Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling

Abstract

Zihe Zhao,1,* Taicai Chen,2,3,* Qingyuan Liu,4 Jianhang Hu,1 Tong Ling,2,3 Yuanhao Tong,5 Yuexue Han,1 Zhengyang Zhu,6 Jianfeng Duan,7 Yi Jin,1 Dongsheng Fu,1 Yuzhu Wang,1 Chaohui Pan,1 Reyaguli Keyoumu,1 Lili Sun,1 Wendong Li,1 Xia Gao,8,9 Yinghuan Shi,2,3 Huan Dou,10,11 Zhao Liu1 1Department of Vascular Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 2The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China; 3National Institute of Healthcare Data Science, Nanjing University, Nanjing, People’s Republic of China; 4Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People’s Republic of China; 5Department of Thoracic Surgery, BenQ Medical Center, Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 6Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 7Department of Critical Care Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 8Department of Otolaryngology, Head and Neck Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 9Jiangsu Provincial Key Medical Discipline (Laboratory), Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People’s Republic of China; 10The State Key Laboratory of Pharmaceutical Biotechnology, Division of Immunology, Medical School, Nanjing University, Nanjing, People’s Republic of China; 11Jiangsu Key Laboratory of Molecular Medicine, Medical School, Nanjing University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhao Liu, Department of Vascular Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, #321 Zhongshan Road, Nanjing, 210008, People’s Republic of China, Email [email protected] Huan Dou, The State Key Laboratory of Pharmaceutical Biotechnology, Division of Immunology, Medical School, Nanjing University, Nanjing, People’s Republic of China, Email [email protected]: Stanford Type B Aortic Dissection (TBAD), a critical aortic disease, has exhibited stable mortality rates over the past decade. However, diagnostic approaches for TBAD during routine health check-ups are currently lacking. This study focused on developing a model to improve the diagnosis in a population.Patients and Methods: Serum biomarkers were investigated in 88 participants using proteomic profiling combined with machine learning. The findings were validated using ELISA in other 80 participants. Subsequently, a diagnostic model for TBAD integrating biomarkers with clinical indicators was developed and assessed using machine learning.Results: Six differentially expressed proteins (DEPs) were identified through proteomic profiling and machine learning in discovery and derivation cohorts. Five of these (GDF-15, IL6, CD58, LY9, and Siglec-7) were further verified through ELISA validation within the validation cohort. In addition, ten blood-related indicators were selected as clinical indicators. Combining biomarkers and clinical indicators, the machine learning-based models performed well (AUC of the biomarker model = 0.865, AUC of the clinical model = 0.904, and AUC of the combined model = 0.909) using relative quantitation. The performance of the three models was verified (AUC of biomarker model = 0.866, AUC of clinical model = 0.868, and AUC of combined model = 0.886) using absolute quantitation. Crucially, the combined models outperformed individual biomarkers and clinical models, demonstrating superior efficacy.Conclusion: Using proteomic profiling, we identified serum IL-6, GDF-15, CD58, LY9, and Siglec-7 as TBAD biomarkers. The machine-learning-based diagnostic model exhibited significant potential for TBAD diagnosis using only blood samples within the population.Keywords: type B aortic dissection, proteomics, machine learning, serum biomarkers, diagnostic model

Keywords

• type b aortic dissection
• proteomics
• machine learning
• serum biomarkers
• diagnostic model