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 nonsevere 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. Univariate and multivariate stepwise forward logistic regression analyses were used to statistically assess data and establish the prediction model. The prediction model's effectiveness was evaluated via 10-fold cross-validation of the validation group to facilitate the construction of a nomogram. Results: After the screening, 479 patients were included in the study: 132 (27.6%) in the postoperative severe ALI group and 347 (72.4%) in the postoperative nonsevere ALI group. Based on multivariate logistics regression analyses, the following variables were included in the model: coronary heart disease, cardiopulmonary bypass (CPB) ≥ 257.5 min, left atrium diameter ≥ 35.5 mm, hemoglobin ≤ 139.5 g/L, preCPB OI ≤ 100 mmHg, intensive care unit OI ≤ 100 mmHg, left ventricular posterior wall thickness ≥ 10.5 mm, and neutrophilic granulocyte percentage ≥ 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 (χ 2 = 6.037, df = 8, p = .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 2 = 3.3782, df = 7; p = .848). The average 10-fold crossvalidation 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.

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.

BackgroundAcute type A aortic dissection (ATAAD) is a rare, life-threatening condition affecting the aorta. This study explores the relationship between the level of admission D-dimer, which was assessed during the first 2 h from admission, and in-hospital major adverse events (MAE) with ATAAD.MethodsA total of 470 patients with enhanced computed tomography (CT) confirmed diagnosis of ATAAD who underwent operation treatment in Guangdong Provincial People's hospital between September 2017 and June 2021 were enrolled in the present study. The X-tile program was used to determine the optimal D-dimer thresholds for risk. Restricted cubic spline (RSC) was performed to assess the association between D-dimer and endpoint. The perioperative data were compared between the two groups, univariate and multivariate analyses were used to investigate the risk factors of major adverse events (in-hospital mortality, gastrointestinal bleeding, paraplegia, acute kidney failure, reopen the chest, low cardiac output syndrome, cerebrovascular accident, respiratory insufficiency, MODS, gastrointestinal bleeding, and severe infection).ResultsAmong 470 patients, 151 (32.1%) had MAE. In-hospital mortality was 7.44%. The patients with D-dimer >14,500 ng/ml were more likely to present with acute kidney failure, low cardiac output, cerebrovascular accident, multiple organ dysfunction syndromes (MODS), gastrointestinal bleeding, and severe infection. D-dimer level was an independent risk factor for acute kidney failure (OR 2.09, 95% CI: 1.25–3.51, p = 0.005), MODS (OR 6.40, 95% CI: 1.23–33.39, p = 0.028), gastrointestinal bleeding (OR 17.76, 95% CI: 1.99–158.78, p = 0.010) and mortality (OR 3.17, 95% CI: 1.32–7.63, p = 0.010). Multivariate regression analysis of adverse events also suggested that D-dimer >14,500 ng/ml (OR 1.68, 95% CI: 1.09–2.61, p = 0.020) was the independent risk factor of major adverse events.ConclusionsIncreasing D-dimer levels were independently associated with the in-hospital MAE and thus can be used as a useful prognostic biomarker before the surgery.