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Enregistrement W4317207572 · doi:10.1101/2023.01.16.23284620

Long-term cardiac symptoms following COVID-19: a systematic review and meta-analysis

2023· review· en· W4317207572 sur OpenAlexaboutno aff
Boya Guo, Chenya Zhao, Mike Z. He, Camilla Senter, Zhenwei Zhou, Song Li, Annette L. Fitzpatrick, Sara Lindström, Rebecca C. Stebbins, Grace A. Noppert, Chihua Li

Notice bibliographique

RevuemedRxiv · 2023
Typereview
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineMeta-analysisChest painConfidence intervalSample size determinationSystematic reviewStratified samplingCoronavirus disease 2019 (COVID-19)Internal medicinePhysical therapyMEDLINEPathologyDiseaseStatistics

Résumé

récupéré en direct d'OpenAlex

Abstract Background There is growing body of literature on the long-term cardiac symptoms following COVID-19. We conducted a systematic review and meta-analysis to synthesize and evaluate related evidence to inform clinical management and future studies. Methods We searched two preprint and seven peer-reviewed article databases from January 1, 2020 to January 8, 2022 for studies investigating cardiac symptoms that persisted for at least 4 weeks among individuals who survived COVID-19. A customized Newcastle–Ottawa scale was used to evaluate the quality of included studies. Random-effects meta-analyses were performed to estimate the proportion of symptoms with 95% confidence intervals (CI), and stratified analyses were conducted to quantify the proportion of symptoms by study characteristics and quality. Results A total of 101 studies describing 49 unique long-term cardiac symptoms met the inclusion criteria. Based on quality assessment, only 15.8% of the studies (n=16) were of high quality, and most studies scored poorly on sampling representativeness. The two most examined symptoms were chest pain and arrhythmia. Meta-analysis showed that the proportion of chest pain was 10.1% (95% CI: 6.4-15.5) and arrhythmia was 9.8% (95% CI: 5.4-17.2). Stratified analyses showed that studies with low-quality score, small sample size, unsystematic sampling method, and cross-sectional design were most likely to report high proportions of symptoms. For example, the proportion of chest pain was 21.3% (95% CI: 10.5-38.5), 9.3% (95% CI: 6.0-14.0), and 4.0% (95% CI: 1.3-12.0) in studies with low, medium, and high-quality scores, respectively. Similar patterns were observed for other cardiac symptoms including hypertension, cardiac abnormalities, myocardial injury, thromboembolism, stroke, heart failure, coronary disease, and myocarditis. Discussion There is a wide spectrum of long-term cardiac symptoms following COVID-19. Findings of existing studies are strongly related to study quality, size and design, underscoring the need for high-quality epidemiologic studies to characterize these symptoms and understand their etiology. Research in context Evidence before this study Accumulating evidence shows long-term cardiac symptoms following COVID-19. However, no previous reviews systematically evaluated and synthesized findings from studies on long-term cardiac symptoms. Added value of this study This is the first systematic review and meta-analysis that focused on studies of long-term cardiac symptoms of COVID-19. We included 101 studies and identified 49 cardiac symptoms that are indicative of cardiac abnormalities. We scored their quality based on epidemiologic principles and identified domains of study design that need improvements. We quantified proportions of multiple long-term cardiac symptoms including chest pain, arrhythmia, and others. We also observed systematic differences in reported proportions of these symptoms by selected study characteristics, including total quality assessment score, sample size, sampling representativeness, and study design. High-quality studies identified here can provide important guidelines for future studies of long-term symptoms following COVID-19. Implications of all the available evidence Multiple domains of study design, especially sampling representativeness, need to be improved in future studies on long-term cardiac symptoms following COVID-19. Notably, low-quality and smaller studies tend to report a larger proportion of symptoms, are more likely to be subject to greater sampling variation, and hence are less precise. These studies should be revisited with the emergence of large studies with rigriours study designs. This systematic review and meta-analysis highlight the scope of persistent cardiac symptoms among those who survived the acute phase of COVID-19, and the importance of synthesizing rigorous evidence to inform post-COVID surveillance and management plans.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,022
score de la tête « metaresearch » (Gemma)0,056
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,118

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0220,056
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0180,034
Bibliométrie0,0080,008
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,087
Tête enseignante GPT0,404
Écart entre enseignants0,317 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2023
Routes d'admission1
Résumé présentoui

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