Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns holding in this wealth of data, and thus be helpful in understand- ing the progression of diseases and the efficacy of the as- sociated therapies. A typical structure of medical data is a sequence of ob- servations of clinical parameters taken at different time moments. In this kind of contexts, the temporal dimen- sion of data is a fundamental variable that should be taken in account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well established framework of sequential pattern mining is not enough, because it only focuses on the sequentiality of events, without extracting the typical time elapsing be- tween two particular events. Time-annotated sequences (TAS), is a novel mining paradigm that solves this prob- lem. Recently defined in our laboratory together with an efficient algorithm for extracting them, TAS are sequen- tial patterns where each transition between two events is annotated with a typical transition time that is found fre- quent in the data. In this paper we report a real-world medical case study, in which the TAS mining paradigm is applied to clinical data regarding a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photo- pheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For each patient, a set of biochem- ical variables is recorded at different time moments af- ter the transplantation. The TAS patterns extracted show the values of interleukins and other clinical parameters at specific dates, from which it is possible for the physician to assess the effectiveness of the ECP therapy. We believe that this case study does not only show the interestingness of extracting TAS patterns in this par- ticular context but, more ambitiously, it suggests a gen- eral methodology for clinical data mining, whenever the time dimension is an important variable of the problem in analysis.