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@#<b>Objective</b> To investigate the computed tomography (CT) features of solitary nodular invasive mucinous lung adenocarcinoma (IMA) in stage IA and establish its prediction model. <b>Methods</b> We included 53 lesions of 53 patients with stage-IA IMA and 141 control lesions of 141 patients with invasive non-mucinous lung adenocarcinoma (NIMA) that were confirmed by surgical pathology in our hospital from January 2017 to December 2019. Univariable analysis was used to compare the demographics and CT signs of the two groups. Multivariable logistic regression analysis was performed to determine the main factors influencing solitary nodular IMA. A risk score prediction model was constructed based on the regression coefficients of the main influencing factors. A receiver operating characteristic (ROC) curve was used to assess the performance of the model. <b>Results</b> The univariable analysis showed significant differences between the two groups in age, largest nodule diameter, tumor-lung interface, lobulation, spiculation, air-bronchogram or vacuole sign, vessel abnormalities (<i>P</i> < 0.05). The spiculation sign was different between the two groups, which was longer and softer in the IMA group while shorter and harder in the NIMA group. There was no significant difference in sex, nodule shape, or pleural retraction (<i>P</i> > 0.05), but irregular shapes were slightly more frequent in the IMA group. The multivariable logistic regression analysis showed that obscure tumor-lung interface (odds ratio (<i>OR</i> = 20.930, <i>P</i> < 0.05), air-bronchogram or vacuole sign (<i>OR</i> = 7.126, <i>P</i> < 0.05), spiculation sign (<i>OR</i> = 4.207, <i>P</i> < 0.05), and vessel abnormalities (<i>OR</i> = 0.147, <i>P</i> < 0.05) were the main influencing factors. The prediction model based on those factors’ regression coefficients had an area under the ROC curve of 0.829 (<i>P</i> < 0.05). <b>Conclusion</b> Compared with those with NIMA, patients with solitary nodular IMA in stage IA were older and more likely to have the CT features of obscure tumor-lung interface, air-bronchogram or vacuole sign, and longer and softer spiculation. Based on the regression coefficients of tumor-lung interface, air-bronchogram or vacuole sign, spiculation, and vessel abnormalities, the risk score prediction model showed good predictive performance for solitary nodular IMA.
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Background:@#Cardiac rupture (CR) is a major lethal complication of acute myocardial infarction (AMI). However, no valid risk score model was found to predict CR after AMI in previous researches. This study aimed to establish a simple model to assess risk of CR after AMI, which could be easily used in a clinical environment.@*Methods:@#This was a retrospective case-control study that included 53 consecutive patients with CR after AMI during a period from January 1, 2010 to December 31, 2017. The controls included 524 patients who were selected randomly from 7932 AMI patients without CR at a 1:10 ratio. Risk factors for CR were identified using univariate analysis and multivariate logistic regression. Risk score model was developed based on multiple regression coefficients. Performance of risk model was evaluated using receiveroperating characteristic (ROC) curves and internal validity was explored using bootstrap analysis.@*Results:@#Among all 7985 AMI patients, 53 (0.67%) had CR (free wall rupture, n=39; ventricular septal rupture, n=14). Hospital mortalities were 92.5% and 4.01% in patients with and without CR (P < 0.001). Independent variables associated with CR included: older age, female gender, higher heart rate at admission, body mass index (BMI) <25 kg/m2, lower left ventricular ejection fraction (LVEF) and no primary percutaneous coronary intervention (pPCI) treatment. In ROC analysis, our CR risk assess model demonstrated a very good discriminate power (area under the curve [AUC]= 0.895, 95% confidence interval: 0.845–0.944, optimism-corrected AUC= 0.821, P < 0.001).@*Conclusion:@#This study developed a novel risk score model to help predict CR after AMI, which had high accuracy and was very simple to use.