Objective: This study aimed to establish a risk assessment model to predict postoperative severe acute lung injury (ALI) risk in patients with acute type A aortic dissection (ATAAD). Methods: Consecutive patients with ATAAD admitted to our hospital were included in this retrospective assessment and placed in the postoperative severe ALI and non-severe ALI groups based on the presence or absence of ALI within 72 h postoperatively (oxygen index (OI) ≤100 mmHg). Patients were then randomly divided into training and validation groups in a ratio of 8:2. Logistic regression analyses were used to statistically assess data and establish the prediction model. The prediction model’s effectiveness was evaluated via tenfold cross-validation of the validation group to facilitate construction of a nomogram. Results: After screening, 479 patients were included in the study: 132 (27.5%) in the postoperative severe ALI group and 347 (72.5%) in the postoperative non-severe ALI group. Based on logistics regression analyses, the following variables were included in the model: coronary heart disease (CHD), cardiopulmonary bypass (CPB) ≥257.5 min, left atrium (LA) diameter ≥35.5 mm, hemoglobin ≤139.5 g/L, preCPB OI ≤100 mmHg, intensive care unit (ICU) OI ≤100 mmHg, left ventricular posterior wall thickness (LVPWT) ≥10.5 mm, and neutrophilic granulocyte percentage (NEUT) ≥0.824. The area under the receiver operating characteristic (ROC) curve of the modeling group was 0.805, and differences between observed and predicted values were not deemed statistically significant via the Hosmer–Lemeshow test (χ2=6.037, df=8, P=0.643). For the validation group, the area under the ROC curve was 0.778, and observed and predicted value differences were insignificant when assessed using the Hosmer–Lemeshow test (χ =3.3782, df=7; P=0.848). The average tenfold cross-validation score was 0.756. Conclusions: This study established a prediction model and developed a nomogram to determine the risk of postoperative severe ALI after ATAAD. Variables used in the model were easy to obtain clinically and the effectiveness of the model was good.

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OBJECTIVES Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A.METHODS A total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model.RESULTS Among the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ2 = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8,448 (95% CI, 0.8,007-0.8,888). The validation dataset had 168 patients, calibration was significant (χ2 = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8,086 (95% CI, 0.7,291-0.8,881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007) . Our data suggest that a simple and effective preoperative death risk assessment model has been established.CONCLUSIONS Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.

BACKGROUNDAcute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations, laboratory tests, and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A.METHODSA total of 673 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. The patients included were divided into the survivor and deceased groups, and the endpoint event was preoperative death. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model.RESULTSAmong the 673 patients, 527 patients survived (78.31%) and 146 patients died (21.69%). The developmental dataset had 505 patients, calibration by Hosmer Lemeshow was significant (χ2 = 3.260, df = 8, P = 0.917) and discrimination by area under ROC curve was 0.8448 (95% CI, 0.8007-0.8888). The validation dataset had 168 patients, calibration was significant (χ2 = 5.500, df = 8, P = 0.703) and the area under the ROC curve was 0.8086 (95% CI, 0.7291-0.8881). The following independent variables increased preoperative mortality: age (OR = 1.008, P = 0.510), abrupt chest pain (OR = 3.534, P < 0.001), lactic in arterial blood gas ≥ 3 mmol/L (OR = 3.636, P < 0.001), inotropic support (OR = 8.615, P < 0.001), electrocardiographic myocardial ischemia (OR = 3.300, P = 0.001), innominate artery involvement (OR = 1.625, P = 0.104), right common carotid artery involvement (OR = 3.487, P = 0.001), superior mesenteric artery involvement (OR = 2.651, P = 0.001), false lumen / true lumen of ascending aorta ≥ 0.75 (OR = 2.221, P = 0.007) . Our data suggest that a simple and effective preoperative death risk assessment model has been established.CONCLUSIONSUsing a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.

OBJECTIVES Acute aortic dissection type A is a life-threatening disease required emergency surgery during acute phase. Different clinical manifestations: laboratory tests: and imaging features of patients with acute aortic dissection type A are the risk factors of preoperative mortality. This study aims to establish a simple and effective preoperative mortality risk assessment model for patients with acute aortic dissection type A. METHODS A total of 508 Chinese patients with acute aortic dissection type A who were admitted to our hospital were retrospectively included. All patients were unable to receive surgically treatment within 3 days from the onset of disease. Multivariable analysis was used to investigate predictors of preoperative mortality and to develop a prediction model. RESULTS Among the 508 patients: 394 patients survived (77.56%) and 114 patients died (22.44%). The following independent variables increased preoperative mortality: initial pain site: chest (OR = 7.536: P = 0.021): D-Dimmer ≥ 12000 ng/ml (OR = 2.982: P < 0.001): the average ascending diameter measured by transthoracic echocardiography ≥ 55 mm (OR = 4.226: P < 0.001): moderate or massive pericardial effusion (OR = 2.534: P = 0.040): electrocardiographic myocardial ischemia (OR = 3.355: P < 0.001): patent false lumen (OR = 2.808: P < 0.001): right common carotid artery involvement (OR = 4.415: P < 0.001): false lumen /true lumen of abdominal aorta ≥ 0.75 (OR = 2.310: P = 0.011). Our data suggest that a simple and effective preoperative death risk assessment model has been established. CONCLUSIONS Using a simple and effective risk assessment model can help clinicians quickly identify high-risk patients and make appropriate medical decisions.

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