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

doi:10.16460/j.issn1008-9969.2022.19.011

摘要/Abstract

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:

祁进芳, 董正惠, 李阳, 李振刚, 王志伟. 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[J]. 护理学报, 2022, 29(19): 11-15.

QI Jin-fang, DONG Zhen-hui, LI Yang, LI Zhen-gang, WANG Zhi-wei. Construction of Risk Prediction Model of Craniofacial Medical Device-related Pressure Injury in ICU Patients[J]. Journal of Nursing, 2022, 29(19): 11-15.

参考文献

[1] 张亚斌. 甘肃省ICU护士预防医疗器械相关性压力性损伤知信行现状调查及影响因素分析[D].兰州:兰州大学,2021.DOI:10.27204/d.cnki.glzhu.2021.000851.
[2] 贾盈盈,张红燕,马媛媛,等. ICU患者压力性损伤风险预测模型的系统评价[J].中华护理杂志,2021,56(8):1242-1248. DOI:10.3761/j.issn.0254-1769.2021.08.020.
[3] 付佳,田甜. 糖尿病患者术中皮肤压力性损伤风险列线图预测模型的构建[J].中国医科大学学报,2021,50(11):1014-1019,1025. DOI:10.12007/j.issn.0258-4646.2021.11.012.
[4] Coyer F, Cook JL, Doubrovsky A, et al.Exploring Medical Device-related Pressure Injuries in a Single Intensive Care Setting: A Longitudinal Point Prevalence Study[J]. Intensive Crit Care Nurs, 2022, 68:103155. DOI:10.1016/j.iccn.2021.103155.
[5] 顾梦倩,赵燕燕,陈圣枝,等. 2019年版国际《压力性损伤的预防与治疗:临床实践指南》解读[J]. 河北医科大学学报,2021,42(5):497-500.DOI:10.3969/j.issn.1007-3205.2021.05.001.
[6] Dang W, Liu Y, Zhou Q, et al.Risk Factors of Medical Device-related Pressure Injury in Intensive Care Units[J]. J Clin Nurs, 2022,31(9-10):1174-1183. DOI:10.1111/jocn.15974.
[7] Lustig A, Margi R, Orlov A, et al.The Mechanobiology Theory of the Development of Medical Device-related Pressure Ulcers Revealed Through a Cell-scale Computational Modeling Framework[J].Biomech Model Mechanobiol,2021, 20(3):851-860.DOI:10.1007/s10237-021-01432-w.
[8] 王娜, 熊尹诗, 张颖,等. 医疗器械相关压力性损伤的危险因素及集束化管理的研究进展[J]. 职业与健康, 2022, 38(1):4.DOI:10.13329/j.cnki.zyyjk.2022.0024.
[9] 赵琦,徐雲,蒋红,等. 医疗器械相关压力性损伤预防和管理的最佳证据总结[J]. 护理学杂志,2019,34(13):8-11. DOI:10.3870/j.issn.1001-4152.2019.13.008.
[10] Shearer SC, Parsa KM, Newark A, et al.Facial Pressure Injuries from Prone Positioning in the COVID-19 Era[J]. Laryngoscope,2021,131(7):E2139-E2142.DOI:10.1002/lary.29374.
[11] 郭艳侠,周金莉,侯赛宁,等. 我国医疗机构成人手术患者手术获得性压力性损伤流行特征的Meta分析[J]. 解放军护理杂志, 2021, 38(6):49-53.DOI:10.3969/j.issn.1008-9993.2021.06.013.
[12] 杨小辉,赵媛媛,钮美娥. ICU医疗器械相关压力性损伤的研究现状[J].护理学报,2017,24(13):49-53. DOI:10.16460/j.issn1008-9969.2017.13.049.
[13] 张倩倩,郭爱敏,李尊柱. 俯卧位通气相关面部压力性损伤影响因素及预防策略[J].护理实践与研究,2022,19(11):1662-1666. DOI:10.3969/j.issn.1672-9676.2022.11.018.
[14] 白一蕾. APACHEⅡ对重症脑血管病患者死亡率的预测价值[D].河南:新乡医学院,2020.DOI:10.27434/d.cnki.gxxyc.2020.000294.
[15] 韩妹,符杨,张春雨. 老年重症患者压力性损伤风险模型构建[J]. 中国老年学杂志,2022,42(13):3234-3237. DOI:10.3969/j.issn.1005-9202.2022.13.035.
[16] Choi BK, Kim MS, Kim SH.Risk Prediction Models for the Development of Oral-mucosal Pressure Injuries in Intubated Patients in Intensive Care Units: A Prospective Observational Study[J]. J Tissue Viability,2020,29(4):252-257.DOI: 10.1016/j.jtv.2020.06.002.

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