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Phenotypically driven subgroups of primary antiphospholipid syndrome-associated thrombocytopenia display distinct outcomes: a prospective cohort study with cluster analysis

lupusscimed · 2025-11-13 · canonical JSON source

2 visible annotations · policy: published · automated confidence ≥ 75.00%

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Objectives To identify antiphospholipid antibody-associated thrombocytopenia (aPLs-TP) phenotypes and assess their clinical outcomes.Methods This single-centre, prospective cohort study (January 2012 to April 2024) consecutively enrolled patients with aPLs-TP from Peking Union Medical College Hospital. Inclusion required persistent aPL positivity (≥12 weeks apart) and platelet (PLT) count <100×10 9/L twice, excluding secondary causes. Demographic aPL profiles and clinical outcomes (thrombosis, pregnancy morbidity, microangiopathy and valve disease) were analysed. Hierarchical clustering and Kaplan-Meier survival analysis were performed.Results A total of 123 patients (65.9% female, mean age 36.0 years) were consecutively enrolled in the study. Median PLT count was 50.0×10 9/L. Three clusters were identified: cluster 1 (n=35, all male, median PLT 67.0×109/L) consisted of men with smoking history, hyperhomocysteinaemia and diabetes, demonstrating the highest rate of atherothrombotic and valvular events; cluster 2 (n=51, all female, median PLT 60.0×109/L) included females with recurrent pregnancy morbidity and mild anaemia; and cluster 3 (n=37, 81.1% female, median PLT 27.0×109/L) comprised patients with isolated severe thrombocytopenia with the lowest rate of complete remission. Analysis of event-free survival for key clinical outcomes differed significantly among clusters at 5 years (p=0.026): cluster 1 at 66.9% (95% CI 52.50 to 85.24), cluster 2 at 45.85% (95% CI 32.41 to 64.86) and cluster 3 at 88.68% (95% CI 78.80 to 99.80).Conclusions Significant heterogeneity exists in patients with aPLs-TP, thus making PLT count alone an inadequate predictor of clinical phenotypes and prognosis. Subgroup analysis leveraging distinct clinical features is essential to develop individualised treatment strategies and improve outcomes.