Convalescent plasma associates with reduced mortality and improved clinical trajectory in patients hospitalized with COVID-19
Bibliographic record
Abstract
BACKGROUNDEvidence supporting convalescent plasma (CP), one of the first investigational treatments for coronavirus disease 2019 (COVID-19), has been inconclusive, leading to conflicting recommendations. The primary objective was to perform a comparative effectiveness study of CP for all-cause, in-hospital mortality in patients with COVID-19.METHODSThe multicenter, electronic health records-based, retrospective study included 44,770 patients hospitalized with COVID-19 in one of 176 HCA Healthcare-affiliated community hospitals. Coarsened exact matching (1:k) was employed, resulting in a sample of 3774 CP and 10,687 comparison patients.RESULTSExamination of mortality using a shared frailty model, controlling for concomitant medications, date of admission, and days from admission to transfusion, demonstrated a significant association of CP with lower mortality risk relative to the comparison group (adjusted hazard ratio [aHR] = 0.71; 95% CI, 0.59-0.86; P < 0.001). Examination of patient risk trajectories, represented by 400 clinico-demographic features from our real-time risk model (RTRM), indicated that patients who received CP recovered more quickly. The stratification of days to transfusion revealed that CP within 3 days after admission, but not within 4 to 7 days, was associated with a significantly lower mortality risk (aHR = 0.53; 95% CI, 0.47-0.60; P < 0.001). CP serology level was inversely associated with mortality when controlling for its interaction with days to transfusion (HR = 0.998; 95% CI, 0.997-0.999; P = 0.013), yet it did not reach univariable significance.CONCLUSIONSThis large, diverse, multicenter cohort study demonstrated that CP, compared with matched controls, is significantly associated with reduced risk of in-hospital mortality. These observations highlight the utility of real-world evidence and suggest the need for further evaluation prior to abandoning CP as a viable therapy for COVID-19.FUNDINGThis research was supported in whole by HCA Healthcare and/or an HCA Healthcare-affiliated entity, including Sarah Cannon and Genospace.
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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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".