Early experimental COVID-19 therapies: associations with length of hospital stay, mortality and related costs
Bibliographic record
Abstract
AIMS OF THE STUDY: Hydroxychloroquine and lopinavir/ritonavir have been used as experimental therapies to treat COVID-19 during the first wave of the pandemic. Randomised controlled trials have recently shown that there are no meaningful benefits of these two therapies in hospitalised patients. Uncertainty remains regarding the potential harmful impact of these therapies as very early treatments and their burden to the health care system. The present study investigated the length of hospital stay (LOS), mortality, and costs of hydroxychloroquine, lopinavir/ritonavir or their combination in comparison with standard of care among patients hospitalised for coronavirus disease 2019 (COVID-19). METHODS: This retrospective observational cohort study took place in the Geneva University Hospitals, Geneva, Switzerland (n = 840) between 26 February and 31 May 2020. Demographics, treatment regimens, comorbidities, the modified National Early Warning Score (mNEWS) on admission, and contraindications to COVID-19 treatment options were assessed. Outcomes included LOS, in-hospital mortality, and drug and LOS costs. RESULTS: After successful propensity score matching, patients treated with (1) hydroxychloroquine, (2) lopinavir/ritonavir or (3) their combination had on average 3.75 additional hospitalisation days (95% confidence interval [CI] 1.37–6.12, p = 0.002), 1.23 additional hospitalisation days (95% CI −1.24 – 3.51, p = 0.319), and 4.19 additional hospitalisation days (95% CI 1.52–5.31, p <0.001), respectively, compared with patients treated with the standard of care. Neither experimental therapy was significantly associated with mortality. These additional hospital days amounted to 1010.77 additional days for hydroxychloroquine and hydroxychloroquine combined with lopinavir/ritonavir, resulting in an additional cost of US$ 2,492,214 (95%CI US$ 916,839–3,450,619). CONCLUSIONS: Prescribing experimental therapies for COVID-19 was not associated with a reduced LOS and might have increased the pressure put on healthcare systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".