ICU经口气管插管患者口腔黏膜压力性损伤风险预测模型的系统评价

doi:10.3761/j.issn.2096-7446.2026.05.016

摘要/Abstract

摘要：

目的系统性评价ICU经口气管插管患者口腔黏膜压力性损伤风险的预测模型，为临床医护人员挑选或设计出适宜的评估工具提供依据。方法系统检索PubMed、Embase、Web of Science、Cochrane Library、CINAHL、中国知网、维普数据库、万方数据库和中国生物医学文献数据库自建库至2025年2月1日发表的关于构建ICU经口气管插管患者口腔黏膜压力性损伤风险预测模型的研究。由两名研究者独立进行文献筛选、数据提取，并运用预测模型偏倚风险评估工具评估纳入文献的偏倚风险。结果共纳入10项研究，涉及10个模型。气管插管留置时间、白蛋白、急性生理学和慢性健康状况Ⅱ（Acute Physiology and Chronic Health EvaluationⅡ，APACHE Ⅱ）评分、Richmond躁动-镇静评分量表（Richmond Agitation-Sedation Scale，RASS）评分和牙垫的使用是模型重复报告的前5名预测变量。8项研究报告了受试者操作特征曲线下面积为0.600~0.930。10项研究适用性良好但普遍存在高偏倚风险，尤以数据分析领域突出。结论 ICU经口气管插管患者口腔黏膜压力性损伤风险预测模型研究仍处于初期阶段。未来需开展高质量研究，结合人工智能优化模型开发与临床转化，重点加强模型内外部验证，并关注气管插管留置时间、APACHE Ⅱ评分及牙垫使用等核心预测因子。

关键词: 重症监护室, 气管插管, 口腔黏膜压力性损伤, 系统评价, 预测模型

Abstract:

Objective To systematically review prediction models for the risk of oral mucosa pressure injury in ICU patients with orotracheal intubation，aiming to assist clinical healthcare professionals in selecting or designing appropriate assessment tools. Methods We systematically searched PubMed，Embase，Web of Science，Cochrane Library，CINAHL，China National Knowledge Infrastructure，VIP Database，Wanfang Database，and China Biological Medicine Literature Database for studies published from database inception to February 1，2025，focusing on the development of risk prediction models for oral mucosa pressure injuries in ICU patients with orotracheal intubation. Two researchers independently performed literature screening and data extraction. The Prediction model Risk of Bias Assessment Tool（PROBAST） was applied to assess the risk of bias in the included studies. Results Ten studies involving 10 distinct prediction models were included. The top five most frequently reported predictor variables were duration of intubation，albumin level，acute physiology and chronic health evaluationⅡ（APACHEⅡ），Richmond Agitation-Sedation Scale score（RASS），and bite block usage. Eight studies reported area under the receiver operating characteristic curve（AUC） values ranging from 0.600 to 0.930. While the applicability of all ten studies was rated as good，they universally exhibited a high risk of bias，particularly in the domain of analysis. Conclusion Research on prediction models for oral mucosa pressure injury risk in ICU patients with orotracheal intubation remains in its early stages. Future studies should focus on conducting high-quality research，incorporating artificial intelligence to optimize model development and clinical translation. Emphasis should be placed on strengthening both internal and external validation of models，with particular attention to core predictors such as duration of intubation，APACHE Ⅱ score，and bite block usage.

Key words: Intensive Care Units, Endotracheal Intubation, Oral Mucosal Pressure Injuries, Systematic Review, Predictive Model

刘丽, 涂萍, 黄斯瑶, 熊燕, 徐建梅. ICU经口气管插管患者口腔黏膜压力性损伤风险预测模型的系统评价[J]. 中华急危重症护理杂志, 2026, 7(5): 608-615.

LIU Li, TU Ping, HUANG Siyao, XIONG Yan, XU Jianmei. Risk prediction models of oral mucosal pressure injury in patients with oral endotracheal intubation in ICU：a systematic review[J]. Chinese Journal of Emergency and Critical Care Nursing, 2026, 7(5): 608-615.

图/表 4

参考文献 29

[1]	周红芳, 兰旭红, 贾东珲, 等. ICU成人气管插管患者围拔管期管理的最佳证据总结[J]. 中华急危重症护理杂志, 2024, 5(1):33-39.
	Zhou HF, Lan XH, Jia DH, et al. Summary of the best evidences for the management of the peri-extubation period for ICU adult patients with tracheal intubation[J]. Chin J Emerg Crit Care Nurs, 2024, 5(1):33-39.
[2]	Zhao YY, Guo J, Ma J, et al. Characteristics of oral mucosal pre-ssure injuries in children with orotracheal intubation in intensive care units:an observational study[J]. Nurs Crit Care, 2025, 30(3):e13174. doi: 10.1111/nicc.v30.3
[3]	唐莉, 李敏, 姚倩. 经口气管插管病人口腔黏膜压力性损伤护理的研究进展[J]. 护理研究, 2023, 37(20):3682-3686.
	Tang L, Li M, Yao Q. Research progress in nursing care of oral mucosal pressure injury in patients undergoing oral endotracheal intubation[J]. Chin Nurs Res, 2023, 37(20):3682-3686.
[4]	Erbay Dallı Ö, Ceylan İ, Kelebek Girgin N. Incidence,chara-cteristics and risk factors of medical device-related pressure injuries:an observational cohort study[J]. Intensive Crit Care Nurs, 2022, 69:103180. doi: 10.1016/j.iccn.2021.103180
[5]	杨茂凡, 周会兰, 陈柯宇, 等. ICU经口气管插管患者口腔黏膜压力性损伤研究进展[J]. 护理学杂志, 2023, 38(2):21-24.
	Yang MF, Zhou HL, Chen KY, et al. A review of oral-mucosal pressure injury associated with oral tracheal intubation in ICU patients[J]. J Nurs Sci, 2023, 38(2):21-24.
[6]	Bai MN, Wang JX, Li XW, et al. Nomogram for predicting the severity of high-risk plaques in acute coronary syndrome[J]. Front Cardiovasc Med, 2025, 12:1618038. doi: 10.3389/fcvm.2025.1618038
[7]	邢路瑶, 余文静, 胡娟娟, 等. 患者术中获得性压力性损伤风险预测模型的研究进展[J]. 中华护理杂志, 2023, 58(24):3054-3059. doi: 10.3761/j.issn.0254-1769.2023.24.016
	Xing LY, Yu WJ, Hu JJ, et al. Research progress on risk prediction models of intraoperative acquired pressure injury in surgical patients[J]. Chin J Nurs, 2023, 58(24):3054-3059. doi: 10.3761/j.issn.0254-1769.2023.24.016
[8]	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
[9]	Jia LL, Deng YC, Xu Y, et al. Development and validation of a nomogram for oral mucosal membrane pressure injuries in ICU patients:a prospective cohort study[J]. J Clin Nurs, 2024, 33(10):4112-4123. doi: 10.1111/jocn.v33.10
[10]	辜甜田, 黄光梅, 张培, 等. 经口气管插管患者口腔黏膜压力性损伤风险预测模型的构建与验证[J]. 护理学杂志, 2024, 39(20):64-68.
	Gu TT, Huang GM, Zhang P, et al. Construction and validation of a predictive model for oral mucosal pressure injury in patients with oral endotracheal intubation[J]. J Nurs Sci, 2024, 39(20):64-68.
[11]	张树光, 姜洪彪, 侯杰, 等. ICU经口气管插管老年病人口腔黏膜压力性损伤风险预测模型的构建[J]. 护理研究, 2024, 38(20):3592-3597.
	Zhang SG, Jiang HB, Hou J, et al. Construction of risk pre-diction model of oral mucosal pressure injury in elderly patients with oral tube intubation in ICU[J]. Chin Nurs Res, 2024, 38(20):3592-3597.
[12]	Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical ap-praisal and data extraction for systematic reviews of predi-ction modelling studies:the CHARMS checklist[J]. PLoS Med, 2014, 11(10):e1001744. doi: 10.1371/journal.pmed.1001744
[13]	Moons KGM, Wolff RF, Riley RD, et al. PROBAST:a tool to assess risk of bias and applicability of prediction model stu-dies:explanation and elaboration[J]. Ann Intern Med, 2019, 170(1):W1-W33. doi: 10.7326/M18-1377
[14]	陈锐, 王志伟, 赵瑞玲, 等. ICU体外循环术后患者口腔黏膜压力性损伤列线图预测模型的构建[J]. 遵义医科大学学报, 2024, 47(3):262-269.
	Chen R, Wang ZW, Zhao RL, et al. Construction of nomogram prediction model for oral mucosal pressure injury in patients after extracorporeal circulation in ICU[J]. J Zunyi Med Univ, 2024, 47(3):262-269.
[15]	王志伟, 何小燕, 陶珍珍, 等. ICU气管插管患者口腔黏膜压力性损伤风险列线图模型的构建[J]. 中华现代护理杂志, 2024, 30(13):1764-1770.
	Wang ZW, He XY, Tao ZZ, et al. Construction of risk nomo-gram model of oral mucosal pressure injury in patients with tracheal intubation in ICU[J]. Chin J Mod Nurs, 2024, 30(13):1764-1770.
[16]	李敏, 唐莉, 姚倩, 等. 经口气管插管患者口腔黏膜压力性损伤风险预测模型的构建及验证[J]. 护理与康复, 2024, 23(6):1-8.
	Li M, Tang L, Yao Q, et al. Construction and verification of risk prediction model of oral mucosal membrane pressure injury in patients with oral tracheal intubation[J]. J Nurs Re-habil, 2024, 23(6):1-8.
[17]	喻海涛, 吴运莲. ICU气管插管患者口腔黏膜压力性损伤预测模型的构建及验证[J]. 中文科技期刊数据库(全文版)医药卫生, 2023(1):183-186.
	Yu HT, Wu YL. Construction and verification of prediction model of oral mucosal stress injury in ICU patients with tracheal intubation[J]. Chin Sci Tech J Data(Full-text Ed)-Med &Health, 2023(1):183-186.
[18]	周祥龙, 张国琴, 张帅, 等. ICU患者人工气道相关压力性损伤风险列线图预测模型的构建[J]. 护士进修杂志, 2023, 38(14):1254-1260.
	Zhou XL, Zhang GQ, Zhang S, et al. Construction of a nomo-gram prediction model for the risk of pressure-related injury associated with artificial airway in ICU patients[J]. J Nurses Train, 2023, 38(14):1254-1260.
[19]	周祥龙, 姚惠萍, 刘瑞红. ICU患者气管插管相关压力性损伤风险列线图预测模型的构建[J]. 当代护士(上旬刊), 2023, 30(12):82-89.
	Zhou XL, Yao HP, Liu RH. Construction of nomogram pre-diction model for risk of tracheal intubation-related stress injury in ICU patients[J]. Mod Nurse, 2023, 30(12):82-89.
[20]	华海涌, 王涛, 孙芳, 等. 新发展晚期血吸虫病成因前瞻性队列研究的设计与基线特征[J]. 中国病原生物学杂志, 2024, 19(3):308-311.
	Hua HY, Wang T, Sun F, et al. Prospective cohort study on causes of newly developed advanced schistosomiasis:cohort design and baseline characteristics[J]. J Pathog Biol, 2024, 19(3):308-311.
[21]	俞晓慧, 章新琼, 杨胜菊, 等. 糖尿病患者低血糖发生风险预测模型的系统评价[J]. 中华护理杂志, 2022, 57(15):1830-1839. doi: 10.3761/j.issn.0254-1769.2022.15.006
	Yu XH, Zhang XQ, Yang SJ, et al. The risk prediction models for the occurrence of hypoglycemia in patients with diabetes mellitus:a systematic review and critical appraisal[J]. Chin J Nurs, 2022, 57(15):1830-1839.
[22]	黄民水, 邓志航, 张健蔚. 基于LASSO-ASAPSO-LSTM的双曲拱坝缺失位移数据恢复[J]. 水电能源科学, 2024, 42(12):128-132.
	Huang MS, Deng ZH, Zhang JW. Recovery of missing displa-cement data for hyperbolic arch dams based on LASSO-ASAPSO-LSTM[J]. Water Resour Power, 2024, 42(12):128-132.
[23]	陈浩然, 刘夏阳, 王敏, 等. 机器学习模型在非比例风险生存资料中的应用及案例实践[J]. 中国循证医学杂志, 2024, 24(9):1108-1116.
	Chen HR, Liu XY, Wang M, et al. Application of machine learning models for survival data with non-proportional hazard and case study[J]. Chin J Evid Based Med, 2024, 24(9):1108-1116.
[24]	Collins GS, Reitsma JB, Altman DG, et al. Transparent report-ing of a multivariable prediction model for individual progno-sis or diagnosis(TRIPOD):the TRIPOD statement[J]. Eur Urol, 2015, 67(6):1142-1151. doi: S0302-2838(14)01199-3 pmid: 25572824
[25]	Sahoo M, Tripathy S, Mishra N, et al. Is there an optimal place for holding the tracheal tube during intubation? A proof-of-concept randomised clinical trial[J]. Emerg Med J, 2024, 41(2):89-95. doi: 10.1136/emermed-2023-213342 pmid: 38050113
[26]	Nan RL, Su YJ, Pei JH, et al. Characteristics and risk factors of nasal mucosal pressure injury in intensive care units[J]. J Clin Nurs, 2023, 32(1/2):346-356. doi: 10.1111/jocn.v32.1-2
[27]	陈倩倩, 彭纪芳, 刘晗. 胸腔镜肺段切除术后病人合并肺不张的危险因素及预测模型构建[J]. 护理研究, 2024, 38(21):3812-3817.
	Chen QQ, Peng JF, Liu H. Risk factors and construction of risk prediction model for patients with atelectasis after thoracoscopic segmental resection[J]. Chin Nurs Res, 2024, 38(21):3812-3817.
[28]	张家慧, 熊镁铃, 李佳佳, 等. 基于深度学习的老年人跌倒检测工具研究进展[J]. 护理研究, 2024, 38(20):3633-3637.
	Zhang JH, Xiong ML, Li JJ, et al. Research progress of fall detection tools for the elderly based on deep learning[J]. Chin Nurs Res, 2024, 38(20):3633-3637.
[29]	辜甜田, 陈俊希, 杨洋, 等. 机械通气患者亚谵妄综合征风险预测模型的构建及验证研究[J]. 中华急危重症护理杂志, 2023, 4(12):1068-1074.
	Gu TT, Chen JX, Yang Y, et al. Construction and validation of a nomogram risk prediction model for subsyndromal delirium in mechanically ventilated patients[J]. Chin J Emerg Crit Care Nurs, 2023, 4(12):1068-1074.