The risk prediction model (RPM) can be used to predict the risks of disease for individuals, playing an extremely important role in decision-making regarding disease prevention, treatment, and prognostic management. Most of the existing RPMs only utilize a single-time cross-section of variable data, so-called static models, which fail to consider the many changes during disease progression and lead to limited prediction accuracy. Dynamic prediction models can incorporate longitudinal data such as repeated measurements of variables during follow-up to capture the longitudinal changes in individual characteristics over time, describe the dynamic trajectory of individual disease risk and improve the prediction accuracy of the models; however, their application in medical research is still relatively small. In this paper, we conducted a systematic literature search to summarize the commonly used dynamic models: joint model, landmark model, and Bayesian dynamic model. By introducing their application scenarios, advantages and disadvantages, and software implementations and conducting comparisons, we aimed to provide methodological references for the future application of dynamic prediction models in medical research.
The accuracy of the clinical prediction model determines its extrapolation and application value. When the prediction model is applied to a new setting, the differences between the new population and the initially modeled population in terms of study time, population characteristics, region, and other factors could lead to a reduction in its predictive performance. Calibrating or updating the prediction model with appropriate statistical methods is important to improve the accuracy of the prediction model in new populations. The model updating methods mainly include regression coefficients updating, meta-model updating and dynamic model updating. However, due to the limitations of meta-model updating and dynamic model updating in practical applications, the regression coefficient updating method is still the most common method in model updating. This paper introducd several types of model updating methods, the regression coefficients updating methods for two common clinical prediction models based on Logistic regression and Cox regression, and provide corresponding R codes for reference of researchers.
Currently, transcatheter intervention has emerged as a first-line treatment for coarctation of the aortic. Due to the radiation exposure associated with catheter interventional therapy, there are numerous restrictions, which harms both patients and medical personnel and is dependent on sizable radiation apparatus. Here, we report for the first time a case of echo-guiding percutaneous aortic stent implantation for a 27 years female patient of reproductive age. After discharge, the patient's aortic coarctation pressure decreased to 18 mm Hg, and the surgical results were satisfactory.
Currently, transcatheter intervention is the preferred treatment for patients with anatomically suitable atrial septal defects. However, the use of nickel-titanium alloy occluders in interventional procedures results in lifelong presence of the implant in the body, leading to complications such as metal allergies and arrhythmias in some patients. To overcome the short-term and long-term complications associated with the presence of metal, and to avoid radiation exposure and metal toxicity, this paper reports a case of successful transcatheter closure of atrial septal defect in a pediatric patient with metal allergies using fully biodegradable occlude under ultrasound guidance, achieving excellent results by interventional therapy.