Geographic and diagnostic variations in thrombosis risk among patients with immune thrombocytopenia: A systematic review and meta-analysis.

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.