您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(医学版)》

山东大学学报 (医学版) ›› 2023, Vol. 61 ›› Issue (12): 86-93.doi: 10.6040/j.issn.1671-7554.0.2023.0796

• 临床医学 • 上一篇    

ICU机械通气患者撤机风险预测模型的构建

王建华,孙淑青,张效东,杨筱筱,王友健,卢金宝,李赞武   

  1. 潍坊市人民医院重症医学科, 山东 潍坊 261044
  • 发布日期:2024-01-11
  • 通讯作者: 孙淑青. E-mail:Shuqingsun@163.com
  • 基金资助:
    潍坊市卫生健康委员会项目(WFWSJK-2023-295)

Construction of a risk prediction model for ventilator weaning in mechanically ventilated patients in ICU

WANG Jianhua, SUN Shuqing, ZHANG Xiaodong, YANG Xiaoxiao, WANG Youjian, LU Jinbao, LI Zanwu   

  1. Intensive Care Unit, Weifang Peoples Hospital, Weifang 261044, Shandong, China
  • Published:2024-01-11

摘要: 目的 探讨重症医学科(ICU)机械通气患者撤机失败的危险因素,建立风险预测模型,并进行内部验证检验预测效果。 方法 采用便利抽样方法,收集2020年1月至2022年1月潍坊市人民医院ICU机械通气患者546例,其中2020年1月至2021年1月就诊患者为建模组(n=358),2021年2月至2022年1月就诊患者为验模组(n=188),分析撤机失败的危险因素并建立预测模型,采用Hosmer-Lemeshow检验判断模型的拟合优度,接受者操作特性曲线(ROC)检测模型的预测效能,利用ROC曲线下面积(AUC)进行模型评价。 结果 ICU机械通气患者撤机风险预测模型纳入机械通气时间(OR=0.993)、膈肌移动度(OR=3.886)、膈肌厚度变异率(OR=65.917)、浅快呼吸指数(RSBI,OR=0.960)、下腔静脉变异度(OR=1.176)5个预测因子模型公式:Z=-0.007×机械通气时间+1.357×膈肌移动度+4.188×膈肌厚度变异率-0.041×RSBI+0.162×下腔静脉变异度-3.183。ROC下面积为0.926,特异度为0.961,灵敏度为0.887。 结论 本研究构建的ICU机械通气患者撤机风险预测模型预测效果较好,为今后相关干预措施的制定与实施提供参考依据。

关键词: 重症医学科, 机械通气, 撤机, 预测模型

Abstract: Objective To investigate the risk factors of weaning failure in mechanically ventilated patients in the intensive care unit(ICU), develop a risk prediction model, and perform internal validation to assess the predictive performance. Methods A total of 546 cases of mechanically ventilated patients were collected during Jan. 2020 and Jan. 2022 with convenience sampling method from the ICU of Weifang Peoples Hospital, including 358 collected from Jan. 2020 and Jan. 2021 in the modeling group, and 188 collected from Feb. 2021 to Jan. 2022 in the model testing group. The risk factors of weaning failure were analyzed, based on which a prediction model was developed. The goodness of fit was assessed with Hosmer-Lemeshow test. The prediction performance was evaluated with receiver operating characteristic(ROC)curve, and the area under the ROC curve(AUC). Results The risk factors included mechanical ventilation time(OR=0.993), diaphragm mobility(OR=3.886), diaphragm thickness variability(OR=65.917), shallow fast respiratory index(RSBI, OR=0.960), and inferior vena cava variability(OR=1.176). The model equation was as follows: Z=-0.007×mechanical ventilation time+1.357×diaphragm mobility +4.188×diaphragm thickness variability -0.041×shallow fast respiratory index+ 0.162×inferior vena cava variability -3.183. The AUC was 0.926, with a specificity of 0.961 and a sensitivity of 0.887. Conclusion The risk prediction model demonstrates good performance, which can provide reference for the development and implementation of future interventions.

Key words: Intensive care unit, Mechanical ventilation, Weaning from mechanical ventilation, Prediction model

中图分类号: 

  • R605.97
[1] 孙鹏, 倪越男, 梁宗安, 等. 镇静镇痛对拔管后慢性阻塞性肺疾病急性加重患者使用无创正压通气的影响[J]. 中国呼吸与危重监护杂志, 2022, 21(6): 386-390. SUN Peng, NI Yuenan, LIANG Zongan, et al. Impact of sedation and/or analgesia during noninvasive positive pressure ventilation in patients with AECOPD after extubation [J]. Chinese Journal of Respiratory and Critical Care, 2022, 21(6): 386-390.
[2] Macintyre NR. Evidence-based assessments in the ventilator discontinuation process [J]. Respir Care, 2012, 57(10): 1611-1618.
[3] 伍春燕, 黄宏, 朱辉严, 等. 超声膈肌功能评估在指导COPD机械通气患撤机中的临床价值研究[J]. 临床和实验医学杂志, 2016, 15(12): 1203-1206. WU Chunyan, HUANG Hong, ZHU Huiyan, et al. Study on clinical significance of ultrasonic diaphragm function evaluation in patients with COPD under mechanical ventilation [J]. Journal of Clinical and Experimental Medicine, 2016, 15(12): 1203-1206.
[4] 吴红梅, 宋彩萍, 罗春梅, 等. 基于风险评分标准的气道分级管理在重型/危重型新型冠状病毒肺炎患者中的应用[J]. 重庆医学, 2020, 49(24): 4158-4162. WU Hongmei, SONG Caiping, LUO Chunmei, et al. Application of airway grading management based on risk score standard in patients with severe/critical COVID-19 [J]. Chongqing Medicine, 2020, 49(24): 4158-4162.
[5] 王慧, 马明, 陈德生, 等. 左室舒张功能不全对机械通气撤机结果的预测价值[J]. 中华危重病急救医学, 2017, 29(5): 413-418. WANG Hui, MA Ming, CHEN Desheng, et al. Predictive value of left ventricular diastolic dysfunction on mechanical ventilation [J]. Chinese Critical Care Medicine, 2017, 29(5): 413-418.
[6] 刘亚梅, 田龙, 王晨宇. 新冠肺炎疫情背景下一种新型重症监护室转入率预测模型的研究[J/OL]. 安徽医学, 2023, 44(11): 1384-1387.
[7] 李萍, 张野, 戴桂英, 等. 不同评分对监护病房发生医院感染的评估价值[J]. 中国现代医生, 2023, 61(30): 76-80. LI Ping, ZHANG Ye, DAI Guiying, et al. Evaluation value of different scores on hospital infection in intensive care unit [J]. China Modern Doctor, 2023, 61(30): 76-80.
[8] 葛慧青, 孙兵, 王波, 等. 重症患者气道廓清技术专家共识[J]. 中华重症医学电子杂志(网络版), 2020, 6(3): 272-282. GE Huiqing, SUN Bing, WANG Bo, et al. Expert consensus of airway clearance in critically ill patients [J]. Chinese Journal of Critical Care & Intensive Care Medicine(Electronic Edition), 2017, 29(5): 413-418.
[9] Monastesse A, Girard F, Massicotte N, et al. Lung ultrasonography for the assessment of perioperative atelectasis: a pilot feasibility study [J]. Anesthesia & Analgesia, 2017, 124(2): 1.
[10] 谢晨星.下腔静脉变异度及膈肌增厚分数对机械通气患者撤机结局的预测价值[D]. 唐山: 华北理工大学, 2021.
[11] 林一娟, 杨姝婷. 浅快呼吸指数、膈肌移动度和增厚率对重症肺炎机械通气撤机的指导价值[J]. 浙江实用医学, 2021, 26(2): 91-94. LIN Yijuan, YANG Shuting. The guiding value of shallow rapid breathing index、diaphragm displacement and diaphragm thickening fraction to weaning from mechanical ventilation [J]. Zhejiang Practical Medicine, 2021, 26(2): 91-94.
[12] 罗彩琴, 王东磊, 何小花, 等. 床旁超声监测膈肌功能对COPD机械通气患者撤机结果的预测价值[J].上海医药, 2023, 44(3): 40-43. LUO Caiqin, WANG Donglei, HE Xiaohua, et al. Value of bedside ultrasound monitoring of diaphragmatic function for the predictive outcome of withdrawal of mechanical ventilation in patients with COPD [J]. Shanghai Medical & Pharmaceutical Journal, 2023, 44(3): 40-43.
[13] 赵浩天, 王华伟, 龙玲, 等. 心脏、肺和膈肌超声联合预测重症机械通气患者撤机的价值[J]. 临床超声医学杂志, 2022, 24(8): 608-612. ZHAO Haotian, WANG Huawei, LONG Ling, et al. Value of cardiac,lung combined with diaphragmatic ultrasound in the prediction of weaning patients with severe mechanical ventilation [J]. Journal of Clinical Ultrasound in Medicine, 2022, 24(8): 608-612.
[14] 胡健. 超声评估膈肌功能在神经重症患者撤机中的应用[D]. 长春: 吉林大学, 2021.
[15] Demoule A, Jung B, Prodanovic H, et al. Diaphragm dysfunction on admission to the intensive care unit. Prevalence, risk factors, and prognostic impact-a prospective study [J]. Am J Respir Crit Care Med, 2013, 188(2): 213-219.
[16] Supinski GS, Morris PE, Dhar S, et al. Diaphragm dysfunction in critical illness [J]. Chest, 2018, 153(4): 1040-1051.
[17] 黄位明. 电针联合体外膈肌起搏在机械通气膈肌功能障碍患者中的应用研究[D]. 杭州: 浙江中医药大学, 2023.
[18] 唐智生. 膈肌功能障碍的超声检测进展[J]. 中国实用医药, 2023, 18(10): 177-180. TANG Zhisheng. Progress in ultrasonic detection of diaphragmatic dysfunction [J]. China Practical Medicine, 2023, 18(10): 177-180.
[19] Farghaly S, Hasan AA. Diaphragm ultrasound as a new method to predict extubation outcome in mechanically ventilated patients [J]. Aust Crit Care, 2017, 30(1): 37-43.
[20] Le Neindre A, Philippart F, Luperto M, et al. Diagnostic accuracy of diaphragm ultrasound to predict weaning outcome: a systematic review and meta-analysis [J]. Int J Nurs Stud, 2021, 117: 103890. doi: 10.1016/j.ijnurstu.2021.103890.
[21] 刘小丽. 阻塞性睡眠呼吸暂停与尿酸水平变化的相关性研究[D]. 石河子: 石河子大学, 2020.
[22] Kaptein MJ, Kaptein EM. Inferior vena cava collapsibility index: clinical validation and application for assessment of relative intravascular volume [J]. Adv Chronic Kidney Dis, 2021, 28(3): 218-226.
[23] 窦志敏, 曹永强, 刘欣, 等. 超声监测下腔静脉变异度对机械通气患者撤机结果的预测价值[J]. 中华超声影像学杂志, 2019, 28(2): 118-122. DOU Zhimin, CAO Yongqiang, LIU Xin, et al.Predictive value of ultrasonographic assessment of inferior vena cava variability in the weaning of patients with mechanical ventilation [J]. Chinese Journal of Ultrasonography, 2019, 28(2): 118-122.
[24] 赵浩天, 龙玲, 任珊, 等. 膈肌超声联合呼吸力学指标对ICU老年患者撤机预后评价功能[J]. 中国老年学杂志, 2021, 41(10): 2065-2069. ZHAO Haotian, LONG Ling, REN Shan, et al. The evaluation function of diaphragmatic ultrasound combined with respiratory mechanics indicators for the prognosis of elderly ICU patients after weaning [J]. Chinese Journal of Gerontology, 2021, 41(10): 2065-2069.
[25] Spadaro S, Grasso S, Mauri T, et al.Can diaphragmatic ultrasonography performed during the T-tube trial predict weaning failure? The role of diaphragmatic rapid shallow breathing index [J]. Crit Care, 2016, 20(1): 305.
[26] 姜明明, 章雪佳, 陈志鑫, 等. 胸部超声对脓毒症患者机械通气撤机结果的预测价值研究[J]. 中国全科医学, 2020, 23(30): 3870-3877. JIANG Mingming, ZHANG Xuejia, CHEN Zhixin, et al. Value of chest ultrasound in predicting the outcome of weaning from mechanical ventilation in patients with sepsis [J]. Chinese General Practice, 2020, 23(30): 3870-3877.
[1] 钟璐,薛付忠. 基于贝叶斯网络不确定性推理的肺癌风险预测模型[J]. 山东大学学报 (医学版), 2023, 61(4): 86-94.
[2] 张明慧,王丽云,王芸,张新月,沙凯辉. 髋部骨折患者术后恐动症风险的列线图分析[J]. 山东大学学报 (医学版), 2023, 61(11): 74-81.
[3] 巨艳丽,王丽华,成芳,黄凤艳,陈学禹,贾红英. 基于机器学习构建放射性碘治疗疗效的预测模型[J]. 山东大学学报 (医学版), 2023, 61(1): 94-99.
[4] 贺士卿,李皖皖,董书晴,牟婧怡,刘宇莹,魏思雨,刘钊,张家新. 基于数据库构建乳腺癌焦亡相关基因的预后风险模型[J]. 山东大学学报 (医学版), 2022, 60(8): 34-43.
[5] 李皖皖,周文凯,董书晴,贺士卿,刘钊,张家新,刘斌. 利用数据库信息构建乳腺癌免疫关联lncRNAs风险评估模型[J]. 山东大学学报 (医学版), 2021, 59(7): 74-84.
[6] 赵洁,李岩,李明,于德新. 螺旋CT对黏液性软组织肿瘤良恶性鉴别的价值[J]. 山东大学学报 (医学版), 2021, 59(4): 100-107.
[7] 李潘,李月月,李延青. 个体化肠道准备对肠道准备质量的影响[J]. 山东大学学报 (医学版), 2020, 58(3): 113-117.
[8] 孙艳婷,吴大玮,王晓斐,徐建,王睿. 新建医院ICU临床分离菌的分布及耐药变迁[J]. 山东大学学报 (医学版), 2020, 58(2): 64-71.
[9] 徐源佑,杨亚超,王春霞,马晓天,薛付忠,刘言训,王萍. 基于体检队列的胃炎风险预测模型[J]. 山东大学学报 (医学版), 2019, 57(6): 112-116.
[10] 周苗,夏同耀,孙爱玲,李明,申振伟,卞伟玮,蒋正,康凤玲,柳晓涓,薛付忠,刘静. 健康管理人群慢性肾脏病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 98-103.
[11] 孙苑潆,杨亚超,曲明苓,陈雁敏,李敏,王淑康,薛付忠,刘云霞. 健康管理人群代谢综合征发病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 87-92.
[12] 苏萍,杨亚超,杨洋,季加东,阿力木·达依木,李敏,薛付忠,刘言训. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 82-86.
[13] 张光,王广银,吴红彦, 张红玉,王停停,李吉庆,李敏,康凤玲,刘言训,薛付忠. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 72-76.
[14] 王春霞,许艺博,杨宁,夏冰,王萍,薛付忠. 基于健康管理队列的冠心病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 66-71.
[15] 于涛,刘焕乐,冯新,徐付印,陈亚飞,薛付忠,张成琪. 基于健康管理队列的高血压风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 61-65.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!