Efficacy of therapeutic plasma exchange in patients with severe COVID‐19: A systematic review and meta‐analysis
Bibliographic record
Abstract
We conducted this systematic review and meta-analysis to evaluate the existing evidence and to quantitatively synthesise evidence on the impact of therapeutic plasma exchange (TPE) on severe COVID-19 patients. This systematic review and meta-analysis protocol was prospectively registered on PROSPERO (CRD42022316331). We systemically searched six electronic databases (PubMed, Scopus, Web of Science, ScienceDirect, clinicaltrial.gov, and Cochrane Central Register of Controlled Trials) from inception until 1 June 2022. We included studies comparing patients who received TPE versus those who received the standard treatment. For risk of bias assessment, we used the Cochrane risk of bias assessment tool, the ROBINS1 tool, and the Newcastle Ottawa scale for RCTs, non-RCTs, and observational studies, respectively. Continuous data were pooled as standardized mean difference (SMD), and dichotomous data were pooled as risk ratio in the random effect model with the corresponding 95% confidence intervals (CI). Thirteen studies (one randomized controlled trials (RCT) and 12 non-RCTs) were included in the meta-analysis, with a total of 829 patients. There is a moderate-quality evidence from one RCT that TPE reduces the lactic dehydrogenase (LDH) levels (SMD -1.09, 95% CI [-1.59 to -0.60]), D-dimer (SMD -0.86, 95% CI [-1.34 to -0.37]), and ferritin (SMD -0.70, 95% CI [-1.18 to -0.23]), and increases the absolute lymphocyte count (SMD 0.54, 95% CI [0.07-1.01]), There is low-quality evidence from mixed-design studies that TPE was associated with lower mortality (relative risk 0.51, 95% CI [0.35-0.74]), lower IL-6 (SMD -0.91, 95% CI [-1.19 to -0.63]), and lower ferritin (SMD -0.51, 95% CI [-0.80 to -0.22]) compared to the standard control. Among severely affected COVID-19 patients, TPE might provide benefits such as decreasing the mortality rate, LDH, D-dimer, IL-6, and ferritin, in addition to increasing the higher absolute lymphocyte count. Further well-designed RCTs are needed.
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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 | 0.011 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".