Research and Practice in Thrombosis and Haemostasis (Jan 2025)

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

  • Mark J.R. Smeets,
  • Pelle B. Petersen,
  • Christoffer C. Jørgensen,
  • Suzanne C. Cannegieter,
  • Sisse R. Ostrowski,
  • Henrik Kehlet,
  • Banne Nemeth

Journal volume & issue
Vol. 9, no. 1
p. 102644

Abstract

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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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