卒中后吞咽障碍患者卒中相关性肺炎预测模型的构建

doi:10.3877/cma.j.issn.1674-6902.2023.06.010

中华肺部疾病杂志(电子版) ›› 2023, Vol. 16 ›› Issue (06) : 793 -798. doi: 10.3877/cma.j.issn.1674-6902.2023.06.010

论著

卒中后吞咽障碍患者卒中相关性肺炎预测模型的构建

黎倩, 何静^†(

), 李燕玲, 罗业涛, 徐婷, 吴敏, 王羚入

400037　重庆，陆军（第三）军医大学第二附属医院神经内科医学中心
400037　重庆，陆军（第三）军医大学第二附属医院感染控制科
400038　重庆，陆军（第三）军医大学第一附属医院神经内科
400042　重庆，陆军特色医学中心神经内科

收稿日期:2023-09-17 出版日期:2023-12-25
通信作者: 何静
基金资助:
重庆市卫生适宜技术推广项目(2022jstg007)

Establishment of a predictive model for stroke-associated pneumonia in post-stroke patients with swallowing disorders

Qian Li, Jing He^†(), Yanling Li, Yetao Luo, Ting Xu, Ming Wu, Lingru Wang

Department of Neurology, Second Affiliated Hospital of Army Military Medical University, Chongqing 400037, China
Department of Hospital-Acquired Infection Control, Second Affiliated Hospital of Army Military Medical University, Chongqing 400037, China
Department of Neurology, First Affiliated Hospital of Army Military Medical University, Chongqing 400038, China
Department of Neurology, Army Specialized Medical Center, Chongqing 400042, China

Received:2023-09-17 Published:2023-12-25
Corresponding author: Jing He

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引用本文：

黎倩, 何静, 李燕玲, 罗业涛, 徐婷, 吴敏, 王羚入. 卒中后吞咽障碍患者卒中相关性肺炎预测模型的构建[J]. 中华肺部疾病杂志(电子版), 2023, 16(06): 793-798.

Qian Li, Jing He, Yanling Li, Yetao Luo, Ting Xu, Ming Wu, Lingru Wang. Establishment of a predictive model for stroke-associated pneumonia in post-stroke patients with swallowing disorders[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2023, 16(06): 793-798.

目的

分析卒中后吞咽障碍患者卒中相关性肺炎(stroke-associated pneumonia, SAP)的影响因素，构建SAP风险预测模型并验证其预测效果。

方法

前瞻性连续纳入2022年11月至2023年5月，重庆市3所医院神经内科住院的卒中后吞咽障碍患者为对象，调查患者一般情况、临床资料、吞咽及口腔健康状况。应用弹性网络筛选预测因素，多因素Logistic回归建模并进行验证，绘制列线图展示模型结果。

结果

共纳入211例患者，46例(21.80%)发生卒中相关性肺炎。弹性网络共筛选出5个预测因素，即美国国立卫生院卒中量表(national institutes of health stroke scale, NIHSS评分)、洼田饮水试验、唾液分泌状况、口腔健康评估量表(oral health assessment tool, OHAT)评分、口腔卫生状况。似然比检验结果提示，模型在统计上显著(χ²＝104.17，P<0.01)，判别指数为0.960。模型受试者操作特征曲线下面积(AUC)为0.93(95%CI：0.87～0.93)，最佳风险阈值为0.10，准确度为83.87%，灵敏度为84.62%，特异度为83.67%。

结论

构建的模型能较好地预测卒中后吞咽障碍患者SAP风险，对尽早识别高风险患者、制定个体化预防策略具有临床意义。

关键词: 脑卒中, 吞咽障碍, 口腔健康, 卒中相关性肺炎, 风险预测模型

Objective

Investigate the influencing factors of stroke-associated pneumonia (SAP) in patients with post-stroke dysphagia, and to develop a risk prediction model for SAP, as well as verify its predictive effectiveness.

Methods

Prospective continuous inclusion of patients with post-stroke dysphagia admitted to the neurology departments of three hospitals in Chongqing from November 2022 to May 2023. Investigated the general characteristics, clinical data, swallowing function, and oral health status of the patients. An elastic net method was applied to select predictive factors, followed by constructing a multiple-factor logistic regression model and validating it. The results were presented using nomogram to display the outcomes of the model.

Results

A total of 211 patients were included, 56(21.80%)of whom developed SAP. The SAP prediction model included 5 items, including national institutes of health stroke scale(NIHSS) score, Water swallow test, salivation status, OHAT score and oral hygiene status. The likelihood ratio test results showed that the model was statistically significant (χ²=132.62, P<0.001)and the discrimination index was 0.960. The area under the ROC curve of this model was 0.93(95%CI: 0.87-0.93), with the optimal critical value of 0.10, accuracy of 83.87%, sensitivity of 84.62% and the specificity of 83.67%.

Conclusion

The constructed model can effectively predict the risk of stroke-associated pneumonia (SAP) in patients with post-stroke dysphagia. This has clinical significance in identifying high-risk patients early and developing personalized preventive strategies.

Key words: Stroke, Dysphagia, Oral health, Stroke-associated pneumonia, Prediction model

表1 卒中后吞咽障碍患者卒中相关性肺炎结果[n(%)]

临床资料	分　类	对照组(n＝165)	观察组(n＝46)	检验统计量	P值
卒中类型	脑梗死	161(97.58)	42(91.30)	3.88^a	0.040
	脑出血	4(2.42)	4(8.70)
NIHSS评分[M(P₂₅，P₇₅)]		4(2，7)	10(6，15)	35.33^c	0.000
Barthel评分	完全自理(100分)	18(10.91)	0(0.00)	42.51^a	0.000
	轻度功能依赖(60～99分)	50(30.30)	3(6.53)
	中度功能依赖(41～60分)	41(24.85)	6(13.04)
	重度功能依赖(20～40分)	42(25.46)	18(39.13)
	极重度功能依赖(<20分)	14(8.48)	19(41.30)
洼田饮水试验	3级	114(69.09)	4(8.70)	71.33^a	0.000
	4级	39(23.64)	17(36.95)
	5级	12(7.27)	25(54.35)
摄食吞咽功能	1级	3(1.82)	11(23.91)	77.94^a	0.000
	2级	9(5.45)	16(34.78)
	3级	4(2.42)	3(6.52)
	4级	21(12.73)	9(19.57)
	5级	59(35.76)	5(10.87)
	6级	69(41.82)	2(4.35)
唾液分泌状况	>25 mm/3 min	138(83.64)	10(21.74)	65.81^a	0.000
	≤25 mm/3 min	27(16.36)	36(78.26)
口腔卫生状况	口腔卫生达标	128(77.58)	3(6.52)	77.15^a	0.00
	口腔卫生不佳	37(22.42)	43(93.48)

表1 卒中后吞咽障碍患者卒中相关性肺炎结果[n(%)]

临床资料	分　类	对照组(n＝165)	观察组(n＝46)	检验统计量	P值
卒中类型	脑梗死	161(97.58)	42(91.30)	3.88^a	0.040
	脑出血	4(2.42)	4(8.70)
NIHSS评分[M(P₂₅，P₇₅)]		4(2，7)	10(6，15)	35.33^c	0.000
Barthel评分	完全自理(100分)	18(10.91)	0(0.00)	42.51^a	0.000
	轻度功能依赖(60～99分)	50(30.30)	3(6.53)
	中度功能依赖(41～60分)	41(24.85)	6(13.04)
	重度功能依赖(20～40分)	42(25.46)	18(39.13)
	极重度功能依赖(<20分)	14(8.48)	19(41.30)
洼田饮水试验	3级	114(69.09)	4(8.70)	71.33^a	0.000
	4级	39(23.64)	17(36.95)
	5级	12(7.27)	25(54.35)
摄食吞咽功能	1级	3(1.82)	11(23.91)	77.94^a	0.000
	2级	9(5.45)	16(34.78)
	3级	4(2.42)	3(6.52)
	4级	21(12.73)	9(19.57)
	5级	59(35.76)	5(10.87)
	6级	69(41.82)	2(4.35)
唾液分泌状况	>25 mm/3 min	138(83.64)	10(21.74)	65.81^a	0.000
	≤25 mm/3 min	27(16.36)	36(78.26)
口腔卫生状况	口腔卫生达标	128(77.58)	3(6.52)	77.15^a	0.00
	口腔卫生不佳	37(22.42)	43(93.48)

图1 预测集ROC曲线

表2 多因素Logistic回归结果

相关指标	β值	标准误	Wald χ²	P值
常量	－8.071	1.733	－4.657	0.000
NIHSS评分	0.079	0.076	1.029	0.304
洼田饮水等级(4级)	3.215	1.154	2.784	0.000
洼田饮水等级(5级)	3.747	1.334	2.810	0.000
OHAT评分	0.354	0.272	1.298	0.194
口腔卫生不佳	3.108	0.923	3.366	0.000
唾液分泌不足	0.357	0.807	0.442	0.658

表2 多因素Logistic回归结果

相关指标	β值	标准误	Wald χ²	P值
常量	－8.071	1.733	－4.657	0.000
NIHSS评分	0.079	0.076	1.029	0.304
洼田饮水等级(4级)	3.215	1.154	2.784	0.000
洼田饮水等级(5级)	3.747	1.334	2.810	0.000
OHAT评分	0.354	0.272	1.298	0.194
口腔卫生不佳	3.108	0.923	3.366	0.000
唾液分泌不足	0.357	0.807	0.442	0.658

图2 卒中后吞咽障碍患者SAP风险预测模型列线图

表3 不同变量筛选方法建立的预测模型对SAP的预测(%)

比较指标	弹性网络	逐步回归
准确度	90.60	72.58
灵敏度	94.74	84.62
特异度	77.14	69.39
阳性预测值	93.10	42.31
阴性预测值	81.82	94.44

表3 不同变量筛选方法建立的预测模型对SAP的预测(%)

比较指标	弹性网络	逐步回归
准确度	90.60	72.58
灵敏度	94.74	84.62
特异度	77.14	69.39
阳性预测值	93.10	42.31
阴性预测值	81.82	94.44

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[15]	许毛毛, 王宝军, 石雨, 陈超, 庞江霞. 人工寒潮和热浪对易卒中型肾血管性高血压大鼠脑卒中的影响[J]. 中华脑血管病杂志(电子版), 2023, 17(03): 237-243.

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Establishment of a predictive model for stroke-associated pneumonia in post-stroke patients with swallowing disorders

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Establishment of a predictive model for stroke-associated pneumonia in post-stroke patients with swallowing disorders