Analysis of neuropsychological and laboratory parameters in patients with cerebrovascular disease and SARS-CoV-2 compared to those without SARS-CoV-2
Notice bibliographique
Résumé
Background. Severe acute respiratory syndrome сoronavіrus 2 (SARS-CoV-2, formerly known as 2019-nCoV) is the cause of coronavirus disease 2019 (COVID-19), and was first reported in Wuhan, China. However, it is also contagious to humans and spreads rapidly around the world through close contact between infected people or through a relatively simple transmission mechanism (airborne transmission). COVID-19 is known to affect almost all systems of the human body. Initial reports suggest that hypertension may be a risk factor for susceptibility to SARS-CoV-2 infection, a more severe course of COVID-19, and increased mortality associated with COVID-19. It is estimated that 1–3 % of COVID-19 patients experience transient ischemic attacks with a frequency similar to other coronavirus infections (SARS-CoV-1 and MERS-CoV). The cause of ischemic stroke associated with COVID-19 is unknown, but previous studies have suggested that an inflammatory cytokine storm may cause hypercoagulation and endothelial damage. We see that COVID-19 is closely related to neurological complications because there are potential factors that can cause them. Materials and methods. Cerebrovascular diseases were analyzed in 111 patients infected with SARS-CoV-2 (n = 71) and those without a history of SARS-CoV-2 (n = 40). The subject of the study was neuropsychological and laboratory indicators. The following methods were used: psychometric — Beck Anxiety Inventory, Hamilton Depression Rating Scale, Fatigue Assessment Scale; neuropsychological — Mini-Mental State Examination, Montreal Cognitive Assessment, Frontal Assessment Battery; clinical — neurological status; polymerase chain reaction to detect COVID-19 RNA; statistical methods. Results. In patients who suffered transient ischemic attack and ischemic stroke with a minimal neurological deficit and COVID-19, there were elevations in the erythrocyte sedimentation rate, leukocytes, segmented neutrophils, while an increase in C-reactive protein was noted in all participants with cerebrovascular disease and COVID-19, with more significant levels among those with ischemic stroke. All subgroups with COVID-19 showed an increase in D-dimer and fibrinogen with higher content in patients after ischemic stroke. Also in this subgroup, the procalcitonin index exceeded the norm, which indicates the severity of the course of COVID-19 with the addition of co-infection. Data of neuropsychological tests in patients with ischemic stroke with a minimal neurological deficit with SARS-CoV-2 revealed a decrease in the Montreal Cognitive Assessment score, indicating mild cognitive changes in these patients. The level of anxiety in patients with hypertension with frequent crises and ischemic stroke with a minimal neurological deficit was above the reference values, with a slight predominance in patients who did not have COVID-19. It follows that both laboratory and neuropsychological parameters differed in three subgroups depending on cerebrovascular disease, as well as the presence and absence of SARS-CoV-2, which makes it possible to develop more appropriate diagnostic methods in order to predict the course and outcome of COVID-19.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».