尼日利亚博尔诺州迈杜古里大学教学医院(UMTH)急性缺血性脑卒中患者的神经影像学表现及其预后价值

Iko Musa, Musa Joseph, Zakka Musa, Vinah Vivian Kehinde, David Adesoye Tunwagun, Rita Chineze Igweike, Cynthia Udan Kawai, Jeremiah Maina Yaga, Ibrahim Khalil Ja'afar, Mercy Akwum Olokpo, Grace Manmak Paul, Ifeoma Lauretta Chukwu, Patience Ungut Kanhu, Innocent Onyekachi Amanum, Yusuf Yakub, Wisdom Onyemaechi Mogbolu
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引用次数: 0

摘要

背景:在资源有限的情况下准确预测脑卒中结局仍然具有挑战性。本研究评估了尼日利亚迈杜古里大学教学医院的神经影像学结果在预测急性缺血性脑卒中患者死亡率方面的效用。方法:这项前瞻性研究在2023年1月至12月期间连续招募了171名急性缺血性卒中成人患者。所有患者都进行了非对比脑CT扫描,使用标准化测量方法计算梗死体积。主要终点为30天死亡率。多变量logistic回归分析确定了死亡率的独立预测因子,并用于建立风险分层系统。结果:大梗死面积(bbb10万mm3)是死亡率最强的独立预测因子(aOR 6.82, 95% CI 2.0522.68, p=0.002),其次是多区域累及(aOR 3.42, 95% CI 1.43-8.17, p=0.006)。建立的风险评分具有良好的判别能力(AUC 0.775, 95% CI 0.689-0.860),可将患者分为3个风险类别,死亡率分别为8.2%(低)、11.8%(中)和42.0%(高)。结论:特异性神经影像学参数可有效预测急性缺血性脑卒中的早期死亡率。开发的风险分层工具可以改善资源有限环境下的患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Neuroimaging Findings and Their Prognostic Value in Acute Ischaemic Stroke Patients at University of Maiduguri Teaching Hospital (UMTH), Borno State, Nigeria.

Neuroimaging Findings and Their Prognostic Value in Acute Ischaemic Stroke Patients at University of Maiduguri Teaching Hospital (UMTH), Borno State, Nigeria.

Neuroimaging Findings and Their Prognostic Value in Acute Ischaemic Stroke Patients at University of Maiduguri Teaching Hospital (UMTH), Borno State, Nigeria.

Background: Accurate prediction of stroke outcomes in resource-limited settings remains challenging. This study assessed the utility of neuroimaging findings in predicting mortality among acute ischaemic stroke patients at the University of Maiduguri Teaching Hospital, Nigeria.

Methodology: This prospective study enrolled 171 consecutive adults with acute ischaemic stroke between January and December 2023. All patients underwent non-contrast brain CT scanning, with infarct volume calculated using standardized measurements. The primary outcome was 30-day mortality. Multivariate logistic regression analysis identified independent predictors of mortality, which were used to develop a risk stratification system.

Results: Large infarct volume (>100,000 mm3) emerged as the strongest independent predictor of mortality (aOR 6.82, 95% CI 2.0522.68, p=0.002), followed by multiple territory involvement (aOR 3.42, 95% CI 1.43-8.17, p=0.006). The developed risk score demonstrated good discriminative ability (AUC 0.775, 95% CI 0.689-0.860) and stratified patients into three risk categories with mortality rates of 8.2% (low), 11.8% (intermediate), and 42.0% (high) (p<0.001).

Conclusion: Specific neuroimaging parameters can effectively predict early mortality in acute ischaemic stroke. The developed risk stratification tool could improve patient care in resource-limited settings.

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