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Development and Validation of a Diagnostic Model for Stanford Type B Aortic Dissection Based on Proteomic Profiling
Zihe Zhao1, Taicai Chen2,3, Qingyuan Liu4
1Department of Vascular Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.
Journal of Inflammation Research
|January 16, 2025
Summary
A new diagnostic model for Stanford Type B Aortic Dissection (TBAD) uses serum biomarkers and clinical indicators. This blood-based approach shows promise for early TBAD detection during routine check-ups.
Area of Science:
- Biochemistry
- Genomics
- Medical Diagnostics
Background:
- Stanford Type B Aortic Dissection (TBAD) presents a significant challenge in cardiovascular medicine.
- Current diagnostic methods for TBAD lack integration into routine health screenings.
- Stable mortality rates highlight the need for improved early detection strategies.
Purpose of the Study:
- To develop and validate a diagnostic model for TBAD.
- To identify novel serum biomarkers for TBAD detection.
- To integrate biomarkers with clinical indicators for enhanced diagnostic accuracy.
Main Methods:
- Proteomic profiling and machine learning were employed to identify differentially expressed proteins (DEPs) in serum.
- Enzyme-linked immunosorbent assay (ELISA) was used for biomarker validation.
- Machine learning models were constructed using identified biomarkers and clinical indicators.
Main Results:
- Six DEPs were identified, with five (GDF-15, IL6, CD58, LY9, Siglec-7) validated as potential TBAD biomarkers.
- Machine learning models integrating biomarkers and clinical indicators achieved high diagnostic performance (AUC up to 0.909).
- Combined models demonstrated superior efficacy compared to individual biomarker or clinical indicator models.
Conclusions:
- Serum biomarkers IL-6, GDF-15, CD58, LY9, and Siglec-7 show potential for TBAD diagnosis.
- A machine learning-based diagnostic model utilizing blood samples offers a promising approach for TBAD detection.
