Departments of Cardiovascular Surgery, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Departments of Cardiovascular Surgery, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Int J Surg. 2018 Dec;60:266-272. doi: 10.1016/j.ijsu.2018.11.024. Epub 2018 Nov 26.
The study aims to build and validate a nomogram for estimating the probability of patients developing type A aortic dissection at a diameter less than 55 mm.
A primary cohort of 896 patients diagnosed with acute type A aortic dissection by computed tomography angiography (CTA) were used for model development, with data collected between January 2005 and March 2012. The subjects were assigned to two groups based on ascending aorta diameter (group A<55 mm, Group B ≥ 60 mm). Univariate and multivariate logistic regression analyses were employed for the development of the prediction model. Demographic factors, as well as clinical and imaging characteristics were taken into account. The resulting nomogram was evaluated for performance traits, e.g. calibration, discrimination and clinical usefulness. After internal validation, the nomogram was further assessed in a different cohort containing 385 consecutive subjects examined between January 2013 and December 2015.
The individualized prediction nomogram included 9 predictors derived from univariate and multivariable analyses, including gender, age, weight, hypertension, liver cyst, renal cyst, bicuspid aortic valve, and bovine arch. Those predictors were double confirmed with Lasso regression. Internal validation showed good discrimination of the model with area under the curve (AUC) of 0.854 and good calibration (Hosmer-Lemeshow test, P = 0.876). Application of the nomogram in the validation cohort still revealed good discrimination (AUC = 0.802) and good calibration (Hosmer-Lemeshow test, P = 0.398). Decision curve analysis demonstrated that the prediction nomogram was clinically useful.
The current work presents a prediction nomogram incorporating demographical data as well as clinical and imaging characteristics that could help identify patients who might develop type A aortic dissection at a diameter less than 55 mm with convenience.
本研究旨在建立并验证一个列线图模型，用于预测直径小于 55mm 的急性 A 型主动脉夹层患者发生类型 A 主动脉夹层的概率。
使用 896 例经计算机断层扫描血管造影（CTA）诊断为急性 A 型主动脉夹层的患者的原始队列进行模型开发，数据收集时间为 2005 年 1 月至 2012 年 3 月。根据升主动脉直径将受试者分为两组（A 组<55mm，B 组≥60mm）。采用单变量和多变量逻辑回归分析来建立预测模型。考虑了人口统计学因素以及临床和影像学特征。评估了生成的列线图的性能特征，例如校准、区分度和临床实用性。内部验证后，该列线图在另一个队列中进行了进一步评估，该队列包含 2013 年 1 月至 2015 年 12 月期间连续检查的 385 例连续患者。
个体化预测列线图包括 9 个由单变量和多变量分析得出的预测因子，包括性别、年龄、体重、高血压、肝囊肿、肾囊肿、二叶式主动脉瓣和牛型主动脉弓。这些预测因子通过 Lasso 回归进行了双重确认。内部验证显示该模型具有良好的区分度（曲线下面积[AUC]为 0.854）和良好的校准度（Hosmer-Lemeshow 检验，P=0.876）。该列线图在验证队列中的应用仍显示出良好的区分度（AUC=0.802）和良好的校准度（Hosmer-Lemeshow 检验，P=0.398）。决策曲线分析表明，该预测列线图具有临床实用性。
本研究提出了一个包含人口统计学数据以及临床和影像学特征的预测列线图，可方便地帮助识别可能发生直径小于 55mm 的 A 型主动脉夹层的患者。
