急性缺血性脑卒中患者早期预后不良及预测模型构建

doi:10.12183/j.scjpm.2023.1213

摘要/Abstract

摘要： 目的探讨急性缺血性脑卒中（AIS）患者早期预后不良的影响因素,并进一步构建风险预测模型。方法于2019年1月至2021年12月选取恩施某三甲综合医院接诊的AIS患者为研究对象,于患者入院6个月后根据改良Rankin量表（mRS）评分对患者早期预后不良情况进行随访评价,根据随访结果将其分为预后良好组和预后不良组,比较2组AIS患者病例特征并利用多因素logistic回归模型分析早期预后不良的影响因素,构建风险预测模型和绘制ROC曲线对模型进行验证评价。结果共纳入AIS患者836例,男515例,女321例,年龄42~89岁,平均年龄（65.37±11.96）岁。卒中部位：颈内动脉227例,大脑中动脉301例,大脑后动脉及其他263例。其中279例AIS患者在入院6个月短期随访内出现预后不良,预后不良率为33.37%。多因素logistic回归分析显示年龄（OR=1.234）、房颤（OR=2.992）、入院NIHSS评分（OR=1.906）、NLR（OR=1.770）、D-dimer（OR=1.614）和UA（OR=0.834）均是AIS患者预后不良的独立影响因素。ROC 曲线下面积（AUC）为0.817（95% CI：0.739~0.934）,当cut-off 值为0.469时,该模型的准确性为85.19%,敏感性为81.43%,特异性为76.13%。结论基于年龄、房颤、入院NIHSS评分、NLR、D-dimer和UA构建的风险预测模型对AIS患者早期预后不良的预测效能较好,且模型稳定,拟合程度好。

关键词: 急性缺血性脑卒中, 早期预后, 影响因素, 预测模型构建

Abstract: Objective To explore the influencing factors of early poor prognosis in patients with acute ischemic stroke （AIS） and further construct a risk prediction model. Methods AIS patients admitted to a tertiary general hospital in Enshi from January 2019 to December 2021 were selected for this study. After 6 months of admission, the patients were followed up and evaluated for early poor prognosis based on the modified Rankin scale （mRS） score. Based on the follow-up results, they were divided into a good prognosis group and a poor prognosis group. The case characteristics of the two groups were compared and the influencing factors for early poor prognosis were analyzed using multivariate logistic regression, and the risk prediction model was constructed and ROC curve was drawn to verify and evaluate the model. Results The results included a total of 836 AIS patients, 515 males and 321 females, aged 42-89 years, with an average age of （65.37±11.96） years. Stroke location： 227 cases of internal carotid artery, 301 cases of middle cerebral artery, 263 cases of posterior cerebral artery and others. Among them, 279 AIS patients had poor prognosis during a short-term follow-up of 6 months after admission, with a poor prognosis rate of 33.37%. Multivariate logistic regression analysis showed that age （OR=1.234）, atrial fibrillation （OR=2.992）, admission NIHSS score （OR=1.906）, NLR （OR=1.770）, D-dimer （OR=1.614）, and UA （OR=0.834） were independent influencing factors for poor prognosis in AIS patients. The area under the ROC curve （AUC） was 0.817 （95% CI： 0.739-0.934）. When the cut off value was 0.469, the accuracy of the model was 85.19%, sensitivity was 81.43%, and specificity was 76.13%. Conclusion The risk prediction model constructed based on age, atrial fibrillation, admission NIHSS score, NLR, D-dimer, and UA has good predictive efficacy for early poor prognosis in AIS patients, and the model is stable and well fitted.

Key words: Acute ischemic stroke, Early prognosis, Influencing factor, Prediction model construction

中图分类号:

R195

阎纯, 龚媛, 黄平, 陈娟, 黄淼. 急性缺血性脑卒中患者早期预后不良及预测模型构建[J]. 华南预防医学, 2023, 49(10): 1213-1217.

YAN Chun, GONG Yuan, HUANG Ping, CHEN Juan, HUANG Miao. Early poor prognosis and predictive model construction in patients with acute ischemic stroke[J]. South China Journal of Preventive Medicine, 2023, 49(10): 1213-1217.

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