Volume 22 Issue 12
Dec.  2024
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SHEN Yunxia, YAO Guomei, FU Yueyuan, LU Shenghua, GAO Zhichao. Risk model construction and decision curve analysis of deep learning thrombosis in orthopedic surgery patients[J]. Chinese Journal of General Practice, 2024, 22(12): 2041-2045. doi: 10.16766/j.cnki.issn.1674-4152.003793
Citation: SHEN Yunxia, YAO Guomei, FU Yueyuan, LU Shenghua, GAO Zhichao. Risk model construction and decision curve analysis of deep learning thrombosis in orthopedic surgery patients[J]. Chinese Journal of General Practice, 2024, 22(12): 2041-2045. doi: 10.16766/j.cnki.issn.1674-4152.003793

Risk model construction and decision curve analysis of deep learning thrombosis in orthopedic surgery patients

doi: 10.16766/j.cnki.issn.1674-4152.003793
Funds:

 2019KY550

  • Received Date: 2024-03-05
    Available Online: 2025-01-20
  •   Objective  To establish a deep learning thrombosis risk model for patients undergoing orthopaedic surgery, and to analyze its clinical efficacy by using a decision curve.  Methods  A total of 180 orthopaedic patients admitted to Linping Campus in School of Medicine, the Second Affiliated Hospital of Zhejiang University from February 2022 to February 2024 were retrospectively selected and divided into training group (n=126) and verification group (n=54) according to the ratio of 7∶3. The patients were subdivided into two groups on the basis of the results of the deep vein thrombosis (DVT) examination. A predictive model for DVT risk in orthopaedic surgery patients was developed using Python software. The clinical efficacy of the model was evaluated through the construction of a decision curve.  Results  The training group, the age, BMI, operation time and bed rest time of the group with DVT were higher than those of the group without DVT. Furthermore, the proportions of diabetes, hypertension, spinal surgery history, lower limb injury and general anaesthesia were higher than those of the group without DVT (P < 0.05). The independent risk factors for DVT in orthopaedic surgery patients, as identified through statistical analysis, were age, BMI, diabetes, hypertension, disease distribution (lower limb injury) and bed rest time exceeding five days (P < 0.05). The AUC for the training set and verification set is 0.887 and 0.903, respectively. When the threshold probabilities of the training set and verification set are 18%-56% and 19%-58% respectively, the implementation of effective intervention measures can facilitate the optimal clinical benefits for patients undergoing orthopaedic surgery.  Conclusion  The following factors have been identified as independent risk factors for DVT in patients undergoing orthopaedic surgery: age, BMI, diabetes, hypertension, disease distribution (lower limb injury) and bed rest time exceeding five days. The ANN model constructed using these influencing factors is an effective method for predicting the risk of DVT in orthopedic surgery patients. This is beneficial in order to ensure that the clinical benefits of DVT prevention and treatment are optimized.

     

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