Adding caplacizumab to standard of care in thrombotic thrombocytopenic purpura: a systematic review and meta-analysis
Bibliographic record
Abstract
Immune thrombotic thrombocytopenic purpura (iTTP) is an acquired, fatal microangiopathy if untreated. Randomized controlled trials (RCTs) demonstrated faster time to response with addition of caplacizumab to standard of care (SOC). However, concerns about RCT selection bias and the high cost of caplacizumab warrant examination of all evidence, including real-world observational studies. In this systematic review and meta-analysis, we searched for comparative studies evaluating SOC with or without caplacizumab for the treatment of iTTP. We assessed risk of bias using the Cochrane risk-of-bias-2 tool (RCTs) and the Newcastle-Ottawa Scale (observational studies). The primary efficacy and safety outcomes were all-cause mortality and treatment-emergent bleeding, respectively. Secondary outcomes included exacerbation and relapse, refractory iTTP, and time to response. We included 2 high-quality RCTs and 3 observational studies at high risk of bias comprising 632 total participants. Compared with SOC, caplacizumab was associated with a nonsignificant reduction in the relative risk [RR] of death in RCTs (RR, 0.21; 95% confidence interval [CI], 0.05-1.74) and observational studies (RR, 0.62; 95% CI, 0.07-4.41). Compared with SOC, caplacizumab was associated with an increased bleeding risk in RCTs (RR, 1.37; 95% CI, 1.06-1.77). In observational studies, bleeding risk was not significantly increased (RR, 7.10; 95% CI, 0.90-56.14). Addition of caplacizumab was associated with a significant reduction in refractory iTTP and exacerbation risks and shortened response time but increased relapse risk. Frontline addition of caplacizumab does not significantly reduce all-cause mortality compared with SOC alone, although it reduces refractory disease risk, shortens time to response, and improves exacerbation rates at the expense of increased relapse and bleeding risk.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".