Babaee Mahsa, Atashgar Karim, Amini Harandi Ali, Yousefi Atefeh
Faculty of Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.
Brain Mapping Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Basic Clin Neurosci. 2024 Jan-Feb;15(1):89-100. doi: 10.32598/bcn.2022.3608.1. Epub 2024 Jan 1.
Although several studies have been published about COVID-19, ischemic stroke is known yet as a complicated problem for COVID-19 patients. Scientific reports have indicated that in many cases, the incidence of stroke in patients with COVID-19 leads to death.
The obtained mathematical equation in this study can help physicians' decision-making about treatment and identification of influential clinical factors for early diagnosis.
In this retrospective study, data from 128 patients between March and September 2020, including their demographic information, clinical characteristics, and laboratory parameters were collected and analyzed statistically. A logistic regression model was developed to identify the significant variables in predicting stroke incidence in patients with COVID-19.
Clinical characteristics and laboratory parameters for 128 patients (including 76 males and 52 females; with a mean age of 57.109±15.97 years) were considered as the inputs that included ventilator dependence, comorbidities, and laboratory tests, including WBC, neutrophil, lymphocyte, platelet count, C-reactive protein, blood urea nitrogen, alanine transaminase (ALT), aspartate transaminase (AST) and lactate dehydrogenase (LDH). Receiver operating characteristic-area under the curve (ROC-AUC), accuracy, sensitivity, and specificity were considered indices to determine the model capability. The accuracy of the model classification was also addressed by 93.8%. The area under the curve was 97.5% with a 95% CI.
The findings showed that ventilator dependence, cardiac ejection fraction, and LDH are associated with the occurrence of stroke and the proposed model can predict the stroke effectively.
尽管已经发表了多项关于新冠病毒病（COVID-19）的研究，但缺血性中风仍是COVID-19患者面临的一个复杂问题。科学报告表明，在许多情况下，COVID-19患者中风的发生率会导致死亡。
本研究中获得的数学方程有助于医生在治疗决策以及识别早期诊断的有影响的临床因素方面提供帮助。
在这项回顾性研究中，收集了2020年3月至9月期间128例患者的数据，包括他们的人口统计学信息、临床特征和实验室参数，并进行了统计分析。建立了一个逻辑回归模型来识别预测COVID-19患者中风发生率的显著变量。
128例患者（包括76例男性和52例女性；平均年龄为57.109±15.97岁）的临床特征和实验室参数被视为输入变量，包括呼吸机依赖、合并症以及实验室检查，如白细胞、中性粒细胞、淋巴细胞、血小板计数、C反应蛋白、血尿素氮、丙氨酸转氨酶（ALT）、天冬氨酸转氨酶（AST）和乳酸脱氢酶（LDH）。曲线下面积（ROC-AUC）、准确性、敏感性和特异性被视为确定模型能力的指标。模型分类的准确性也达到了93.8%。曲线下面积为97.5%，95%置信区间。
研究结果表明，呼吸机依赖、心脏射血分数和LDH与中风的发生有关，所提出的模型可以有效地预测中风。
