1126. Effect of Remdesivir on Recovery, Quality of Life, and Long-COVID Symptoms One Year after Hospitalization for COVID-19 Infection: A Randomized Controlled SOLIDARITY Finland Trial
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (COVID-19) patients frequently suffer from long-term sequelae, often called “long COVID” or “post COVID-19 condition”. Remdesivir, given in early disease, decreases the risk of hospitalization and potentially mortality. No randomized trials have thus far published long-term follow-up data on any COVID-19 drug treatment. We investigated the effects of remdesivir on a range of patient-important outcomes at one year. Methods Between July 2020 and January 2021, an open-label randomized multicenter trial in Finland recruited 208 adult patients from 11 Finnish hospitals. Patients were randomly assigned (1:1 ratio) to standard of care (SoC)with remdesivir (median duration of remdesivir treatment 5 days) or SoC alone. Primary outcomes were self-reported recovery, exertional dyspnea, fatigue, and quality of life at one year. Secondary outcomes were overall mortality and several potential long-COVID symptoms. Results At one year, 5 (4.4%) of 114 patients in the remdesivir and 5 (5.3%) of 94 in the SoC group had died (RR 0.82, 95% CI 0.25-2.76; absolute difference: -0.9%, 95% CI -7.9-5.3); 181 (92% of survivors) completed the follow-up. Self-reported recovery (fully or largely) occurred in 85% in remdesivir and in 86% in SoC (RR 0.94, 0.47-1.90; absolute difference: -0.9%, 95% CI -11%-10%). Exertional dyspnea occurred in 5% in remdesivir and 8% in SoC (OR 0.61, 95% CI 0.20-1.85; absolute difference -3.3%, 95% CI -12%-4.4%). We found no convincing difference between remdesivir and SoC groups in quality of life or symptom outcomes (p > 0.05 for all). Of the 21 potential long-COVID symptoms, patients often reported moderate or major bother from fatigue (26%), joint pain (22%), persistent respiratory mucus (21%), and problems with memory (19%) and attention/concentration (18%) (Figure). Bother from potential long-COVID symptoms at one year from COVID-19 hospitalization between the standard of care and standard of care plus remdesivir groups. Conclusion After a one-year follow-up of hospitalized patients (with a very high participation rate), approximately one in four reported substantial bother from fatigue, and one in six reported that they had not recovered well from COVID-19. We found no convincing evidence of a remdesivir effect, but confidence intervals were wide and included possible substantial benefit and substantial harm. Disclosures Hanna-Riikka Kreivi, MD, PhD, Pfizer: Advisor/Consultant|Roche: Advisor/Consultant Tuomas Rosberg, MD, PhD, AstraZeneca: Honoraria|Boehringer-Ingelheim: Honoraria|GSK: Honoraria.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".