This study aims to establish a multicenter, prospective, observational registry database for type B aortic dissection based on approximately 60 regional sentinel hospitals across China. It develops a digital intelligent management system, formulates quality control indicators and data collection standards for type B aortic dissection, and optimizes the data quality control and evaluation system to realize comprehensive evaluation covering the entire diagnosis and treatment process and long-term prognosis. Artificial intelligence algorithms are adopted to construct a risk prediction model for adverse events, and auxiliary decision-making software is developed to identify potential risks of adverse events in a timely manner and establish an early warning system, ultimately reducing the in-hospital complication and mortality rates.

Relying on the National Quality Control Platform for Peripheral Vascular Intervention, this study further builds a national quality control platform for type B aortic dissection covering more than 5,000 hospitals nationwide by establishing a three-level quality control system. By integrating health administrative data, quality control data, electronic medical record data and clinical research data, a high-quality, multi-dimensional national quality control platform is constructed to support the sustainable development of quality control work. In addition, a clinical research collaboration network will be established to conduct in-depth evaluation of the national diagnosis and treatment patterns and outcomes of type B aortic dissection. Regular national training and assessment programs will be organized to improve the overall diagnosis and treatment capacity of medical institutions at all levels and reduce regional and institutional disparities in the quality of clinical care for the disease.