Therapeutic Plasma Exchange in Patients With Acute‐On‐Chronic Liver Failure Improves Survival—An Updated Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Acute-on-chronic liver failure (ACLF) is a syndrome that develops after an acute insult and is associated with organ failures and high short-term mortality. Plasma exchange (PLEX) is an emerging modality for treating ACLF patients. We aimed to evaluate the efficacy of PLEX in treating ACLF. METHODS: We conducted a systematic review and meta-analysis of studies comparing PLEX versus standard medical therapy (SMT) to treat patients with ACLF across different definitions and etiologies. Pooled risk ratios were determined by the Mantel-Haenszel method within a random effect model. The primary outcome studied was survival at 30 days in PLEX group compared to SMT. RESULTS: Twenty-three studies (5336 ACLF patients with 2724 in PLEX arm, including 4 RCTs) were included. PLEX was associated with a significant reduction in mortality at 30 days (RR 0.70; 95% CI, 0.60-0.81; p < 0.001) and at 90 days (RR 0.81;0.77-0.86; p < 0.001). Six studies (1495 patients; 2 RCTs) with data for 1-year survival showed better outcomes in the PLEX group (RR 0.85; 0.79-0.92; p < 0.0001) compared to SMT. Among HBV-related ACLF and alcohol-related ACLF, there was a significant reduction in mortality among PLEX treated group at 90 days; RR 0.79 (0.74-0.85), p < 0.001 and RR 0.69 (0.52-0.92), p = 0.01 respectively. PLEX was associated with improved 3-month survival across definitions for ACLF. The most common adverse effects were skin rash and allergic reactions (14%). CONCLUSIONS: In this up-to-date meta-analysis, significant 1, 3-month and up to 1-year survival benefit was noted among patients with ACLF treated with PLEX compared to SMT.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".