TY - JOUR AU - Wu, Jinlin AU - Qiu, Juntao AU - Xie, Enzehua AU - Jiang, Wenxiang AU - Zhao, Rui AU - Qiu, Jiawei AU - Zafar, Mohammad A. AU - Huang, Yan AU - Yu, Cuntao PY - 2019 TI - Predicting in-hospital rupture of type A aortic dissection using Random Forest JF - Journal of Thoracic Disease; Vol 11, No 11 (November 28, 2019): Journal of Thoracic Disease Y2 - 2019 KW - N2 - Background: This study is to establish prediction tools for in-hospital rupture of type A aortic dissection (TAAD) patients, to better guide emergency surgical triage and patient counselling. Methods: We retrospectively evaluated 1,133 consecutive patients with TAAD from January 2010 to December 2016. The study population was divided into training and testing datasets in a 70:30 ratio for further analysis using Random Forest. Results: The Random Forest classification model was developed with the training dataset and 16 variables were confirmed as ‘important’: age, BMI, gender, syncope, lower limb numbness/pain, acute phase of the TAAD, BP >160 mmHg at admission; acute liver dysfunction, WBC >15×10 9 /L, aortic size, aortic height index (AHI), periaortic hematoma, pleural effusion, brachiocephalic artery involvement, renal artery involvement, and hemopericardium. Validation of the model showed good discrimination with an AUC, sensitivity, specificity, positive predictive value and negative predictive value of 0.994, 1.000, 0.987, 0.998 and 1.000, respectively, in the training dataset, and 0.752, 0.990, 0.514, 0.945 and 0.857, respectively, in the testing dataset. Conclusions: An easy-to-use tool to predict in-hospital rupture for TAAD patients was developed and validated (http://47.107.228.109/). Periaortic hematoma is the strongest predictor. Simple clinical information such as syncope can be very useful in in-hospital rupture risk stratification. UR - https://jtd.amegroups.org/article/view/33475