Objectives: To investigate the risk factors of recurrence of acute ischemic stroke (AIS) within 1 year and establish a nomogram model for predicting the recurrence risk.
Methods: This study was conducted in two cohorts of AIS patients (≤7 days) hospitalized in Dongzhimen Hospital (modeling set) and Fangshan Hospital (validation set) from March, 2021 to March, 2022. Lasso regression analysis was used to identify the important predictive factors for AIS recurrence within 1 year, and multivariate Logistic regression analysis was performed to analyze the independent factors affecting AIS recurrence. The recurrence risk prediction nomogram model was constructed using R studio, and its discriminating power and calibration were assessed using ROC curve analysis and Hosmer-Lemeshow goodness-of-fit test.
Results: The modeling and validation sets contained 28 cases (15.22%) and 21 cases (15.00%) of AIS recurrence, respectively. In the modeling set, compared with the non-relapse group, the recurrence group had higher proportions of patients with age >65 years, diabetes, arrhythmia, constipation after stroke, and FBG >7.5 and significantly higher levels of NLR, UREA, Cr, HbA1c, FIB and TT (P<0.05). Multivariate Logistic regression analysis showed that an age >65 years, arrhythmia, constipation after stroke, FBG >7.5, NLR and Cr were all independent risk factors of AIS recurrence (P<0.05). Hosmer-Lemeshow goodness-of-fit test and calibration curve analysis showed that the risk prediction model had good fitting between the modeling set and the verification set. The ROC curve showed that for predicting AIS recurrence within 1 year, the AUC of the predictive model was 0.857 (95%CI: 0.782-0.932) in the modeling set and 0.679 (95%CI: 0.563-0.794) in the validation set.
Conclusions: The nomogram model established based on age >65 years, arrhythmia, constipation after stroke, FBG >7.5, NLR and Cr has a good predictive value for AIS recurrence within 1 year.
目的: 探讨急性缺血性卒中患者1年内复发的影响因素，并构建其复发风险预测列线图模型。方法: 纳入2021年3月~2022年3月于北京中医药大学东直门医院住院治疗的184例急性缺血性卒中（≤7 d）患者为建模集，另纳入2021年3月~2022年3月于北京中医药大学房山医院住院治疗的140例急性缺血性卒中（≤7 d）患者为外部验证集。收集患者临床资料，并进行为期1年的电话随访，依据是否出现结局事件将患者分为复发组与未复发组。使用Lasso回归筛选重要预测因素，多因素Logistic回归分析探讨急性缺血性卒中患者1年内复发的独立影响因素。运用R studio软件建立复发风险预测列线图模型，ROC曲线评估该模型的区分度，Hosmer-Lemeshow拟合优度检验与校准曲线评估模型校准度。结果: 建模集患者复发28例（15.22%），外部验证集患者复发21例（15.00%）。在建模集，复发患者年龄>65岁、糖尿病、心律失常、卒中后便秘、FBG>7.5的占比高于未复发患者，中性粒细胞与淋巴细胞计数比值（NLR）、尿素氮、肌酐、糖化血红蛋白、纤维蛋白原含量、凝血酶凝结时间水平高于未复发患者（P<0.05）。多因素Logistic回归分析结果显示，年龄>65岁、心律失常、卒中后便秘、空腹血糖>7.5、NLR升高、肌酐升高是急性缺血性卒中患者1年内复发的独立危险因素（P<0.05）。Hosmer-Lemeshow拟合优度检验与校准曲线分析显示，建模集与外部验证集中该风险预测列线图模型拟合良好。ROC曲线分析显示，该列线图模型预测建模集与外部验证集急性缺血性卒中患者1年内复发的AUC分别为0.857［95% CI（0.782-0.932）］、0.679［95% CI（0.563-0.794）］。结论: 基于年龄>65岁、心律失常、卒中后便秘、空腹血糖>7.5、NLR、肌酐等预测因素构建的列线图模型对急性缺血性卒中患者1年内复发具有一定预测价值。.
Keywords: Lasso regression; influencing factors; ischemic stroke; predictive model; recurrence.