摘要
目的分析ICU重症患者发生压力性损伤的危险因素,建立压力性损伤发生风险的列线图模型。方法抽取2017年1-12月某三级甲等医院入住ICU且符合纳入标准的341例患者作为研究对象,依据患者住院期间压疮发生情况,采用自制调查表回顾性收集患者临床资料,运用Logistic回归分析压力性损伤的影响因素,建立预测压力性损伤发生的列线图模型,分别用H-L偏差度检验和ROC曲线评估预测模型的偏差度和区分度。结果本次研究的压力性损伤发生率为16.4%。多因素Logistic回归分析显示:脑卒中、糖尿病、乳酸、有创机械通气、性别为压力性损伤发生的危险因素(P<0.05);血红蛋白及血清白蛋白为压力性损伤发生的保护性因素(P<0.05)。依此建立的列线图模型具有较好的准确度(H-L检验:χ^2=4.561,P=0.683)和区分度(AUC=0.886,95%CI:0.749~0.970)。结论本次研究整合了压力性损伤的7项影响因素,依此建立的列线图预测模型具有较好的预测价值,依据个体在列线图中各个危险因素的不同水平指导医护人员采取有针对性的预防策略,从而有效预防ICU患者压力性损伤的发生。
Objective To analyze the risk factors of pressure injury(PI) in ICU patients and establish a nomogram model for predicting the risk of PI. Methods A total of 341 patients admitted to ICU from January 2017 to December 2017 who met the inclusion criteria were selected as research subjects. According to the occurrence of pressure sores in patients, the clinical data of patients during hospitalization were retrospectively collected by a self-designed questionnaire. Logistic regression analysis was used to determine independent risk factors of PI. Nomogram model for predicting the occurrence of PI was established. H-L deviation test and ROC curve were used to evaluate the deviation and discrimination of the prediction model respectively. Results The incidence of PI in this study was 16.4%. Multivariate logistic regression analysis showed that stroke, diabetes, lactic acid, invasive mechanical ventilation, gender, hemoglobin and serum albumin were independent influence factors for the occurrence of PI(all P <0.05). The established nomogram model had good accuracy(H-L test:χ^2=4.561, P=0.683) and discrimination(AUC=0.886, 95%CI:0.749~0.970). Conclusion This study integrates 7 independent influence factors of PI, and the corresponding nomogram prediction model has good predictive value, which is helpful for medical personnel to adopt targeted prevention strategies, thus to effectively prevent the occurrence of PI in ICU patients.
作者
姚秀英
耿丽
张理想
黄蕾
陈霞
安曼德
YAO Xiu-ying;GEN Li;ZHANG Li-xiang;HUANG Lei;CHEN Xia;Amanda(ICU, South District of the First Affiliated Hospital, University of Science and Technology of China, Hefei 230036, China;College of Computer Science and Technology, University of Science and Technology of China, Hefei 230036, China)
出处
《护理学报》
2019年第11期55-59,共5页
Journal of Nursing（China）
基金
中国科学技术大学新医学联合基金培育项目(WK2150110012)
关键词
重症患者
压力性损伤
预测模型
列线图
severe patient
pressure injury
prediction model
the column chart
