Alterations in Laboratory Parameters Related to Disease Severity in Vaccinated Patients Against SARS-CoV-2
Notice bibliographique
Résumé
Background: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide with global financial and health care systems consequences. It is already well recognized that immunization against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a precondition for blocking mutations and prevent the emergence of variants. The aim of the study was to investigate the possible relationship between COVID-19 vaccines and the commonly used disease-related blood biomarkers. Methods: Adult patients with confirmed SARS-CoV-2 infection who were hospitalized from November 8, 2021, to December 31, 2021, were included. The retrospective study was conducted in Patras University Hospital, Greece. Two groups of patients were assessed, the ones who were previously vaccinated against SARS-CoV-2 (group A, n = 21), and those who were not (group B, n = 55). After analysis of peripheral blood, we calculated on admission day for each patient the total white blood cell (WBC), absolute lymphocytes count (ALC), absolute monocyte count, D-dimers, C-reactive protein (CRP) plasma levels, lactate dehydrogenase (LDH), ferritin, high-sensitive troponin, as well as the arterial oxygen partial pressure/fractional inspired oxygen (PO 2 /FiO 2 ) ratio. Results: The median age of all patients was 65.3 ± 15.2 years old; 68.4% were men and 31.6% were women. Comorbidities were present in 51 patients (67.1%). Hypertension and diabetes were observed as the most common comorbidities (33.3%). About 72.4% of the patients were unvaccinated or have received the first dose of vaccine, and 27.6% were completely vaccinated. No statistical difference was found in the total WBC count and ALC between the two groups (group A vs. group B: 8,168.95 ± 7,584.4 vs. 8,521.9 ± 6,571.3, P = 0.848 and 3,052.1 ± 7,230.7 vs. 1,279.6 ± 1,218.6, P = 0.087). Monocytes count in both groups did not show statistical difference: group A vs. group B: 672.6 ± 384.7 vs. 637.9 ± 477.8 (P = 0.754). Similarly, no difference for D-dimers (1,348.5 ± 1,397.6 vs. 1,850.9 ± 3,877.5, P = 0.575), ferritin (1,082.8 ± 1,399.5 vs. 1,327.4 ± 1,307.8, P = 0.508), high-sensitive troponin (113.6 ± 318.1 vs. 157.5 ± 48.8, P = 0.252), and CRP (6.92 ± 4.9 vs. 7.4 ± 5.9, P = 0.732). For LDH plasma levels, the statistical difference was significant (274.2 ± 85.6 vs. 387.5 ± 223.4, P = 0.003), as well as for the PO 2 /FiO 2 ratio (355.6 ± 129.7 vs. 260.5 ± 123.3, P = 0,006). Conclusions: In a mixed population hospitalized for COVID-19, only LDH plasma levels and the PaO 2 /FiO 2 on admission day showed statistically significant difference between vaccinated and unvaccinated patients. Although unvaccinated patients are more likely to develop severe illness, they did not express significantly higher values of commonly used plasma biomarkers such as ferritin, CRP, and D-dimers which are related to disease severity. J Clin Med Res. 2022;14(11):487-491 doi: https://doi.org/10.14740/jocmr4821
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».