Shao Kangmei, Zhang Fan, Li Yongnan, Cai Hongbin, Paul Maswikiti Ewetse, Li Mingming, Shen Xueyang, Wang Longde, Ge Zhaoming
Department of Neurology, Lanzhou University Second Hospital, Lanzhou 730030, China.
Gansu Provincial Neurology Clinical Medical Research Center, Lanzhou University Second Hospital, Lanzhou 730030, China.
Brain Sci. 2023 Jul 10;13(7):1051. doi: 10.3390/brainsci13071051.
Non-cardioembolic ischemic stroke (IS) is the predominant subtype of IS. This study aimed to construct a nomogram for recurrence risks in patients with non-cardioembolic IS in order to maximize clinical benefits. From April 2015 to December 2019, data from consecutive patients who were diagnosed with non-cardioembolic IS were collected from Lanzhou University Second Hospital. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection. Multivariable Cox regression analyses were used to identify the independent risk factors. A nomogram model was constructed using the "rms" package in R software via multifactor Cox regression. The accuracy of the model was evaluated using the receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA). A total of 729 non-cardioembolic IS patients were enrolled, including 498 (68.3%) male patients and 231 (31.7%) female patients. Among them, there were 137 patients (18.8%) with recurrence. The patients were randomly divided into training and testing sets. The Kaplan-Meier survival analysis of the training and testing sets consistently revealed that the recurrence rates in the high-risk group were significantly higher than those in the low-risk group ( < 0.01). Moreover, the receiver operating characteristic curve analysis of the risk score demonstrated that the area under the curve was 0.778 and 0.760 in the training and testing sets, respectively. The nomogram comprised independent risk factors, including age, diabetes, platelet-lymphocyte ratio, leukoencephalopathy, neutrophil, monocytes, total protein, platelet, albumin, indirect bilirubin, and high-density lipoprotein. The C-index of the nomogram was 0.752 (95% CI: 0.7050.799) in the training set and 0.749 (95% CI: 0.6630.835) in the testing set. The nomogram model can be used as an effective tool for carrying out individualized recurrence predictions for non-cardioembolic IS.
非心源性缺血性卒中(IS)是IS的主要亚型。本研究旨在构建非心源性IS患者复发风险的列线图,以实现临床效益最大化。2015年4月至2019年12月,从兰州大学第二医院收集连续诊断为非心源性IS患者的数据。采用最小绝对收缩和选择算子(LASSO)回归分析优化变量选择。多变量Cox回归分析用于识别独立危险因素。通过R软件中的“rms”包,经多因素Cox回归构建列线图模型。使用受试者工作特征(ROC)、校准曲线和决策曲线分析(DCA)评估模型的准确性。共纳入729例非心源性IS患者,其中男性患者498例(68.3%),女性患者231例(31.7%)。其中,有137例患者(18.8%)复发。将患者随机分为训练集和测试集。训练集和测试集的Kaplan-Meier生存分析一致显示,高风险组的复发率显著高于低风险组(<0.01)。此外,风险评分的受试者工作特征曲线分析表明,训练集和测试集的曲线下面积分别为0.778和0.760。列线图包括年龄、糖尿病、血小板淋巴细胞比值、脑白质病变、中性粒细胞、单核细胞、总蛋白、血小板、白蛋白、间接胆红素和高密度脂蛋白等独立危险因素。列线图在训练集的C指数为0.752(95%CI:0.7050.799),在测试集为0.749(95%CI:0.6630.835)。该列线图模型可作为对非心源性IS进行个体化复发预测的有效工具。