Incidence and Characterization of Post-COVID-19 Symptoms in Hospitalized COVID-19 Survivors to Recognize Syndemic Connotations in India: Single-Center Prospective Observational Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Long COVID, or post-COVID-19 syndrome, is the persistence of signs and symptoms that develop during or after COVID-19 infection for more than 12 weeks and are not explained by an alternative diagnosis. In spite of health care recouping to prepandemic states, the post-COVID-19 state tends to be less recognized from low- and middle-income country settings and holistic therapeutic protocols do not exist. Owing to the syndemic nature of COVID-19, it is important to characterize post-COVID-19 syndrome. OBJECTIVE: We aimed to determine the incidence of post-COVID-19 symptoms in a cohort of inpatients who recovered from COVID-19 from February to July 2021 at a tertiary-care center in South India. In addition, we aimed at comparing the prevalence of post-COVID-19 manifestations in intensive care unit (ICU) and non-ICU patients, assessing the persistence, severity, and characteristics of post-COVID-19 manifestations, and elucidating the risk factors associated with the presence of post-COVID-19 manifestations. METHODS: A total of 120 adult patients admitted with COVID-19 in the specified time frame were recruited into the study after providing informed written consent. The cohort included 50 patients requiring intensive care and 70 patients without intensive care. The follow-up was conducted on the second and sixth weeks after discharge with a structured questionnaire. The questionnaire was filled in by the patient/family member of the patient during their visit to the hospital for follow-up at 2 weeks and through telephone follow-up at 6 weeks. RESULTS: The mean age of the cohort was 55 years and 55% were men. Only 5% of the cohort had taken the first dose of COVID-19 vaccination. Among the 120 patients, 58.3% had mild COVID-19 and 41.7% had moderate to severe COVID-19 infection. In addition, 60.8% (n=73) of patients had at least one persistent symptom at the sixth week of discharge and 50 (41.7%) patients required intensive care during their inpatient stay. The presence of persistent symptoms at 6 weeks was not associated with severity of illness, age, or requirement for intensive care. Fatigue was the most common reported persistent symptom with a prevalence of 55.8%, followed by dyspnea (20%) and weight loss (16.7%). Female sex (odds ratio [OR] 2.4, 95% CI 1.03-5.58; P=.04) and steroid administration during hospital stay (OR 4.43, 95% CI 1.9-10.28; P=.001) were found to be significant risk factors for the presence of post-COVID-19 symptoms at 6 weeks as revealed by logistic regression analysis. CONCLUSIONS: Overall, 60.8% of inpatients treated for COVID-19 had post-COVID-19 symptoms at 6 weeks postdischarge from the hospital. The incidence of post-COVID-19 syndrome in the cohort did not significantly differ across the mild, moderate, and severe COVID-19 severity categories. Female sex and steroid administration during the hospital stay were identified as predictors of the persistence of post-COVID-19 symptoms at 6 weeks.
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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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) | 0,001 | 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 ».