Abstract：
OBJECTIVE: Early identification of blunt thoracic aortic injury is vital for preventing subsequent aortic rupture. However, risk factors for blunt thoracic aortic injury remain unclear, and a prediction rule remains to be established. We developed and internally validated a new nomogram-based screening model that allows clinicians to quantify blunt thoracic aortic injury risk.
METHODS: Adult patients (age ≥ 18 years) with blunt injury were selected from a nationwide Japanese database (January 2004-May 2019). Patients were randomly divided into training and test cohorts. A new nomogram-based blunt thoracic aortic injury-screening model was constructed using multivariate logistic regression analysis to quantify the association of potential predictive factors with blunt thoracic aortic injury in the training cohort.
RESULTS: Overall, 305,141 patients (training cohort, n = 152,570; test cohort, n = 152,571) were eligible for analysis. Median patient age was 65 years, and 60.9% were men. Multivariate analysis in the training cohort revealed that 13 factors (positive association: age ≥ 55 years, male sex, high-energy impact, hypotension on hospital arrival, Glasgow Coma Scale score < 9 on hospital arrival, diaphragmatic injuries, hepatic injuries, pulmonary injuries, cardiac injuries, renal injuries, sternum fractures, multiple rib fractures, and pelvic fractures) were significantly associated with blunt thoracic aortic injury and included in the screening model. In the test cohort, the new screening model had an area under the curve of 0.87.
CONCLUSIONS: Our novel nomogram-based screening model aids in the quantitative assessment of blunt thoracic aortic injury risk. This model may improve tailored decision-making for each patient.
METHODS: Adult patients (age ≥ 18 years) with blunt injury were selected from a nationwide Japanese database (January 2004-May 2019). Patients were randomly divided into training and test cohorts. A new nomogram-based blunt thoracic aortic injury-screening model was constructed using multivariate logistic regression analysis to quantify the association of potential predictive factors with blunt thoracic aortic injury in the training cohort.
RESULTS: Overall, 305,141 patients (training cohort, n = 152,570; test cohort, n = 152,571) were eligible for analysis. Median patient age was 65 years, and 60.9% were men. Multivariate analysis in the training cohort revealed that 13 factors (positive association: age ≥ 55 years, male sex, high-energy impact, hypotension on hospital arrival, Glasgow Coma Scale score < 9 on hospital arrival, diaphragmatic injuries, hepatic injuries, pulmonary injuries, cardiac injuries, renal injuries, sternum fractures, multiple rib fractures, and pelvic fractures) were significantly associated with blunt thoracic aortic injury and included in the screening model. In the test cohort, the new screening model had an area under the curve of 0.87.
CONCLUSIONS: Our novel nomogram-based screening model aids in the quantitative assessment of blunt thoracic aortic injury risk. This model may improve tailored decision-making for each patient.
摘要：
目的：早期发现闭合性胸主动脉损伤对预防随后的主动脉破裂至关重要。然而,闭合性胸主动脉损伤的危险因素尚不清楚，和预测规则仍有待建立。我们开发并内部验证了一种新的基于列线图的筛查模型，该模型允许临床医生量化钝性胸主动脉损伤风险。
方法：从日本全国数据库（2004年1月至2019年5月）中选择患有钝性损伤的成年患者（年龄≥18岁）。患者被随机分为训练和测试组。使用多变量逻辑回归分析构建新的基于列线图的钝性胸主动脉损伤筛查模型，以量化训练队列中潜在预测因素与钝性胸主动脉损伤的关联。
结果：总体而言，305,141名患者(培训队列，n=152,570；测试队列，n=152,571)符合分析条件。患者年龄中位数为65岁，60.9%是男性。训练队列中的多变量分析显示13个因素(正相关：年龄≥55岁，男性,高能冲击，到达医院时低血压，到达医院时，格拉斯哥昏迷量表评分<9，膈肌损伤,肝损伤,肺损伤,心脏损伤,肾损伤,胸骨骨折,多发性肋骨骨折,和骨盆骨折）与钝性胸主动脉损伤显着相关，并包括在筛选模型中。在测试队列中，新筛查模型的曲线下面积为0.87.
结论：我们新颖的基于列线图的筛查模型有助于对钝性胸主动脉损伤风险进行定量评估。该模型可以改善针对每个患者的定制决策。
方法：从日本全国数据库（2004年1月至2019年5月）中选择患有钝性损伤的成年患者（年龄≥18岁）。患者被随机分为训练和测试组。使用多变量逻辑回归分析构建新的基于列线图的钝性胸主动脉损伤筛查模型，以量化训练队列中潜在预测因素与钝性胸主动脉损伤的关联。
结果：总体而言，305,141名患者(培训队列，n=152,570；测试队列，n=152,571)符合分析条件。患者年龄中位数为65岁，60.9%是男性。训练队列中的多变量分析显示13个因素(正相关：年龄≥55岁，男性,高能冲击，到达医院时低血压，到达医院时，格拉斯哥昏迷量表评分<9，膈肌损伤,肝损伤,肺损伤,心脏损伤,肾损伤,胸骨骨折,多发性肋骨骨折,和骨盆骨折）与钝性胸主动脉损伤显着相关，并包括在筛选模型中。在测试队列中，新筛查模型的曲线下面积为0.87.
结论：我们新颖的基于列线图的筛查模型有助于对钝性胸主动脉损伤风险进行定量评估。该模型可以改善针对每个患者的定制决策。
