Fu Yan, Huang Siyi, Zhao Deyin, Qiu Peng, Hu Jiateng, Liu Xiaobing, Lu Xinwu, Feng Lvfan, Hu Min, Cheng Yong
Department of Nursing, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200011, China.
Second Ward of General Surgery, Suzhou Hospital of Anhui Medical University (Suzhou Municipal Hospital of Anhui Province), Suzhou 234000, China.
Diagnostics (Basel). 2023 Oct 5;13(19):3130. doi: 10.3390/diagnostics13193130.
Many patients with Type B aortic dissection (TBAD) may not show noticeable symptoms until they become intervention and help prevent critically ill, which can result in fatal outcomes. Thus, it is crucial to screen people at high risk of TBAD and initiate the necessary preventive and therapeutic measures before irreversible harm occurs. By developing a prediction model for aortic arch morphology, it is possible to accurately identify those at high risk and take prompt action to prevent the adverse consequences of TBAD. This approach can facilitate timely the development of serious illnesses. The predictive model was established in a primary population consisting of 173 patients diagnosed with acute Stanford TBAD, with data collected from January 2017 and December 2018, as well as 534 patients with healthy aortas, with data collected from April 2018 and December 2018. Explicitly, the data were randomly separated into the derivation set and validation set in a 7:3 ratio. Geometric and anatomical features were extracted from a three-dimensional multiplanar reconstruction of the aortic arch. The LASSO regression model was utilized to minimize the data dimension and choose relevant features. Multivariable logistic regression analysis and backward stepwise selection were employed for predictive model generation, combining demographic and clinical features as well as geometric and anatomical features. The predictive model's performance was evaluated by examining its calibration, discrimination, and clinical benefit. Finally, we also conducted internal verification. After applying LASSO logistic regression and backward stepwise selection, 12 features were entered into the prediction model. Age, aortic arch angle, total thoracic aorta distance, ascending aorta tortuosity, aortic arch tortuosity, distal descending aorta tortuosity, and type III arch were protective factors, while male sex, hypertension, aortic arch height, and aortic arch distance were risk factors. The model exhibited satisfactory discrimination (AUC, 0.917 [95% CI, 0.890-0.945]) and good calibration in the derivation set. Applying the predictive model to the validation set also provided satisfactory discrimination (AUC, 0.909 [95% CI, 0.864-0.953]) and good calibration. The TBAD nomogram for clinical use was established. This study demonstrates that a multivariable logistic regression model can be used to predict TBAD patients.
许多B型主动脉夹层（TBAD）患者在病情发展到需要干预并预防危重症之前可能没有明显症状，而这可能导致致命后果。因此，筛查TBAD高危人群并在不可逆转的损害发生之前采取必要的预防和治疗措施至关重要。通过开发主动脉弓形态预测模型，可以准确识别高危人群并迅速采取行动预防TBAD的不良后果。这种方法可以促进严重疾病的及时诊治。该预测模型是在一个主要人群中建立的，包括173例被诊断为急性斯坦福B型主动脉夹层的患者（数据收集于2017年1月至2018年12月）以及534例主动脉健康的患者（数据收集于2018年4月至2018年12月）。具体而言，数据以7:3的比例随机分为推导集和验证集。从主动脉弓的三维多平面重建中提取几何和解剖特征。利用LASSO回归模型最小化数据维度并选择相关特征。采用多变量逻辑回归分析和向后逐步选择来生成预测模型，将人口统计学和临床特征以及几何和解剖特征相结合。通过检查其校准、区分度和临床益处来评估预测模型的性能。最后，我们还进行了内部验证。在应用LASSO逻辑回归和向后逐步选择后，12个特征被纳入预测模型。年龄、主动脉弓角度、胸主动脉总长度、升主动脉迂曲度、主动脉弓迂曲度、降主动脉远端迂曲度和III型弓是保护因素，而男性、高血压、主动脉弓高度和主动脉弓长度是危险因素。该模型在推导集中表现出令人满意的区分度（AUC，0.917 [95% CI，0.890 - 0.945]）和良好的校准。将预测模型应用于验证集也提供了令人满意的区分度（AUC，0.909 [95% CI，0.864 - 0.953]）和良好的校准。建立了用于临床的TBAD列线图。这项研究表明，多变量逻辑回归模型可用于预测TBAD患者。
