Zhang Cheng, Zhang Wenli, Huang Ying, Qiu Jianxiang, Huang Zhi-Xin
Department of Neurology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, People's Republic of China.
The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Risk Manag Healthc Policy. 2022 May 5;15:923-934. doi: 10.2147/RMHP.S361073. eCollection 2022.
Despite receiving standard-of-care treatments, a significant proportion of patients with acute ischemic stroke (AIS) are left with long-term functional impairment. Therefore, an easy-to-use tool for predicting of unfavorable outcome following AIS plays an important role in clinical practice. This study was aimed to develop a dynamic nomogram to predict the 3-month unfavorable outcome for AIS patients.
This was a prospective observational study conducted in consecutive patients with AIS admitted to our stroke center between September 2019 and June 2020. Baseline demographic, clinical, and laboratory information were obtained. The primary outcome was evaluated with modified Rankin Scale (mRS) scores at 3 months. Least absolute shrinkage and selection operator regression was used to select the optimal predictive factors. Multiple logistics regression was performed to establish the nomogram. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. The calibration and discrimination property of the nomogram was validated by calibration plots and concordance index.
A total of 93 eligible patients were enrolled: 28 (30.1%) patients had unfavorable outcome (mRS >2). Glycosylated hemoglobin (OR, 1.541; 95% CI, 1.051-2.261), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.635; 95% CI, 0.463-0.871), and National Institute of Health Stroke Scale (NIHSS) (OR 1.484; 95% CI, 1.155-1.907) were significant predictors of the poor outcome of patients with AIS and included into the nomogram model. The nomogram showed good calibration and discrimination. C-index was 0.891 (95% CI, 0.854-0.928). DCA confirmed the clinical usefulness of the model. The dynamic nomogram can be obtained at the website: https://odywong.shinyapps.io/DBT_21/.
The dynamic nomogram, comprised of glycosylated hemoglobin, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable outcome for AIS patients.
尽管接受了标准治疗，但仍有相当一部分急性缺血性卒中（AIS）患者遗留长期功能障碍。因此，一种易于使用的预测AIS后不良结局的工具在临床实践中具有重要作用。本研究旨在开发一种动态列线图，以预测AIS患者3个月时的不良结局。
这是一项前瞻性观察性研究，对2019年9月至2020年6月期间连续入住我院卒中中心的AIS患者进行研究。获取了基线人口统计学、临床和实验室信息。主要结局采用3个月时的改良Rankin量表（mRS）评分进行评估。使用最小绝对收缩和选择算子回归来选择最佳预测因素。进行多因素逻辑回归以建立列线图。应用决策曲线分析（DCA）评估列线图的临床实用性。通过校准图和一致性指数验证列线图的校准和区分性能。
共纳入93例符合条件的患者：28例（30.1%）患者预后不良（mRS>2）。糖化血红蛋白（OR，1.541；95%CI，1.051 - 2.261）、阿尔伯塔卒中项目早期计算机断层扫描评分（ASPECTS）（OR，0.635；95%CI，0.463 - 0.871）和美国国立卫生研究院卒中量表（NIHSS）（OR 1.484；95%CI，1.155 - 1.907）是AIS患者预后不良的显著预测因素，并被纳入列线图模型。列线图显示出良好的校准和区分能力。C指数为0.891（95%CI，0.854 - 0.928）。DCA证实了该模型的临床实用性。动态列线图可在网站：https://odywong.shinyapps.io/DBT_21/获取。
由糖化血红蛋白、ASPECTS和第14天的NIHSS评分组成的动态列线图可能能够预测AIS患者3个月时的不良结局。
