Impact of COVID-19 on Rheumatic Diseases in India: Determinants of Mortality and Adverse Outcome: A Retrospective, Cross-Sectional Cohort Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".