Geographic and diagnostic variations in thrombosis risk among patients with immune thrombocytopenia: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: While immune thrombocytopenia (ITP) is primarily characterized by bleeding manifestations, emerging evidence suggests a paradoxical predisposition to thrombotic events. This study aims to systematically evaluate the incidence of thrombosis in patients with ITP and identify associated risk factors, thereby providing evidence-based guidance for clinical practice. METHODS: PubMed, EMBASE, Cochrane Library, Web of Science, and CNKI were searched for literature on thrombosis in ITP patients from the inception of each database to April 1, 2025. Two independent researchers conducted study selection, data extraction, and quality assessment using the Newcastle-Ottawa Scale (NOS). Statistical analyses were performed using R software. RESULTS: From 20 included studies involving 100,446 ITP patients, we identified 9010 thrombotic events. The pooled incidence of thrombosis was 6.03 % (95 % CI: 4.39-8.24), increasing to 10.43 % (95 % CI: 7.17-14.93) after trim-and-fill adjustment for publication bias. Significant regional variation was observed (North America: 7.13 %; Asia: 4.50 %; Europe: 6.92 %). Incidence also varied by diagnostic criteria, ranging from 2.08 % (2020 CMACSH) to 8.18 % (2011 ASH). No significant differences were found based on gender or type of thrombosis. Key independent risk factors included advanced age (HR = 7.53), lupus anticoagulant positivity (HR = 9.9), elevated IgG-aCL (HR = 7.5), hypertension (HR = 4.12), multiple prior therapies (HR = 3.19), secondary ITP (HR = 1.29), and the use of thrombopoietin receptor agonists (HR = 3.15). CONCLUSION: Patients with ITP have a significantly increased risk of thrombosis, highlighting the need for targeted screening and preventive strategies in high-risk populations. Future research should focus on high-quality, multicenter prospective cohort studies and the development of more accurate thrombotic risk prediction models to guide clinical decision-making.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".