Impact of COVID-19 on Rheumatic Diseases in India: Determinants of Mortality and Adverse Outcome: A Retrospective, Cross-Sectional Cohort Study
Notice bibliographique
Résumé
Introduction: There is varying impact of COVID19 on world population depending on ethnicity, age and underlying co-morbidities. However, the lack of data regarding the effect of COVID on patients with rheumatological disorders (RDs) from different nations adds to uncertainty on disease outcome. Across the world, many rheumatology associations have joined hands to collate-related information. A national database under Indian Rheumatology Associations (IRAs) was developed to understand the impact of underlying RD and immunosuppressants during the COVID pandemic on its severity and outcome in our country. Methods: All registered members of IRA were invited to participate in this registry and provide information of reverse transcription–polymerase chain reaction confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV2)-infected RD patients using an online platform https://iradatabaseard.in/iracovid/index.php. The results of the data were analyzed using the appropriate statistics. Multivariate logistic regression was used to analyze the impact of different variables on mortality. Odds ratio and 95% confidence interval were used to define risk of death. Results: In this retrospective cross-sectional study, data for 447 RD patients who were infected with SARS-CoV2 data were available as of December 1, 2020. The mean age was 47.9 ± 14.4 years, including two children and 93 (20.8%) geriatric age group patients, male: female ratio was 0.4:1 and mean disease duration was 79.3 ± 77.1 months. Rheumatoid arthritis was the most common RD. Underlying disease was quiescent in 54.7% and active in 18.4% patients. Most common medications at the time of COVID diagnosis were steroids (57.76%) and hydroxychloroquine (67.34%). Fever and cough were the most common symptoms. More than half of the patients (54.4%) needed hospitalization. Oxygen requirement was noted in 18.8%, intensive care unit admission, and invasive ventilation was needed in 6.0%, and 2.9% patients, respectively. Complete recovery was seen in 95.5% of patients and 4.47% (n = 20) expired due to COVID. The presence of comorbidity, dyspnea, and a higher neutrophil count was statistically significantly associated with death (P < 0.05). None of the other factors affected COVID-19 outcome. Conclusion: This is the largest cohort from a single nation looking at the interface between RD and COVID. The results indicate that patients with RD do not show increased mortality despite current use of disease-modifying anti-rheumatic drugs/immunosuppressants.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».