Wu Chunxiao, Xu Zhirui, Wang Qizhang, Zhu Shuping, Li Mengzhu, Tang Chunzhi
Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China.
Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Front Aging Neurosci. 2023 Aug 7;15:1200810. doi: 10.3389/fnagi.2023.1200810. eCollection 2023.
Stroke is the second leading cause of death worldwide and a major cause of long-term neurological disability, imposing an enormous financial burden on families and society. This study aimed to identify the predictors in stroke patients and construct a nomogram prediction model based on these predictors.
This retrospective study included 11,435 participants aged >20 years who were selected from the NHANES 2011-2018. Randomly selected subjects ( = 8531; 75%) and the remaining subjects comprised the development and validation groups, respectively. The least absolute shrinkage and selection operator (LASSO) binomial and logistic regression models were used to select the optimal predictive variables. The stroke probability was calculated using a predictor-based nomogram. Nomogram performance was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve with 1000 bootstrap resample validations. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram.
According to the minimum criteria of non-zero coefficients of Lasso and logistic regression screening, older age, lower education level, lower family income, hypertension, depression status, diabetes, heavy smoking, heavy drinking, trouble sleeping, congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris and myocardial infarction were independently associated with a higher stroke risk. A nomogram model for stroke patient risk was established based on these predictors. The AUC (C statistic) of the nomogram was 0.843 (95% CI: 0.8186-0.8430) in the development group and 0.826 (95% CI: 0.7811, 0.8716) in the validation group. The calibration curves after 1000 bootstraps displayed a good fit between the actual and predicted probabilities in both the development and validation groups. DCA showed that the model in the development and validation groups had a net benefit when the risk thresholds were 0-0.2 and 0-0.25, respectively.
This study effectively established a nomogram including demographic characteristics, vascular risk factors, emotional factors and lifestyle behaviors to predict stroke risk. This nomogram is helpful for screening high-risk stroke individuals and could assist physicians in making better treatment decisions to reduce stroke occurrence.
中风是全球第二大死因，也是长期神经功能残疾的主要原因，给家庭和社会带来了巨大的经济负担。本研究旨在确定中风患者的预测因素，并基于这些预测因素构建列线图预测模型。
这项回顾性研究纳入了从2011 - 2018年美国国家健康与营养检查调查（NHANES）中选取的11435名年龄大于20岁的参与者。随机选取的受试者（n = 8531；75%）和其余受试者分别组成开发组和验证组。使用最小绝对收缩和选择算子（LASSO）二项式和逻辑回归模型来选择最佳预测变量。使用基于预测因素的列线图计算中风概率。通过受试者操作特征曲线（AUC）下的面积和进行1000次自抽样验证的校准曲线来评估列线图的性能。进行决策曲线分析（DCA）以评估列线图的临床实用性。
根据Lasso和逻辑回归筛选的非零系数的最低标准，年龄较大、教育水平较低、家庭收入较低、高血压、抑郁状态、糖尿病、大量吸烟、大量饮酒、睡眠障碍、充血性心力衰竭（CHF）、冠心病（CHD）、心绞痛和心肌梗死与较高的中风风险独立相关。基于这些预测因素建立了中风患者风险的列线图模型。列线图在开发组中的AUC（C统计量）为0.843（95%CI：0.8186 - 0.8430），在验证组中为0.826（95%CI：0.7811，0.8716）。1000次自抽样后的校准曲线显示，开发组和验证组的实际概率与预测概率之间拟合良好。DCA表明，开发组和验证组的模型在风险阈值分别为0 - 0.2和0 - 0.25时具有净效益。
本研究有效地建立了一个包含人口统计学特征、血管危险因素、情绪因素和生活方式行为的列线图来预测中风风险。该列线图有助于筛查中风高危个体，并可协助医生做出更好的治疗决策以减少中风的发生。
