Reliability of medical databases for the use of real word data and data mining techniques for cardiovascular diseases progression in diabetic patients

Medical practice is performed based on clinical research, this being commonly based on evaluation of drugs and therapeutic procedures' effect. With the increase of technology and computational storage facilities, usage of Real World Data (RWD) and Big Data Mining (BDM) techniques is proving to be a useful tool for automated data analysis. Entities dealing daily with medical practice such as clinics and hospitals possess databases with a wellspring of information worthwhile being studied to aid clinicians establishing disease patterns identification, future trends, and therapeutic relationships. Aiming at assessing cardiovascular disease (CVD) progression of diabetic patients, a nearly 20 years old private clinic data-base was studied. Primarily goal, and subject of this paper, was the evaluation of the data-base reliability to continue the study of CVD progression. Manual inspection of the database content revealed missing and misleading fields, inconsistency of inputted instrumental data, temporal and user dependency of fields filling, particularly concerning CV data. But, statistics computed on the 20222 diabetic patients' records whose empty entries were eliminated revealed RWD conclusions coherent with published ones.