Acute COVID-19 and the Incidence of Ischemic Stroke and Acute Myocardial Infarction
Bibliographic record
Abstract
◼ stroke R ecent studies have linked coronavirus disease 2019 (COVID-19) infection with an increased risk of ischemic stroke and acute myocardial infarction (AMI). 1,2However, the evidence base is small and current data are limited mainly to case reports and 2 cohort studies. 1-4Therefore, in a nationwide registerbased study considering all patients diagnosed with COVID-19 at Danish hospitals, we assessed the association between COVID-19 infection and the risk of ischemic stroke and AMI during the acute phase of infection using the self-controlled case series method. 5 We used Danish nationwide registers to identify all patients diagnosed at Danish hospitals with a positive test for COVID-19 infection up to July 16, 2020 (International Classification of Diseases-10 codes: B342, B342A, B972, B972A).From this population, we identified all patients who were admitted to the hospital with either a primary or secondary diagnosis of first-ever ischemic stroke (International Classification of Diseases-10 codes: I63 through I66) or first-ever AMI (International Classification of Diseases-10 code: I21) up to 180 days before COVID-19 diagnosis and until the end of available data (July 16, 2020).If a patient experienced >1 outcome during the observation period, only the first was considered.We based our statistical analysis on the self-controlled case series design. 5This design is ideal for assessing the effect of transient exposures such as infections, because each patient acts as his or her own control.Consequently, all confounders, even if unmeasured, are natively controlled for as long as they do not vary within the observation period. 5 We defined the risk interval as the 14 days after the date of laboratory-confirmed COVID-19 diagnosis.The control interval was defined as up to 180 days before COVID-19 diagnosis and until the end of available data (July 16, 2020), excluding the risk interval.The date of COVID-19 diagnosis was used as the index date for defining the exposure.The relative incidence of AMI and ischemic stroke associated with the risk interval was calculated using a conditional Poisson regression model comparing the incidence within the risk interval with the incidence in the control interval. 5We conducted several sensitivity analyses to ascertain the robustness of our results, including controlling for calendar time in 3-month bands, varying the risk interval, varying the control interval, introducing preexposure periods, and restricting the analysis to only consider the time period after the first case of confirmed CO-VID-19 infection was diagnosed in Denmark (February 27, 2020; Table).According to Danish law, purely register-based studies do not require informed consent or approval by an ethics review board.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".