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护理学报 ›› 2022, Vol. 29 ›› Issue (19): 11-15.doi: 10.16460/j.issn1008-9969.2022.19.011

• 研究生园地 • 上一篇    下一篇

ICU患者头面部器械相关性压力性损伤风险预测模型的构建

祁进芳1, 董正惠2, 李阳1, 李振刚1, 王志伟1   

  1. 1.新疆医科大学 护理学院,新疆 乌鲁木齐 830016;
    2.新疆医科大学第六附属医院,新疆 乌鲁木齐 830002
  • 收稿日期:2022-05-22 发布日期:2022-11-08
  • 通讯作者: 董正惠（1970-）,女,湖北潜江人,硕士,主任护师,护理部主任,硕士研究生导师。E-mail:2397495105@qq.com
  • 作者简介:祁进芳（1996-）,女,新疆乌鲁木齐人,土族,本科学历,硕士研究生在读。
  • 基金资助:
    新疆维吾尔自治区自然科学基金资助项目（2021D01C455）

Construction of Risk Prediction Model of Craniofacial Medical Device-related Pressure Injury in ICU Patients

QI Jin-fang1, DONG Zhen-hui2, LI Yang1, LI Zhen-gang1, WANG Zhi-wei1   

  1. 1. School of Nursing, Xinjiang Medical University, Urimqi 830016,China;
    2. The Sixth Affiliated Hospital of Xinjiang Medical University, Urimqi 830002,China
  • Received:2022-05-22 Published:2022-11-08

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摘要： 目的 探讨ICU患者头面部器械相关性压力性损伤的危险因素,构建预测模型。方法 选取2021年6月-2022年2月收治的ICU患者210例,根据头面部是否发生器械相关性压力性损伤分为发生组（n=50）和未发生组（n=160）。使用二分类 Logistic 回归模型分析相关危险因素,构建预测模型并进行验证。结果 210例研究对象中有50例患者发生了器械相关性压力性损伤（23.8%）。多因素Logistic回归分析显示,水肿、俯卧位、急性生理慢性健康评分Ⅱ、血管活性药物使用时间是ICU患者头面部器械相关性压力性损伤的独立影响因素,基于上述影响因素构建预测头面部器械相关性压力性损伤风险的回归方程：Logit(P)=12.399+2.153×水肿+2.217×俯卧位+0.541×APACHEⅡ评分+0.342×血管活性药物使用时间。列线图模型AUC=0.945（95%CI：0.926~0.964）,提示模型区分度较好;校正曲线显示模型校准度较好;Hosmer-Lemeshow检验（χ2=3.063,P=0.930）显示模型拟合度较好。结论 本研究构建的ICU患者头面部器械相关性压力性损伤风险预测模型具有良好的风险识别能力,可以为早期筛查高风险患者提供依据。

关键词: 头面部, 器械相关性压力性损伤, 预测模型, 列线图

Abstract: Objective To explore the risk factors of craniofacial medical device-related pressure injury in ICU patients and to establish a predictive model. Methods Two hundred and ten ICU patients from June 2021 to February 2022 were divided into group A (n=50, with craniofacial medical device-related pressure injury) and B (n=160, without craniofacial medical device-related pressure injury). The binary logistic regression model was used to analyze the risk factors, and a prediction model was constructed and verified. Results Among the 210 subjects, medical device-related pressure injury was observed in 50 patients (23.8%). Multivariate Logistic regression analysis showed that edema, prone position, acute physiology and chronic health evaluationⅡ score and time of using vasoactive drugs were independent factors affecting the craniofacial medical device-related pressure injury of ICU patients. A regression equation was established to predict the risk of craniofacial medical device-related pressure injury:Logit(P)=12.399+2.153×edema+2.217×prone position+0.541×APACHEⅡscore+0.342×vasoactive drug use time. AUC of nomogram model was 0.945（95%CI：0.926~0.964）, and the model differentiation was good. The calibration curve showed that the calibration degree of the model was better, and the Hosmer-Lemeshow test（χ2=3.063, P=0.930）showed that the model fitted well. Conclusion The craniofacial medical device-related pressure injury risk prediction model of ICU patients constructed in this study has a good risk identification ability and can provide reference for early screening of high-risk patients.

Key words: craniofacial, medical device-related pressure injury, predictive model, nomogram

中图分类号: 

  • R47
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