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机构地区:[1]山东大学附属省立医院骨科,济南250022 [2]山东大学附属省立医院眼科,济南250022 [3]山东大学齐鲁医院胸外科
出 处:《中国实用护理杂志》2018年第4期279-283,共5页Chinese Journal of Practical Nursing
摘 要:目的通过对骨折手术患者并发术中压疮相关危险因素进行分析,建立个体化预测骨折手术患者并发术中压疮风险的列线图模型。方法纳入2012年6月至2017年6月符合入选标准的骨折手术患者3796例,收集临床资料,采用单因素分析筛选并发术中压疮相关因素,将有统计学意义变量纳入多因素Logistic回归模型,分析并发术中压疮危险因素;应用R软件建立预测骨折手术患者并发术中压疮风险的列线图模型,采用Bootstrap法进行模型验证,并应用ROC曲线探索列线图模型对骨折手术患者并发术中压疮的预测效率。结果3796例骨折手术患者并发术中压疮392例,发生率为10.33%(392/3796)。年龄(OR=3.56,95%CI1.84~5.21)、肥胖(OR=2.25,95%CI1.27~3.69)、营养不良(OR=3.91,95%CI1.90~5.87)、糖尿病(OR=2.73,95%CI1.50—4.32)及手术持续时间〉/2.5h(OR=5.14,95%CI2.81—7.25)均是骨折手术患者并发术中压疮的独立危险因素(P〈0.05)。列线图模型预测骨折手术患者并发术中压疮风险的一致性指数为0.749,具有良好的区分度与准确度;ROC曲线显示列线图模型预测骨折手术患者并发术中压疮的曲线下面积为0.727(95%C10.661—0.815),临床应用价值高。结论基于年龄、肥胖、营养不良、糖尿病及手术持续时间i〉2.5h这5项独立危险因素建立的预测骨折手术患者并发术中压疮风险的列线图模型,具有良好的区分度与准确度,临床应用价值高,对甄别高风险人群,制订干预对策具有指导意义。Objective To analyze the risk factors of pressure sores in patients underwent fracture surgery to establish a nomogram model to predict the risk of pressure sores. Methods From June 2012 to June 2017, 3 796 patients underwent fracture surgery were enrolled in this study. Their clinical data were analyzed and univariate analysis was used to determine the risk factors related to pressure sores. The significant variables were included in the multivariate Logistic regression analysis to analyze the risk factors of pressure sores. Then the R software was used to establish a predictive nomogram model. Bootstrap method was used to validate the nomogram model and ROC curve was used to explore the predictive efficacy of the model in predicting the pressure sores. Results A total of 392 cases out of 3 796 cases of fracture surgery had pressure sores. The prevalence rate was 10.33%(392/3 796). Age (OR= 3.56,95%CI 1.84-5.21), obesity (0R=2.25,95%CI 1.27-3.69), malnutrition (0R=3.91,95%CI 1.90-5.87), diabetes mellitus (0R=2.73,95%C11.50-4.32), and surgery time over 2.5 h (OR=5.1d,95%CI2.81-7.25) were all independent risk factors for pressure sores in fracture surgery (P 〈 0.05). The nomogram model showed a C-index of 0.749 with good discrimination and accuracy, while ROC curve showed that the area under the curve for the nomogram model predicting pressure sores was 0.727 (95% CI 0.661-0.815). Conclusions The nomogram built based on age, obesity, malnutrition, diabetes mellitus, and surgery time over 2.5 h has good discrimination and accuracy which could be helpful for screening the patients
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