The cardiovascular disease is one of the serious and life-threatening diseases in the developed world. One aspect of medical treatment is using drugs with blood pressure reducing or cholesterol lowering functions. Importantly, such treatment needs to be individually tailored and is significantly correlated to the particular conditions of individual patients. However, such pathologies and mechanisms are still only under investigation. Several novel and unique computational methods, called meta-analyses techniques, for formatting and analyzing a wide variety of cardiac datasets are discussed in this paper with the aim to building cardiovascular database and related patient life-cycle management services. In this paper we also present an overview of a second order inference engine underlying the meta-analyses, which yields evidenced-based reasoning that is more likely to better assist decision-making on the effectiveness of cardiovascular treatment than what is available currently. Furthermore, the software architecture and other details of such a medical informatics system tailored to cardiovascular disease are also described. Research and development work on this project yields itself to application to many other areas, such as disease control and prevention in Epidemiology, and dietics. The system can therefore make a profound impact to medical informatics.