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基于临床数据构建烟雾病与非烟雾病缺血性卒中的鉴别诊断模型

Construction of a differential diagnosis model of ischemic stroke between moyamoya disease and non-moyamoya disease based on clinical data

    摘要
  • 摘要:
    目的  筛选临床上易于获得的变量构建烟雾病(MMD)与非MMD缺血性卒中的鉴别诊断模型。
    方法  纳入确诊为MMD缺血性卒中患者150例和非MMD缺血性卒中患者150例，按照7∶3的比例分为训练组(210例)和验证组(90例)。利用Logistic回归分析、Lasso回归、支持向量机(SVM)构建诊断模型; 通过列线图将最优模型可视化，并分别在训练组和验证组中检验列线图的鉴别能力。
    结果  二分类Logistic回归模型在训练组和验证组中C统计量最大(0.87和0.88)。多因素Logistic回归分析差异有统计学意义(P < 0.05)的变量为收缩压、总胆固醇(TC)、白蛋白(ALB)、游离三碘甲腺原氨酸(FT3)、同型半胱氨酸(HCY)、年龄，将这6个变量纳入列线图。列线图在训练组和验证组的Hosmer-Lemeshow检验P值分别为0.28、0.19, 校正曲线校正度良好。列线图评分 < 168分为MMD缺血性卒中低风险，列线图评分≥168分为MMD缺血性卒中高风险。
    结论  本研究构建的列线图可鉴别MMD和非MMD缺血性卒中，经过验证该模型在训练组和验证组中均有良好的鉴别能力。

     

    Abstract:
    Objective  To establish a differential diagnosis model between moyamoya disease (MMD) and non-MMD ischemic stroke by screening clinically accessible variables.
    Methods  A total of 150 patients diagnosed with MMD ischemic stroke and 150 patients diagnosed with non-MMD ischemic stroke were included and divided into training group (210 cases) and validation group (90 cases) according to a ratio of 7 to 3. Logistic regression analysis, Lasso regression and support vector machine (SVM) were used to construct the diagnosis model; the optimal model was visualized with nomogram, and discriminant ability of the nomogram was tested in the training group and the validation group respectively.
    Results  Binary Logistic regression showed the largest C statistics in the training group and the verification group (0.87 and 0.88). Multivariate logistic regression analysis showed statistically significant differences (P < 0.05) in systolic blood pressure, total cholesterol (TC), albumin (ALB), free triiodothyranine (FT3), homocysteine (HCY) and age, and these six variables were included in the nomogram. Hosmer-Lemeshow test P values for the training group and the verification group were 0.28 and 0.19, respectively, and the calibration curve was well calibrated.The nomogram score < 168 was classified as a low risk of ischemic stroke in MMD, and the nomogram score ≥168 was classified as a high risk of ischemic stroke in MMD.
    Conclusion The nomograms established in the study can be used to distinguish MMD from non-MMD ischemic stroke, and it has been verified that the model has good discriminative ability in both training group and verification group.

     

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