Characteristics and Outcomes of 7620 Multiple Sclerosis Patients Admitted With COVID-19 in the United States
Bibliographic record
Abstract
Background: At the start of the COVID-19 pandemic, several experts raised concerns about its impact on Multiple Sclerosis (MS) patients. This study aims to provide a perspective using the biggest inpatient database from the United States. Method: We screened for COVID-19 cases between April to December 2020, via the 2020 National Inpatient Sample (NIS). Various outcomes were analyzed. Results: We identified 1,628,110 hospitalizations with COVID-19, including 7620 (.5%) MS patients. 8.9% of MS patients with COVID-19 died, and it was lower than non-MS cases (12.9%). Less MS patients with COVID-19 needed non-invasive ventilation (4.5% vs 6.4%) and mechanical ventilation (9.0% vs 11.2%). Furthermore, MS patients with COVID-19 reported higher odds of non-invasive ventilation if they were ≥60 years, had chronic pulmonary disease (CPD), obesity, or diabetes. Private insurance beneficiaries showed reduced risk, vs Medicare. Similarly, for mechanical ventilation, those ≥60 years, with alcohol abuse, obesity, diabetes, hypertension, or dialysis had higher odds, while females, smokers, and those with depression or hyperlipidemia showed reduced odds. The study revealed higher odds of mortality among those aged ≥60, who had CPD, obesity, CKD, or a history of old MI while females, smokers, as well as those with depression, and hyperlipidemia showed better outcomes. Blacks had lower odds, whereas Hispanics had higher odds of death, vs Whites. Medicaid and Privately insured patients had lower odds of dying vs Medicare. Conclusion: We found several differences in patient characteristics and outcomes among MS and non-MS patients with COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".