This paper reported a 75-year-old female patient. She was admitted to our hospital for “repeated chest pain, shortness of breath for more than 5 years, and syncope 3 times”. The CT scan of the patient showed severe aortic valve stenosis, bicuspid valve, and severe calcification; then she underwent transcatheter aortic valve replacement in our hospital. After the prosthesis was implanted, there was a significant paravalvular leak. Considering the triangular area formed between the calcified clumps, the valve was not fully dilated. Paravalvular leak closure was performed during the operation, attempted through the valve stent mesh to closuring. A rare incarceration of the transmitter occurred. An attempt was made to pull out the incarcerated transmitter through a pull-up technique, which resulted in the prosthesis prolapse. The patient was eventually transferred to surgery aortic valve replacement.
Nowadays, aortic bioprostheses are used more and more widely in clinical practice, but the valve will experience structural valve degradation over time, and eventually lose its function, which is valve failure. Valve failure has become a significant challenge for aortic valve replacement and especially limits the expansion of indications for transcatheter aortic valve replacement. This review focuses on the current status and relevant evidence on the definition, risk factors, epidemiological characteristics, diagnosis and evaluation, treatment strategies of aortic bioprostheses failure. The purpose is to provide a basis for a more comprehensive understanding of aortic bioprostheses failure, finding better coping strategies and further improving the long-term durability of the valve.
With the increasing availability of clinical and biomedical big data, machine learning is being widely used in scientific research and academic papers. It integrates various types of information to predict individual health outcomes. However, deficiencies in reporting key information have gradually emerged. These include issues like data bias, model fairness across different groups, and problems with data quality and applicability. Maintaining predictive accuracy and interpretability in real-world clinical settings is also a challenge. This increases the complexity of safely and effectively applying predictive models to clinical practice. To address these problems, TRIPOD+AI (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence) introduces a reporting standard for machine learning models. It is based on TRIPOD and aims to improve transparency, reproducibility, and health equity. These improvements enhance the quality of machine learning model applications. Currently, research on prediction models based on machine learning is rapidly increasing. To help domestic readers better understand and apply TRIPOD+AI, we provide examples and interpretations. We hope this will support researchers in improving the quality of their reports.