卒中相关性肺炎患者神经功能恶化的影像指标及其预测价值分析

doi:10.3969/j.issn.1006-7795.2022.01.014

摘要/Abstract

摘要： 目的探讨卒中相关性肺炎(stroke-associated pneumonia, SAP)患者发生神经功能恶化(neurological deterioration, ND)的影像标志物,并对其预测价值进行分析。方法回顾性收集2020年1月至12月急诊或神经内、外科收治的SAP患者,根据美国国立卫生研究院卒中量表(National Institutes of Health Stroke Scale,NIHSS)是否增加≥4分,分为ND组与非ND组。首先,通过单因素分析比较两组患者临床特征、实验室检查、脑部磁共振成像(magnetic resonance imaging, MRI)及肺部计算机断层扫描(computed tomography, CT)影像学指标差异;其次,将单变量分析P<0.05的指标纳入多因素Logistic回归模型进行分析。结果本研究共纳入SAP患者73例,其中有15例发生ND(20.54%)。单因素结果显示两组患者在枕叶梗死、丘脑梗死、大面积脑梗死和肺部CT评分等方面差异有统计学意义,Logistic 回归分析显示,肺部CT评分(OR=1.218,95%CI:1.010~1.469)、是否为大面积脑梗死(OR=9.033,95%CI:1.746~46.742)、丘脑梗死(OR=10.016,95%CI:1.523~65.862)是SAP发生ND的独立危险因素,受试者工作特征(receiver operating characteristic,ROC)曲线分析显示,曲线下面积(area under curve, AUC)为0.849(95%CI:0.752~0.947),表明模型拟合效果较好;将多因素Logistic回归分析中OR值最大的丘脑梗死进行ROC分析,AUC为0.624(95％CI:0.450~0.797),其预测ND的敏感度为33.33％,特异度为91.38％,阳性预测值为50.00%,阴性预测值为84.13%,准确率为79.45%;大面积脑梗死及肺部CT评分联合分析AUC为0.799(95％CI:0.690~0.909),预测ND的敏感度为100.00%,特异度为48.28%,阳性预测值为33.33%,阴性预测值为100%,准确率为58.90%;肺部CT评分AUC为0.744(95％CI:0.611~0.878),取肺部CT评分截断值为4.5时,其预测ND的敏感度为80.00％,特异度为63.80％,阳性预测值为36.36%,阴性预测值为92.50%,准确率为67.12%。结论肺部CT评分、大面积脑梗死、丘脑梗死可能是SAP患者发生ND的危险因素。肺部CT评分敏感度及诊断价值高,且肺部CT评分越高(＞4.5分),ND转化风险越高。丘脑梗死的特异度且诊断价值较高,可以预测SAP发生ND。脑梗死范围及肺部CT评分联合预测ND的敏感度高,漏诊率低,可以提示临床对于可能发生ND的患者重点关注。

Abstract: Objective To investigate the risk factors for the occurrence of neurological deterioration (ND) in patients with stroke-associated pneumonia (SAP) and to establish a predictive model using imaging features. Methods We retrospectively recruited SAP patients admitted to emergency or neurology departments during January to December 2020, and SAP patients were divided into ND and non-ND groups according to whether the National Institutes of Health Stroke Scale (NIHSS) score increased by ≥ 4 (total) during hospitalization. The clinical characteristics, laboratory tests, brain and lung imaging features of the two groups were compared. Variables with a P value<0.05 by univariate analysis were included in Logistic regression analysis. Results We retrospectively recruited 73 SAP patients. ND was diagnosed in 15 patients (20.54%). The chest computed tomography (CT) score, large infarction, thalamic infarction and occipital lobe infarction were significantly different between the two groups (P<0. 05). In particular, chest CT score (OR=1.218,95%CI:1.010-1.469), thalamic infarction (P<0.05, OR=10.016, 95%CI:1.523-65.862) and large cerebral infarction (P<0.05, OR=9.033, 95%CI:1.746-46.742) were independent risk factors for the occurrence of ND. For prediction of ND, the area under curve (AUC) of the logistic model was 0.849 (95% CI:0.752-0.947); the AUC of chest CT score was 0.744 (95% CI:0.611-0.878) with sensitivity 80.00%, specificity 63.80%, predictive positive value (PPV) 36.36%,predictive negative value (PNV) 92.50%, accuracy 67.12%; the AUC of thalamic infarction was 0.624 (95% CI :0.450-0.797), with sensitivity 33.33%, specificity 91.38%, PPV 50.00%, PNV 84.13%, accuracy 79.45%; and the AUC of combined large infarction and chest CT score was 0.799 (95%CI:0.690-0.909) with sensitivity 100.00%, specificity 48.28%, PPV 33.33%, PNV 100.00%, accuracy 58.9%. Conclusion Our study indicated that chest CT score, thalamic infarction and large cerebral infarction may be independently associated with ND in SAP. The chest CT score may predict ND in SAP patients with higher sensitivity and accuracy, what's more, the higher the chest CT score (>4.5), the more likely to develop ND in SAP patients. Thalamic infarction may predict ND in SAP patients with higher specificity and accuracy. The combined analysis of chest CT score and lesion size of infarction can help clinicians to stratify the SAP patients with higher risk to develop ND.

Key words: stroke-associated pneumonia, neurological deterioration, thalamic infarction, chest computed tomography (CT) score, large cerebral infarction

中图分类号:

R445

孙鹏慧, 李莹莹, 刘鑫, 贾学佳, 贾秀琴, 杨旗. 卒中相关性肺炎患者神经功能恶化的影像指标及其预测价值分析[J]. 首都医科大学学报, 2022, 43(1): 74-81.

Sun Penghui, Li Yingying, Liu Xin, Jia Xuejia, Jia Xiuqin, Yang Qi. Prediction on neurological deterioration based on imaging features in patients with stroke-associated pneumonia[J]. Journal of Capital Medical University, 2022, 43(1): 74-81.

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