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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".