Jin Jiaxin, Ma Pengzhen, Wang Eryu, Xie Yingzhen
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
Traditional Chinese Medicine Data Center, China Academy of Traditional Chinese Medicine, Beijing 100700, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2024 Dec 20;44(12):2375-2381. doi: 10.12122/j.issn.1673-4254.2024.12.13.
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.
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.
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 (<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 (<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%: 0.782-0.932) in the modeling set and 0.679 (95%: 0.563-0.794) in the validation set.
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.
探讨急性缺血性卒中（AIS）1年内复发的危险因素，并建立预测复发风险的列线图模型。
本研究纳入2021年3月至2022年3月在东直门医院（建模组）和房山区医院（验证组）住院的两队列AIS患者（≤7天）。采用Lasso回归分析确定AIS 1年内复发的重要预测因素，并进行多因素Logistic回归分析以分析影响AIS复发的独立因素。使用R studio构建复发风险预测列线图模型，并采用ROC曲线分析和Hosmer-Lemeshow拟合优度检验评估其区分能力和校准情况。
建模组和验证组分别有28例（15.22%）和21例（15.00%）AIS复发。在建模组中，与未复发组相比，复发组年龄>65岁、糖尿病、心律失常、卒中后便秘、空腹血糖>7.5 mmol/L患者的比例更高，且中性粒细胞与淋巴细胞比值（NLR）、尿素、肌酐、糖化血红蛋白（HbA1c）、纤维蛋白原（FIB）及凝血酶时间（TT）水平显著更高（P<0.05）。多因素Logistic回归分析显示，年龄>65岁、心律失常、卒中后便秘、空腹血糖>7.5 mmol/L、NLR及肌酐均为AIS复发的独立危险因素（P<0.05）。Hosmer-Lemeshow拟合优度检验及校准曲线分析显示风险预测模型在建模组与验证组之间具有良好的拟合度。ROC曲线显示对于预测AIS 1年内复发，预测模型在建模组的曲线下面积（AUC）为0.857（95%可信区间：0.7820.932），在验证组为0.679（95%可信区间：0.5630.794）。
基于年龄>65岁、心律失常、卒中后便秘、空腹血糖>7.5 mmol/L、NLR及肌酐建立的列线图模型对AIS 1年内复发具有良好的预测价值。
