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四肢创伤开放性并粉碎性骨折患者发生谵妄的列线图预测模型构建

Construction of a nomogram prediction model for delirium in patients with open and comminuted fractures of the limbs after trauma
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摘要 目的分析四肢创伤开放性并粉碎性骨折患者发生谵妄的危险因素,构建并验证风险预测模型。方法回顾性分析2021年3月至2023年3月西安高新医院收治的108例四肢创伤开放性并粉碎性骨折患者的病历资料,其中男76例、女32例,年龄(54.17±14.62)岁。依据80/20定律随机分为训练集(86例)和验证集(22例)。统计患者术后1个月内谵妄的发病情况,根据患者术后是否发生谵妄分为谵妄组与非谵妄组,筛查四肢创伤开放性并粉碎性骨折患者术后发生谵妄的危险因素,构建并验证风险预测模型。采用t检验、χ^(2)检验、logistic回归模型、受试者操作特征曲线(ROC)、Hosmer-Lemeshow检验。结果86例四肢创伤开放性并粉碎性骨折患者中18例患者术后发生谵妄,发生率为20.93%(18/86)。谵妄组患者的年龄、脑卒中史占比、术中低氧血症占比、血清可溶性肿瘤坏死因子1(sTNFR1)水平均高于非谵妄组(均P<0.05)。年龄(OR=3.187,95%CI 1.176~8.633)、脑卒中史(OR=3.684,95%CI 1.359~9.979)、术中低氧血症(OR=4.958,95%CI 1.830~13.431)、血清sTNFR1(OR=4.591,95%CI 1.695~12.435)均是四肢创伤开放性并粉碎性骨折患者术后发生谵妄的危险因素(均P<0.05)。风险预测模型预测训练集86例四肢创伤开放性并粉碎性骨折患者术后发生谵妄的灵敏度为0.825(95%CI 0.741~0.922),特异度为0.829(95%CI 0.753~0.906),曲线下面积为0.892(95%CI 0.808~0.915)。风险预测模型预测验证集22例四肢创伤开放性并粉碎性骨折患者术后发生谵妄的灵敏度为0.799(95%CI 0.706~0.881),特异度为0.732(95%CI 0.658~0.827),曲线下面积为0.839(95%CI 0.791~0.915)。结论年龄、脑卒中史、术中低氧血症、血清sTNFR1均与四肢创伤开放性并粉碎性骨折患者术后发生谵妄有关,构建风险预测模型有助于早期预测术后谵妄的发生风险。 Objective To analyze the risk factors for delirium in patients with open and comminuted fractures of the limbs after trauma,and to construct and validate a risk prediction model.Methods A retrospective analysis was conducted on the medical records of 108 patients with open and comminuted fractures of the limbs after trauma admitted to Xi'an Gaoxin Hospital from March 2021 to March 2023.Among them,there were 76 males and 32 females,aged(54.17±14.62)years.According to the 80/20 law,they were randomly divided into a training set(86 cases)and a verification set(22 cases).The occurrence of delirium within one month after surgery was recorded,and the patients were divided into a delirium group and a non-delirium group based on whether delirium occurred postoperatively.The risk factors for delirium in patients with open and comminuted fractures of the limbs after trauma were screened,and a risk prediction model was constructed and validated.t test,χ^(2) test,logistic regression model,receiver operating characteristic curve(ROC),and Hosmer-Lemeshow test were used.Results Among the 86 patients with open and comminuted fractures of the limbs after trauma,18 patients developed delirium after surgery,with an incidence of 20.93%(18/86).The age,proportion of stroke history,proportion of intraoperative hypoxemia,and serum soluble tumor necrosis factor 1(sTNFR1)level in the delirium group were higher than those in the non-delirium group(all P<0.05).Age(OR=3.187,95%CI 1.176-8.633),stroke history(OR=3.684,95%CI 1.359-9.979),intraoperative hypoxemia(OR=4.958,95%CI 1.830-13.431),and serum sTNFR1(OR=4.591,95%CI 1.695-12.435)were identified as risk factors for postoperative delirium in patients with open and comminuted fractures of the limbs after trauma(all P<0.05).The risk prediction model showed a sensitivity of 0.825(95%CI 0.741-0.922),specificity of 0.829(95%CI 0.753-0.906),and area under the curve of 0.892(95%CI 0.808-0.915)for predicting postoperative delirium in 86 patients with open and comminuted fractures of the limbs after trauma of the training set.The risk prediction model showed a sensitivity of 0.799(95%CI 0.706-0.881),specificity of 0.732(95%CI 0.658-0.827),and area under the curve of 0.839(95%CI 0.791-0.915)for predicting postoperative delirium in 22 patients with open and comminuted fractures of the limbs after trauma of the verification set.Conclusions Age,history of stroke,intraoperative hypoxemia,and serum sTNFR1 are associated with postoperative delirium in patients with open and comminuted fractures of the limbs after trauma.Constructing a risk prediction model helps to predict the risk of postoperative delirium early.
作者 鱼红进 杨文斌 闫玉 刘兴国 Yu Hongjin;Yang Wenbin;Yan Yu;Liu Xingguo(Department of Orthopedics,Xi'an Gaoxin Hospital,Xi'an 710075,China)
出处 《国际医药卫生导报》 2024年第10期1632-1637,共6页 International Medicine and Health Guidance News
基金 陕西省自然科学基础研究计划(2020JM-689)。
关键词 四肢 开放性骨折 粉碎性骨折 谵妄 危险因素 风险预测模型 Limbs Open fracture Comminuted fracture Delirium Risk factors Risk prediction model
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