Electronic Medical Records (EMRs) are wealthy storehouses of patient information, to which data mining techniques can be prudently applied to reveal clinically significant patterns. Detecting patterns in drug-drug interactions, leading to adverse drug reactions is a powerful application of EMR data mining. Adverse effects of drug treatments can be investigated by mining clinical laboratory tests data which are reliable indicators of abnormal physiological functions. We report here the co-medication effects of pravastatin (HMG-CoA reductase inhibitor) and paroxetine (selective serotonin reuptake inhibitor (SSRI) anti-depressant) on significant clinical parameters, identified through a data mining analysis conducted on the Allscripts data warehouse. We found that the concomitant drug treatments of pravastatin and paroxetine increased the mean values of glucose serum from 113.2 to 132.1 mg/dL and international normalized ratio (INR) from 2.18 to 2.52, respectively. It also decreased the mean values of estimated glomerular filtration rate (eGFR) from 43 to 37 mL/min/1.73 m(3) and blood CO2 levels from 24.8 to 23.9 mEq/L respectively. Our findings indicate that co-medication of pravastatin and paroxetine might have significant impact on blood anti-coagulation, kidney function, and glucose homeostasis. Our methodology can be applied to any EMR data set to reveal co-medication effects of any drug pairs.

Our study reports an adverse weight gain on co-medication of escitalopram and bupropion, which warrants further validation studies. Considering co-medication effects of antidepressants on weight is important to design robust depression treatment plans.