Stanford University School of Medicine, Stanford, CA.
Stanford and Mayo Clinic Hospital, Rochester, MN.
J Thorac Imaging. 2023 Sep 1;38(5):270-277. doi: 10.1097/RTI.0000000000000702. Epub 2023 Mar 6.
Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT.
A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib.
Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53 cm 2 , P <0.000001; 4.34 vs. 3.5 cm, P <0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30 cm 2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5 cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743.
LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm 2 and >4.5 cm, respectively, are specific indicators to predict patients at increased risk for AFib.
胸部计算机断层扫描（CT）的定量生物标志物有助于偶然发现重要疾病。心房颤动（AFib）会大大增加合并症的风险，包括中风。本研究探讨了 AFib 状态与 CT 上左心房扩大（LAE）之间的关系。
共纳入 500 例接受非门控胸部 CT 检查的连续患者，测量左心房最大轴向横截面积（LA-MACSA）、左心房前后径（LA-AP）和椎体横截面积（VB-Area）。从病历中获取身高、体重、年龄、性别和 AFib 诊断。进行参数统计分析和接收者操作特征曲线分析。使用临床危险因素和 LA 测量值运行机器学习分类器，以预测 AFib 患者。
确定了 85 例 AFib 诊断患者。与无 AFib 患者相比，AFib 患者的 LA-MACSA 和 LA-AP 平均值明显更大（28.63 与 20.53cm 2 ，P<0.000001；4.34 与 3.5cm，P<0.000001），曲线下面积（AUC）均为 0.73。包括年龄、性别和 VB-Area 在内的多变量逻辑回归分析与 LA-MACSA 一起提高了预测 AFib 的 AUC（AUC=0.77）。LA-MACSA 阈值为 30cm 2 时，AFib 诊断的特异性为 92%，敏感性为 48%，LA-AP 阈值为 4.5cm 时，特异性为 90%，敏感性为 42%。使用年龄、性别、身高、体表面积和 LA-MACSA 的贝叶斯机器学习模型预测 AFib 的 AUC 为 0.743。
LA-MACSA 或 LA-AP 可从常规胸部 CT 快速测量，当分别>30cm 2 和>4.5cm 时，是预测 AFib 风险增加患者的特异性指标。
