PDF(Pubmed)
Abstract：
UNASSIGNED: Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.
UNASSIGNED: A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient\' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants\' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.
UNASSIGNED: Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.
UNASSIGNED: These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.
UNASSIGNED: A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient\' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants\' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.
UNASSIGNED: Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.
UNASSIGNED: These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.
摘要：
■超过50％的急性缺血性卒中（AIS）幸存者承受不同程度的残疾，复发率为17.7%。因此，AIS结局的预测可能对治疗决策有用.本研究旨在确定机器学习方法在AIS患者中预测早期结果的适用性。
2020年1月至2022年10月，蚌埠医科大学第一附属医院和第二附属医院神经内科收治的659例新发AIS患者纳入研究。病人的人口统计信息，病史,Org10,172在急性中风治疗（TOAST）中的试验，收集美国国立卫生研究院卒中量表(NIHSS)及入院24h实验室指标数据。改良兰金量表（mRS）用于评估参与者的3口预后。我们基于18个参数构建了9个机器学习模型，并比较了它们对结果变量的准确性。
■通过最小绝对收缩和选择算子交叉验证（LassoCV）方法进行的特征选择确定了AIS患者早期预后的最关键预测因子为白细胞（WBC），同型半胱氨酸（HCY），D-二聚体,基线NIHSS,纤维蛋白原降解产物（FDP），和葡萄糖（GLU）。在评估的九种机器学习模型中，随机森林模型在测试集中表现出优异的性能，曲线下面积(AUC)为0.852,准确率为0.818,灵敏度为0.654,特异性为0.945,召回率为0.900。
■这些发现表明，利用从入院最初24小时的一般临床和实验室数据的RF模型可以有效预测AIS患者的早期预后。
2020年1月至2022年10月，蚌埠医科大学第一附属医院和第二附属医院神经内科收治的659例新发AIS患者纳入研究。病人的人口统计信息，病史,Org10,172在急性中风治疗（TOAST）中的试验，收集美国国立卫生研究院卒中量表(NIHSS)及入院24h实验室指标数据。改良兰金量表（mRS）用于评估参与者的3口预后。我们基于18个参数构建了9个机器学习模型，并比较了它们对结果变量的准确性。
■通过最小绝对收缩和选择算子交叉验证（LassoCV）方法进行的特征选择确定了AIS患者早期预后的最关键预测因子为白细胞（WBC），同型半胱氨酸（HCY），D-二聚体,基线NIHSS,纤维蛋白原降解产物（FDP），和葡萄糖（GLU）。在评估的九种机器学习模型中，随机森林模型在测试集中表现出优异的性能，曲线下面积(AUC)为0.852,准确率为0.818,灵敏度为0.654,特异性为0.945,召回率为0.900。
■这些发现表明，利用从入院最初24小时的一般临床和实验室数据的RF模型可以有效预测AIS患者的早期预后。
