Sui He, Wu Jiaojiao, Zhou Qing, Liu Lin, Lv Zhongwen, Zhang Xintan, Yang Haibo, Shen Yi, Liao Shu, Shi Feng, Mo Zhanhao
Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China.
Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Front Neurosci. 2022 Jul 22;16:912287. doi: 10.3389/fnins.2022.912287. eCollection 2022.
Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.
We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.
A total of 476 patients were enrolled in the study and divided into the training set ( = 381) and testing set ( = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)-binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients-the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.
In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781-0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.
The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.
Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.
Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).
中风是一种在全球范围内发病率和死亡率都很高的主要疾病。目前，尚无定量方法来评估患者的短期预后和住院时间。
我们旨在基于非增强计算机断层扫描（NCCT）和CT灌注（CTP）的影像特征以及临床特征，开发列线图作为预后预测指标，以预测缺血性中风患者的日常生活活动能力（ADL）和住院时间。
本研究共纳入476例患者，分为训练集（n = 381）和测试集（n = 95）。每位患者均有NCCT和CTP图像。我们提议从NCCT中提取代表阿尔伯塔中风项目早期CT评分（ASPECTS）值的影像特征，从CBF中提取缺血性病变体积，从CTP中提取TMAX图。基于影像特征和临床特征，我们解决了两个主要问题：（1）根据Barthel指数（BI）预测预后——采用二元逻辑回归分析进行特征选择，并根据区分能力、校准和临床实用性对所得列线图进行评估；（2）预测患者的住院时间——为此使用Cox比例风险模型。经过特征选择后，建立了另一个特定的列线图，并通过校准曲线和时间依赖性ROC曲线进行评估。
在预测二元预后结果的任务中，构建的列线图在测试集中曲线下面积（AUC）值为0.883（95%CI：0.781 - 0.985），准确率为0.853，F1分数为0.909。我们进一步尝试将出院时的BI预测分为四类。在测试集中获得了类似的性能，AUC为0.890。在预测住院时间的任务中，使用了Cox比例风险模型。该模型的一致性指数为0.700（SE = 0.019），预测特定周出院的AUC高于0.80，这表明该模型具有优异的性能。
基于新型非侵入性NCCT和CTP的列线图可以预测缺血性中风患者的短期ADL和住院时间，从而实现个性化的临床结果预测，并在提高临床效率方面显示出巨大潜力。
结合基于NCCT和CTP的列线图可以准确预测缺血性中风患者的短期结果，包括其出院时的BI和住院时间。
使用1310例患者的大型数据集，我们展示了一种在预测患者出院BI类别方面具有良好性能的新型列线图（AUC > 0.850）。第二个列线图具有出色的预测住院时间的能力（AUC > 0.800）。
