Imaging Evaluation of Clinical and Subclinical Myocarditis in Multisystem Inflammatory Syndrome of Children with COVID-19 Using Cardiac Magnetic Resonance: The Results and Prognosis
Notice bibliographique
Résumé
Background: Coronavirus disease 2019 (COVID-19) has emerged as the most significant health crisis in recent years, leading to over 6 million deaths globally due to the disease. Objectives: Given the prevalence of multisystem inflammatory syndrome in children (MISC) following the COVID-19 pandemic, this study aims to examine the imaging findings and prognoses of clinical and subclinical myocarditis in children with MISC through cardiac magnetic resonance (CMR) imaging. Patients and Methods: This prospective cohort study carried out over eighteen months from May 2021 to November 2022, included 14 patients who underwent CMR imaging. A census of all eligible patients during the study period served as the sampling method. Inclusion criteria were patients with confirmed COVID-19 infection through serological tests, polymerase chain reaction (PCR), or recent exposure to COVID-19 patients. Exclusion criteria included patients with a history of congenital heart disease (CHD) or pulmonary disease. Additional diagnostic tests performed included blood sample tests, chest X-ray (CXR), electrocardiogram (ECG), and echocardiography. CMR imaging was conducted on patients with cardiac involvement. A diagnosis of myocardial inflammation was made if a patient met at least two of the Lake Louise Criteria. The Chi-square, Fisher's exact, and Mann-Whitney tests were used to examine the relationship between quantitative variables and treatment outcomes. Additionally, the Wilcoxon signed rank, and McNemar’s tests assessed changes in echocardiography findings from admission to follow-up. A significance level of 0.05 was set. Results: Among the 14 patients studied, 8 (57.10%) were girls and 6 (42.90%) were boys. The average age was 6.03 ± 3.71 years. The median time to CMR imaging after symptom onset was 4 weeks (interquartile range (IQR): 2 - 12, range: 30). Global function assessment using left ventricular ejection fraction (LVEF) showed that 5 (35.70%), 3 (21.40%), and 6 (42.90%) patients had normal function, mild dysfunction, and significant LV dysfunction, respectively. 71.40% of patients who recovered had mild tricuspid regurgitation (TR) and no cardiomegaly. Significant differences in mean values of polymorphonuclear neutrophil (PMN) (37.71 ± 11.75 vs. 81.44 ± 13.06), lymphocytes (48.71 ± 20.08 vs. 12.51 ± 7.26), hemoglobin (Hb) (12.60 ± 1.55 vs. 10.10 ± 1.62), mean corpuscular volume (MCV) (85.90 ± 5.67 vs. 79.37 ± 5.23), erythrocyte sedimentation rate (ESR) (8.86 ± 13.60 vs. 30.29 ± 21.33), and C-reactive protein (CRP) (18.91 ± 27.25 vs. 100.57 ± 85.67) were observed between non-recovered and recovered patients, respectively (P < 0.05). However, no statistically significant association was found between other variables, including N-terminal pro–B-type natriuretic peptide (NT-proBNP), D-dimer, and Troponin I (TPI), with treatment outcomes (P > 0.05). Conclusion: Our findings indicate that a negative COVID-19 test does not exclude an established clinical COVID-19 infection in children with MISC. The results suggest that all children with MISC and a history of COVID-19 infection should undergo assessment for myocardial fibrosis, regardless of ejection fraction (EF) as determined by Echocardiography, laboratory tests, and COVID-19 test results. Strain analysis, conducted during both the acute phase and subsequent follow-ups through CMR imaging or Echocardiography, is recommended to enhance the understanding of the prognosis.
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 | 0,001 | 0,002 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».