In this work, we deal with temporal abstraction of clinical data. Abstractions are, for example, blood pressure state (e.g. normal, high, low) and trend (e.g. increasing, decreasing and stationary) over time intervals. The goal of our work is to provide clinicians with automatic tools to extract high-level, concise, important features of available collections of time-stamped clinical data. This capability is especially important when the available collections constantly increase in size, as in long-term clinical follow-up, leading to information overload. The approach we propose exploits the integration of the deductive and object-oriented approaches in clinical databases. The main result of this work is an object-oriented data model based on the event calculus to support temporal abstraction. The proposed approach has been validated building the CARDIOTABS system for the abstraction of clinical data collected during echocardiographic tests.