ArticleRMD open2023
Validation of three prediction models for thrombosis recurrence in antiphospholipid syndrome patients based on a prospective cohort.
Article in RMD open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Machine learning for predicting thrombotic recurrence in antiphospholipid syndrome.Research and practice in thrombosis and haemostasis · 2025Article
- Artificial intelligence in antiphospholipid syndrome: toward individualized risk prediction.Research and practice in thrombosis and haemostasis · 2025Article
- Remnant cholesterol predicts risk of recurrent thrombosis beyond LDL-cholesterol in patients with antiphospholipid syndrome.BMC medicine · 2025Article
- Construction and validation of a predictive model for postoperative stent occlusion in patients undergoing iliac vein stenting based on an explainable machine learning model.Frontiers in surgery · 2025Article
- Risk factors for lower limb deep vein thrombosis in patients with intracerebral hemorrhage: a retrospective study using LASSO regression to develop a nomogram.Frontiers in neurologyArticle
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Authors and funding
10 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo validate the performance of the adjusted global antiphospholipid syndrome (APS) score (aGAPSS), Padua score and Caprini score to predict thrombosis recurrence in APS.
methodsConsecutive thrombotic-APS patients were included. aGAPSS, Padua and Caprini score at baseline were collected. Harrell c-index and calibration curve were used to validate the prediction models.
results362 patients were enrolled. The mean age was 36.30±13.88 years old, and 209 (57.7%) were female. Patients were followed up for a median of 2.32 years, with 32 (8.84%) venous and 21 (5.80%) arterial thrombosis. The 1-year, 3-year and 5-year thrombosis risks were 5.0%, 14.3% and 17.9%, respectively. The Harrell c-indexes of aGAPSS, Padua and Caprini score were 0.54 (95% CI 0.44 to 0.64), 0.54 (95% CI 0.46 to 0.62), and 0.50 (95%CI 0.42 to 0.58), respectively. Padua score had the best discrimination to predict venous thrombosis (Harrell c-index=0.61, 95% CI 0.53 to 0.69). aGAPSS had the best discrimination to predict arterial thrombosis (Harrell c-index=0.61, 95% CI 0.47 to 0.75). The calibrations for predicting thrombosis within 1, 3 and 5 years of the three models were suboptimal.
conclusionThe performance of aGAPSS, Padua and Caprini score to predict thrombosis recurrence in APS were suboptimal. Arterial and venous thrombosis recurrence predictors were different. New prediction models are required for venous and arterial thrombosis separately.
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