Risk assessment items for silent aspiration in coma patients with tracheotomy based on Delphi method and a clinical empirical study

Jinyu PAN; Xin SHU; Wei SUN

doi:10.16016/j.2097-0927.202307067

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Risk assessment items for silent aspiration in coma patients with tracheotomy based on Delphi method and a clinical empirical study

Clinical Medicine | 更新时间：2026-01-09

- Risk assessment items for silent aspiration in coma patients with tracheotomy based on Delphi method and a clinical empirical study
- Journal of Army Medical University Vol. 45, Issue 20, Pages: 2113-2119(2023)
- 作者机构：
  
  1.400038 重庆，陆军军医大学(第三军医大学)第一附属医院临床护理学教研室
  2.400038 重庆，陆军军医大学(第三军医大学)第一附属医院麻醉科
- 作者简介：
- 基金信息：
  
  the National Key Research and Development Program(2018YFC0116702)
- DOI：10.16016/j.2097-0927.202307067
  CLC： R473.6;R563.9;R653
- Received：08 July 2023，
  
  Revised：2023-08-10，
  
  Published：30 October 2023
- 稿件说明：
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Jinyu PAN, Xin SHU, Wei SUN. Risk assessment items for silent aspiration in coma patients with tracheotomy based on Delphi method and a clinical empirical study[J]. Journal of Army Medical University, 2023, 45(20): 2113-2119.
DOI：

Jinyu PAN, Xin SHU, Wei SUN. Risk assessment items for silent aspiration in coma patients with tracheotomy based on Delphi method and a clinical empirical study[J]. Journal of Army Medical University, 2023, 45(20): 2113-2119. DOI： 10.16016/j.2097-0927.202307067.

摘要

目的

构建气管切开昏迷患者沉默性误吸风险评估指标体系。

方法

在文献查阅及小组讨论基础上，于2021年9月至2023年5月采用Delphi法对29名相关领域专家进行2轮咨询，筛选优化指标条目，根据结果收集陆军军医大学第一附属医院2014年5月至2018年1月收治的315例气管切开昏迷患者相关数据，构建4种机器学习模型进行评估验证，包括Logistic回归(Logistic Regression，LR)、极端梯度提升(eXtreme Gradient Boosting，XGBoost)、朴素贝叶斯(Naive Bayes，NB)、支持向量机(Support Vector Machine，SVM)，通过受试者工作曲线(receiver operating characteristic curve，ROC) 和曲线下面积(area under the ROC curve，AUC)、敏感性、特异性、阳性预测值、阴性预测值、F1分数和准确率来评估模型效能。

结果

最终获得21项风险评估指标，含50个条目。2轮专家问卷咨询的积极系数分别为96.7%和100%，权威系数均为0.88，肯德尔协调系数分别为0.362、0.222(

0.001)，各条目重要性、准确性变异系数范围为0.080~0.299和0.091~0.191。LR、NB、XGboost及SVM模型的AUC依次为0.977、0.981、0.953和0.978。

结论

构建了包括气切导管类型、气切导管气囊压力、机械通气、鼻饲方式、胃残余量、吞咽功能、咳嗽反射、口腔清洁度等21项风险评估指标，含50个条目，经临床验证可评估预测气管切开昏迷患者沉默性误吸风险。

Abstract

Objective

To construct a risk factor assessment scale for silent aspiration in comatose patients with tracheotomy.

Methods

Based on literature review and group discussion

29 experts were consulted in 2 rounds using Delphi method to optimize the items during September 2021 and May 2023. After the clinical data of 315 coma patients with tracheotomy admitted to our hospital between May 2014 and January 2018 were retrospectively collected

4 machine learning models

including Logistic Regression (LR)

eXtreme Gradient Boosting (XGBoost)

Naive Bayes (NB) and Support Vector Machine (SVM)

were established. Receiver operating characteristic (ROC) curve was drawn and area under the ROC curve (AUC)

sensitivity

specificity

positive predictive value

negative predictive value

F1 score and accuracy were calculated to evaluate the effectiveness.

Results

Finally

a total of 21 risk assessment indicators involving 50 grade items were identified. The positive coefficients of the 2 rounds of expert consultation were 96.7% and 100%

respectively. The authority coefficients for the 2 rounds were 0.88

and the Kendall coordination coefficients were 0.362 and 0.222 (

0.001)

respectively. The coefficients of variation of the importance and accuracy of each item ranged from 0.080 to 0.299 and from 0.091 to 0.191

respectively. The AUCs of LR

XGboost and SVM models were 0.977

0.981

0.953 and 0.978

respectively.

Conclusion

Our study identifies 21 risk assessment indicators

including endotracheal tube type

tracheotomy catheter balloon pressure

mechanical ventilation

nasal feeding

gastric remnant

swallowing function

cough reflex and oral cleanliness

and 50 grade items. Clinical verification shows that the constructed risk assessment indicators have good predictive efficacy and can provide reference for the assessment of silent aspiration risk in coma patients with tracheotomy.

关键词

Keywords

references

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