Zhang Baowei, Wang Yingying, Guo Junfang, Zhang Guohui, Yang Bing
Center of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Cardiology, the affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
Cardiovasc Diagn Ther. 2021 Apr;11(2):457-466. doi: 10.21037/cdt-20-935.
Aortic dissection (AD) and non-ST segment elevation acute coronary syndrome (ACS) are two of the most life-threatening diseases encountered in the emergency department (ED), but there are no rapid and reliable tools for differentiation. The purpose of this study is to develop and validate a nomogram that incorporates both the clinical characteristics and bedside laboratory tests available to differentiate between AD and non-ST segment elevation ACS (NSTE-ACS).
Between January 2016 and July 2018, patients with AD and NSTE-ACS were enrolled and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was used to select the factors with significant value of predicting the diagnosis of AD. A nomogram was built on the basis of multivariable logistic regression analysis. Area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve were used to assess the performance of the nomogram. Decision curve analysis was performed to assess the clinical utility of the nomogram.
A final cohort of 263 patients (94 patients with AD and 169 patients with NSTE-ACS) were enrolled. Six variables were incorporated in the nomogram: pain severity, tearing pain, pulse asymmetry, electrocardiogram (ECG), D-dimer level and troponin I level. The AUC of the nomogram to predict the probability of AD was 0.919 (95% CI, 0.876-0.962) in the training group and 0.938 (95% CI, 0.888-0.989) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. The decision curve analysis indicated that the nomogram had higher overall net benefits in predicting AD in both the training group and the validation group.
We developed and validated a predictive nomogram that could be used as a tool to differentiate AD from NSTE-ACS rapidly and accurately.
主动脉夹层（AD）和非ST段抬高型急性冠状动脉综合征（ACS）是急诊科遇到的两种最危及生命的疾病，但尚无快速可靠的鉴别工具。本研究的目的是开发并验证一种列线图，该列线图结合临床特征和床边实验室检查结果，以鉴别AD和非ST段抬高型ACS（NSTE-ACS）。
2016年1月至2018年7月，纳入AD和NSTE-ACS患者，并分为训练组和验证组。采用最小绝对收缩和选择算子（LASSO）回归模型选择对AD诊断有显著预测价值的因素。基于多变量逻辑回归分析构建列线图。采用受试者操作特征（ROC）曲线下面积（AUC）和校准曲线评估列线图的性能。进行决策曲线分析以评估列线图的临床实用性。
最终纳入263例患者（94例AD患者和169例NSTE-ACS患者）。列线图纳入了六个变量：疼痛严重程度、撕裂样疼痛、脉搏不对称、心电图（ECG）、D-二聚体水平和肌钙蛋白I水平。训练组中列线图预测AD概率的AUC为0.919（95%CI，0.876-0.962），验证组为0.938（95%CI，0.888-0.989）。校准曲线显示实际临床结果与预测结果之间具有良好的一致性。决策曲线分析表明，列线图在训练组和验证组中预测AD时均具有更高的总体净效益。
我们开发并验证了一种预测列线图，可作为快速、准确鉴别AD与NSTE-ACS的工具。
