## 2 Related Works
### Post-TAVR Complications  
The Post-TAVR Navigator platform is a systematic solution designed to predict post-TAVR complications early and to respond quickly. This platform enables medical professionals to utilize findings from various post-TAVR complication prediction studies in practical and clinical settings.  
Figure 1 illustrates the configuration of the Post-TAVR Navigator platform, which is a systemic solution that facilitates the application of post-TAVR complication-prediction models by medical practitioners. The system operates a platform that predicts three post-TAVR complications: permanent pacemaker implantation, conduction abnormalities, and paravalvular leaks. The platform employs a microservice architecture (MSA) methodology that permits diverse complication-prediction models and components to be seamlessly integrated [32]. Furthermore, novel complication-prediction models can be easily integrated into the platform in the future. Microservices are orchestrated using Kubernetes [33].  
The platform first aggregates patient data, such as real-world data (RWD), examination outcomes, and medical documentation, which are subsequently preprocessed and stored in the RWD repository of the platform. These archived data were used to monitor the patient's condition and forecast the likelihood of complications by employing complication prognostic models.  
Each element of the platform is modularized into a microservice, and each element is constructed as a Docker image. This minimizes the dependencies among componentsand ensures that development and maintenance can be managed effectively for each component [34]. Complication-prediction models are executed as small API server modules utilizing FastAPI, which returns the prediction results when data requests are issued. These modules were containerized using a Docker and operated on the platform. When complication prediction is required, the platform dispatches API requests to the corresponding models and obtains the prediction results. All generated prediction results were archived in the Prediction Result Database, facilitating the continuous monitoring of the patient's complication risk over time.  
Pacemaker Implantation Prediction  
TAVR is an effective intervention for individuals with aortic valve stenosis; however, permanent pacemaker implantation (PPI) is one of the principal complications that may arise after the procedure. PPI is a crucial intervention for sustaining cardiac functionality compromised by conduction anomalies (such as left bundle branch block and atrioventricular block) following the procedure. In instances where PPI is necessary, they exert a considerable influence on a patient's prognostic outlook, rendering it imperative to anticipate this in advance and devise a response strategy before and after the procedure.  
In this study, an XGBoost-based machine learning model was employed to predict patients who would require PPI post-TAVR. This model estimates the likelihood of PPI occurrence by amalgamating diverse clinical data and procedure-related information pertaining to the patient, thus facilitating more precise decision-making by medical practitioners.  
The crux of predicting PPI lies in the ability to discern the potential for conduction disorders in advance, thus enabling medical personnel to respond with alacrity. By identifying patients who are predisposed to PPI early on, the therapeutic plan pre- and post-procedure can be meticulously refined, which is crucial in reducing the incidence of complications after the procedure and enhancing the prognostic outcome of the patient.  
Figure 1: System of the Post-TAVR Navigator system.