3%, P =0.001). Comparing April to December 2020 to 2019, the adjusted risk of in-hospital AIS mortality was most notably increased in Hispanics (2020 versus 2019: 9.2% versus 5.8%, P <0.001) and the lowest quartile of income (2020 versus 2019: 8.0% versus 6.0%, P <0.001). Conclusions: In-hospital stroke mortality increased in 2020 in the United States because of comorbid AIS and COVID-19, which had higher stroke severity. The increase in AIS mortality during April-December 2020 was significantly more pronounced in Hispanics and those in the lowest quartile of household income.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".