Early Prediction Model of Acute Aortic Syndrome Mortality in Emergency Departments

Daidai Wang,1,&ast; Hua Zhang,2,&ast; Lanfang Du,1,&ast; Qiangrong Zhai,1 Guangliang Hu,1 Wei Gao,1 Anyi Zhang,1 Sa Wang,1 Yajuan Hao,1 Kaijian Shang,1,3 Xueqing Liu,1 Yanxia Gao,4 Nijiati Muyesai,5 Qingbian Ma1 1Department of Emergency Medicine, Peking University Third Hospital, Beijing, People’s Republic of China; 2Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, People’s Republic of China; 3Department of Emergency Medicine, Second hospital of Shanxi Medical University, Shanxi, People’s Republic of China; 4Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of China; 5Department of Emergency Medicine, Xinjiang Ulger Municipal People’s Hospital, Urumqi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qingbian Ma; Nijiati Muyesai, Tel +86 15611908229, Email maqingbian@126.com; muyassar11@aliyun.comPurpose: Acute aortic syndrome is a constellation of life-threatening medical conditions for which rapid assessment and targeted intervention are important for the prognosis of patients who are at high risk of in-hospital death. The current study aims to develop and externally validate an early prediction mortality model that can be used to identify high-risk patients with acute aortic syndrome in the emergency department.Patients and Methods: This retrospective multi-center observational study enrolled 1088 patients with acute aortic syndrome admitted to the emergency departments of two hospitals in China between January 2017 and March 2021 for model development. A total of 210 patients with acute aortic syndrome admitted to the emergency departments of Peking University Third Hospital between January 2007 and December 2021 was enrolled for model validation. Demographics and clinical factors were collected at the time of emergency department admission. The predictive variables were determined by referring to the results of previous studies and the baseline analysis of this study. The study’s endpoint was in-hospital death. To assess internal validity, we used a fivefold cross-validation method. Model performance was validated internally and externally by evaluating model discrimination using the area under the receiver-operating characteristic curve (AUC). A nomogram was developed based on the binary regression results.Results: In the development cohort, 1088 patients with acute aortic syndromes were included, and 88 (8.1%) patients died during hospitalization. In the validation cohort, 210 patients were included, and 20 (9.5%) patients died during hospitalization. The final model included the following variables: digestive system symptoms (OR=2.25; P=0.024), any pulse deficit (OR=7.78; P< 0.001), creatinine (μmol/L)(OR=1.00; P=0.018), lesion extension to iliac vessels (OR=4.49; P< 0.001), pericardial effusion (OR=2.67; P=0.008), and Stanford type A (OR=10.46; P< 0.001). The model’s AUC was 0.838 (95% CI 0.784– 0.892) in the development cohort and 0.821 (95% CI 0.750– 0.891) in the validation cohort, and the Hosmer–Lemeshow test showed p=0.597. The fivefold cross-validation demonstrated a mean accuracy of 0.94, a mean precision of 0.67, and a mean recall of 0.13.Conclusion: This risk prediction tool uses simple variables to provide robust prediction of the risk of in-hospital death from acute aortic syndrome and validated well in an independent cohort. The tool can help emergency clinicians quickly identify high-risk acute aortic syndrome patients, although further studies are needed for verifying the prospective data and the results of our study.Keywords: acute aortic syndrome, risk prediction model, emergency department