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建立并验证一种用于区分院前出血性卒中和缺血性卒中的临床列线图。

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital.

机构信息

Department of Emergency Medicine, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.

Emergency Sub-Station, The Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.

出版信息

BMC Neurol. 2023 Mar 3;23(1):95. doi: 10.1186/s12883-023-03138-1.

Abstract

OBJECTIVES

The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS).

METHODS

This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots.

RESULTS

A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740-0.852, P < 0.001) and 0.808 (95% CI:0.728-0.887, P < 0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke.

CONCLUSIONS

This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

摘要

目的

在院外疑似中风症状的患者中,早期发现和识别中风对于患者的预后至关重要。我们旨在建立一种基于 FAST 评分的风险预测模型,以便为急救医疗服务(EMS)人员早期识别不同类型的中风。

方法

本回顾性观察研究纳入了 2020 年 1 月至 2021 年 12 月在一家单中心就诊的 394 例中风患者。从 EMS 记录数据库中收集患者的人口统计学数据、临床特征和中风危险因素。采用单因素和多因素逻辑回归分析确定独立的风险预测因素。根据独立预测因素制定了列线图,通过接受者操作特征(ROC)曲线和校准图验证了列线图的判别价值和校准度。

结果

在训练集中,共有 31.90%(88/276)的患者被诊断为出血性中风,而在验证集中,这一比例为 36.40%(43/118)。列线图是基于多因素分析建立的,包括年龄、收缩压、高血压、呕吐、手臂无力和言语不清。ROC 曲线下面积(AUC)在训练集和验证集中分别为 0.796(95%CI:0.740-0.852,P<0.001)和 0.808(95%CI:0.728-0.887,P<0.001)。此外,在两个集合中,列线图的 AUC 均优于 FAST 评分。校准曲线表明列线图具有良好的一致性,决策曲线分析也表明列线图在预测出血性中风风险方面具有比 FAST 评分更宽的阈值概率范围。

结论

该新型非侵入性临床列线图在 EMS 人员院外早期鉴别出血性和缺血性中风方面具有良好的性能。此外,列线图的所有变量在临床实践中都可以很容易且经济地获得,无需在院外进行。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7cf/9983153/f360cf10bba2/12883_2023_3138_Fig1_HTML.jpg

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