Acute COVID-19 and the Incidence of Ischemic Stroke and Acute Myocardial Infarction
Notice bibliographique
Résumé
◼ myocardial infarction ◼ stroke R ecent studies have linked coronavirus disease 2019 (COVID-19) infection with an increased risk of ischemic stroke and acute myocardial infarction (AMI).1,2 However, the evidence base is small and current data are limited mainly to case reports and 2 cohort studies.[1][2][3][4] Therefore, 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.5 This 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.5 We 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.The data used for this study are governed by The Danish Health Data Authority and Statistics Denmark and cannot be made available on request by the authors.Access to the data may only be provided through direct submission of a formal request to these agencies.A total of 5119 patients diagnosed with COVID-19 at Danish hospitals were identified.A total of 1945 patients (38%) had received an inpatient diagnosis
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».