ArticleResearch and practice in thrombosis and haemostasis2025
Validation of the 5-SNP score for the prediction of venous thromboembolism in a Danish fast-track cohort of 6789 total hip and total knee arthroplasty patients.
Article in Research and practice in thrombosis and haemostasis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
Background: Venous thromboembolism (VTE) is a serious complication following total hip arthroplasty (THA) and total knee arthroplasty (TKA). Despite improvements with fast-track treatment protocols, 0.5% of patients still develop a VTE within 90-days postoperatively. Previously, the 5-single nucleotide polymorphism (SNP) genetic risk scores (weighted and simplified) were developed to identify people at a high risk for VTE within the general population. Objectives: We aimed to assess whether the 5-SNP scores could be used to identify high-risk patients in a cohort of fast-track THA/TKA patients. Methods: A subset of patients from the Lundbeck Centre for Fast-track Hip and Knee Replacement Database was included based on the availability of genetic information. The 5-SNP scores were calculated for these patients, and their discriminatory performance was determined by c-statistic. Furthermore, the 5-SNP scores were added to a simple logistic prediction model containing clinical predictors to assess the added predictive value. Results: A total of 7753 THA and TKA procedures (6798 patients) were included in this study. The c-statistics for the weighted and simple 5-SNP scores were 0.50 (95% CI, 0.39-0.61) and 0.48 (95% CI, 0.38-0.58), respectively. For the model with clinical predictors, the c-statistic was 0.67 (95% CI, 0.56-0.77). Addition of either of the 5-SNP scores did not improve discrimination in this model. Conclusion: These findings do not support genetic risk profiling in fast-track THA/TKA patients to predict VTE. Hence, efforts should be directed at optimizing prediction models with clinical predictors.
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