Zhou Yang, Luo Yufan, Liang Huazheng, Wei Zhenyu, Ye Xiaofei, Zhong Ping, Wu Danhong
Emergency Department, Shaoxing People's Hospital, Shaoxing, Zhejiang, China.
Department of Neurology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Front Neurol. 2024 Aug 21;15:1433010. doi: 10.3389/fneur.2024.1433010. eCollection 2024.
The present study aimed to develop a reliable and straightforward Nomogram by integrating various parameters to accurately predict the likelihood of early neurological deterioration (END) in patients with acute ischemic stroke (AIS).
Acute ischemic stroke patients from Shaoxing People's Hospital, Shanghai Yangpu District Shidong Hospital, and Shanghai Fifth People's Hospital were recruited based on specific inclusion and exclusion criteria. The primary outcome was END. Using the LASSO logistic model, a predictive Nomogram was generated. The performance of the Nomogram was evaluated using the ROC curve, the Hosmer-Lemeshow test, and a calibration plot. Additionally, the decision curve analysis was conducted to assess the effectiveness of the Nomogram.
It was found that the Nomogram generated in the present study showed strong discriminatory performance in both the training and the internal validation cohorts when their ROC-AUC values were 0.715 (95% CI 0.648-0.782) and 0.725 (95% CI 0.631-0.820), respectively. Similar results were observed in two external validation cohorts when their ROC-AUC values were 0.685 (95% CI 0.541-0.829) and 0.673 (95% CI 0.545-0.800), respectively. In addition, CAD, SBP, neutrophils, TBil, and LDL were found to be positively correlated with the occurrence of END post-stroke, while lymphocytes and UA were negatively correlated.
Our study developed a novel Nomogram that includes CAD, SBP, neutrophils, lymphocytes, TBil, UA, and LDL and it demonstrated strong discriminatory performance in identifying AIS patients who are likely to develop END.
本研究旨在通过整合各种参数开发一种可靠且简便的列线图，以准确预测急性缺血性卒中（AIS）患者早期神经功能恶化（END）的可能性。
根据特定的纳入和排除标准，招募了来自绍兴市人民医院、上海市杨浦区市东医院和上海市第五人民医院的急性缺血性卒中患者。主要结局为END。使用LASSO逻辑回归模型生成预测列线图。采用受试者工作特征（ROC）曲线、Hosmer-Lemeshow检验和校准图评估列线图的性能。此外，进行决策曲线分析以评估列线图的有效性。
研究发现，本研究生成的列线图在训练队列和内部验证队列中均表现出较强的区分性能，其ROC曲线下面积（AUC）值分别为0.715（95%可信区间0.648-0.782）和0.725（95%可信区间0.631-0.820）。在两个外部验证队列中也观察到类似结果，其ROC-AUC值分别为0.685（95%可信区间0.541-0.829）和0.673（95%可信区间0.545-0.800）。此外，发现冠心病（CAD）、收缩压（SBP）、中性粒细胞、总胆红素（TBil）和低密度脂蛋白（LDL）与卒中后END的发生呈正相关，而淋巴细胞和尿酸（UA）呈负相关。
我们的研究开发了一种新型列线图，其包含CAD、SBP、中性粒细胞、淋巴细胞、TBil、UA和LDL，在识别可能发生END的AIS患者方面表现出较强的区分性能。
