Development and internal validation of machine-learning models for postoperative deep vein thrombosis after hip arthroplasty for femoral neck fracture: evaluation of Traditional Chinese Medicine syndrome features

Background Postoperative lower-extremity deep vein thrombosis (DVT) remains a clinically important complication after hip arthroplasty for femoral neck fracture. We evaluated whether Traditional Chinese Medicine (TCM) syndrome features add predictive information beyond conventional preoperative variables and examined the association between the triglyceride-glucose (TyG) index and postoperative DVT. Methods This retrospective single-center study included 380 patients. Group-constrained partitioning and resampling prevented identical prespecified multivariable profiles from crossing evaluation boundaries, yielding a development set ( n = 267; 74 DVT events) and a profile-disjoint held-out validation set ( n = 113; 31 events). Nine machine-learning algorithms were evaluated using a fixed eight-variable panel comprising D-dimer, fibrinogen, platelet count, type 2 diabetes, fasting blood glucose, TyG, Qi stagnation with blood stasis (QSBS), and Qi deficiency with blood stasis (QDBS). Random forest (RF) was selected on the basis of development-set out-of-fold performance before held-out validation. Results Postoperative DVT occurred in 105 patients (27.6%). RF achieved a pooled group-aware development out-of-fold AUC of 0.774 (95% CI 0.710–0.837) and a held-out validation AUC of 0.859 (95% CI 0.782–0.935). Across 30 repeated group-constrained partitions, mean validation AUC was 0.792. In the single profile-disjoint held-out validation partition, the development-derived threshold of 0.298 yielded sensitivity of 0.903, specificity of 0.720, positive predictive value of 0.549, and negative predictive value of 0.952; in that same partition, the calibration slope was 1.136 (95% CI 0.753–1.867), the observed-to-expected ratio was 0.846, and the Brier score was 0.132. Adding QSBS and QDBS did not materially improve discrimination (AUC 0.855 vs. 0.859; ΔAUC = 0.003; DeLong p = 0.746). Higher TyG remained associated with DVT after multivariable adjustment (OR per 1 SD 1.550, 95% CI 1.186–2.026; p = 0.001); restricted cubic splines supported an overall association ( p = 0.007) without significant evidence of nonlinearity ( p = 0.082). Conclusion RF showed favorable discrimination in the held-out cohort but more modest performance across repeated group-constrained partitions. The two retained TCM syndrome variables did not provide a material increment in AUC, whereas higher TyG remained associated with postoperative DVT after multivariable adjustment. External validation and assessment of recalibration are required before clinical implementation.

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Journal
Frontiers in Artificial Intelligence
Published
2026-10-29
DOI
https://doi.org/10.3389/frai.2026.1798230
Primary Topic
Venous Thromboembolism Diagnosis and Management
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article
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article

Development and internal validation of machine-learning models for postoperative deep vein thrombosis after hip arthroplasty for femoral neck fracture: evaluation of Traditional Chinese Medicine syndrome features

励建安, Zhaoke Pi, Yuwei Shen, Yang Liu et al.
5 links
Frontiers in Artificial Intelligence
Venous Thromboembolism Diagnosis and Management
article

Development and internal validation of machine-learning models for postoperative deep vein thrombosis after hip arthroplasty for femoral neck fracture: evaluation of Traditional Chinese Medicine syndrome features

励建安, Zhaoke Pi, Yuwei Shen, Yang Liu, Yuqiang Qian, Peilian Cao, Peng Yuan, Daxing Jiang
article en

Abstract

Background Postoperative lower-extremity deep vein thrombosis (DVT) remains a clinically important complication after hip arthroplasty for femoral neck fracture. We evaluated whether Traditional Chinese Medicine (TCM) syndrome features add predictive information beyond conventional preoperative variables and examined the association between the triglyceride-glucose (TyG) index and postoperative DVT. Methods This retrospective single-center study included 380 patients. Group-constrained partitioning and resampling prevented identical prespecified multivariable profiles from crossing evaluation boundaries, yielding a development set ( n = 267; 74 DVT events) and a profile-disjoint held-out validation set ( n = 113; 31 events). Nine machine-learning algorithms were evaluated using a fixed eight-variable panel comprising D-dimer, fibrinogen, platelet count, type 2 diabetes, fasting blood glucose, TyG, Qi stagnation with blood stasis (QSBS), and Qi deficiency with blood stasis (QDBS). Random forest (RF) was selected on the basis of development-set out-of-fold performance before held-out validation. Results Postoperative DVT occurred in 105 patients (27.6%). RF achieved a pooled group-aware development out-of-fold AUC of 0.774 (95% CI 0.710–0.837) and a held-out validation AUC of 0.859 (95% CI 0.782–0.935). Across 30 repeated group-constrained partitions, mean validation AUC was 0.792. In the single profile-disjoint held-out validation partition, the development-derived threshold of 0.298 yielded sensitivity of 0.903, specificity of 0.720, positive predictive value of 0.549, and negative predictive value of 0.952; in that same partition, the calibration slope was 1.136 (95% CI 0.753–1.867), the observed-to-expected ratio was 0.846, and the Brier score was 0.132. Adding QSBS and QDBS did not materially improve discrimination (AUC 0.855 vs. 0.859; ΔAUC = 0.003; DeLong p = 0.746). Higher TyG remained associated with DVT after multivariable adjustment (OR per 1 SD 1.550, 95% CI 1.186–2.026; p = 0.001); restricted cubic splines supported an overall association ( p = 0.007) without significant evidence of nonlinearity ( p = 0.082). Conclusion RF showed favorable discrimination in the held-out cohort but more modest performance across repeated group-constrained partitions. The two retained TCM syndrome variables did not provide a material increment in AUC, whereas higher TyG remained associated with postoperative DVT after multivariable adjustment. External validation and assessment of recalibration are required before clinical implementation.

Frontiers in Artificial IntelligenceVol. 9
Shenzhen University (CN), West China Medical Center of Sichuan University (CN), West China Hospital of Sichuan University (CN), Nanjing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 10%
Venous Thromboembolism Diagnosis and Management
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