Cost-effectiveness analysis of statins for the treatment of hospitalized COVID-19 patients
Bibliographic record
Abstract
BACKGROUND: A recent systematic review and meta-analysis reporting on thirteen published cohorts investigating 110,078 patients demonstrated that patients who were administered statins after their COVID-19 diagnosis and hospitalization were had a lower risk of mortality. While these findings are encouraging, given competing COVID-19 treatment approaches, it is unclear if statin use should be prioritized and if its use is a cost-effective treatment options for hospitalized COVID-19 patients. In this study, we report on a cost-effectiveness analysis of statin-containing treatment regimens for hospitalized COVID-19 patients. METHODS: A Markov model was used to compare statin use and no statin use among hospitalized COVID-19 patients from a United States healthcare perspective. The cycle length was one week, with a time horizon of 4 weeks. A Monte Carlo microsimulation with 20,000 samples were used. All analyses were conducted using TreeAge Pro Healthcare Version 2021 R1.1. RESULTS: The mean cost for patients receiving statins in addition to usual care was $31,623 (SD $20,331), whereas the mean cost for patients not receiving statins was $33,218 (SD $25,440). The mean effectiveness for the two cohorts were 1.73 (SD 0.96) and 1.71 (SD 1.00), respectively. CONCLUSIONS: This analysis demonstrated that treatment of hospitalized COVID-19 patients with statins was both cheaper and more effective than treatment without statins; statin-containing therapy dominates over non-statin therapy. Statin medications for the treatment of COVID-19 should be further investigated in randomized controlled trials, especially considering its cost-effective nature. Optimistically and pending the results of future randomized trials, statins should be considered for use broadly for the treatment of hospitalized COVID-19 patients.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".