骨科手术患者深度学习血栓形成风险模型构建与决策曲线分析  

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

作　　者：沈云霞 姚国美 傅悦渊 陆升华 高志朝 SHEN Yunxia;YAO Guomei;FU Yueyuan;LU Shenghua;GAO Zhichao(Department of Operating Room,Linping District,the Second Affiliated Hospital of Zhejiang University School of Medicine,Hangzhou,Zhejiang 311100,China;不详)

机构地区：[1]浙江大学医学院附属第二医院临平院区手术室,浙江杭州311100 [2]浙江大学医学院附属第二医院临平院区骨科

出　　处：《中华全科医学》2024年第12期2041-2045,共5页Chinese Journal of General Practice

基　　金：浙江省医药卫生科技计划项目(2019KY550)。

摘　　要：目的构建骨科手术患者深度学习血栓形成风险模型,并选用决策曲线分析其临床效能。方法回顾性选取2022年2月—2024年2月浙江大学医学院附属第二医院临平院区收治的180例骨科手术患者,根据7∶3比例将其划分为训练集(126例)和验证集(54例);根据训练集内患者深静脉血栓(DVT)形成与否进一步分为DVT发生组(32例)与DVT未发生组(94例)。通过Python软件构建骨科手术患者DVT风险预测人工神经网络(ANN)模型,绘制决策曲线分析模型的临床效能。结果训练集内DVT发生组年龄、BMI、手术时间及卧床时间均高于DVT未发生组,糖尿病、高血压、脊柱手术史、病情分布(下肢损伤)、全麻占比均高于DVT未发生组(P<0.05)。年龄大、BMI高、合并糖尿病、高血压、病情分布(下肢损伤)和卧床时间>5 d均为骨科手术患者发生DVT的独立危险因素(P<0.05)。训练集、验证集的AUC分别为0.887、0.903。训练集与验证集分别取阈值概率18%~56%、19%~58%时,对骨科手术患者采取有效干预措施可使临床效益最大化。结论年龄大、BMI高、合并糖尿病、高血压、病情分布(下肢损伤)和卧床时间>5 d是骨科手术患者发生DVT的独立危险因素,由这些影响因素构建的ANN模型对骨科手术患者DVT风险预测效能显著,有助于DVT防治工作的临床效益最大化。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 pa

关 键 词：骨科手术 深度学习 人工神经网络 深静脉血栓形成 决策曲线 模型 

分 类 号：R687.3[医药卫生—骨科学]