WASHINGTON (Oct. 10 2016) -- Coupling data mining of adverse event reports and electronic health records with targeted laboratory experiments, researchers found a way to identify and confirm previously unknown drug interactions, according to a study published today in the Journal of the American College of Cardiology.

Drug-drug interactions account for a significant proportion of side effects and hospitalizations, but they are often very difficult to predict.

Researchers in the new study used the data mining approach to discover that together, two commonly used drugs - a popular over-the-counter medication for heartburn relief and an antibiotic used to prevent and treat infection - were associated with an increased risk of acquired long QT syndrome, which can lead to life-threatening arrhythmias or problems with the way the heart beats.
"Doctors must often rely on a wait-and-see approach to monitor safety when patients are taking multiple medicines. By using large datasets of clinical records available from the Food and Drug Administration and in electronic health records at our hospital, we were able to use data science to accurately identify a previously unexpected interaction from among millions of possibilities, which would not have been suspected using current surveillance methods," said Nicholas Tatonetti, Ph.D., Herbert Irving Assistant Professor, Department of Biomedical Informatics at Columbia University and one of the study's authors.

The researchers chose to investigate QT interval prolongation because of its importance in drug safety and drug development. The QT interval is the measure of the time between the start and the end of the cardiac electrical cycle as recorded by the electrocardiogram. With QT prolongation, it takes longer to transmit electrical signals through the heart muscle, which can lead to serious, even fata, heart rhythm disturbances. "Investigative drugs that have the potential to prolong the QT interval will be withdrawn before they are ever given to a patient; however, no such checks exist for drug-drug interactions and they often go undiscovered for years," said Tatonetti.

Using an algorithm called Latent Signal Detection, researchers scanned data from two independent databases to investigate possible QT interval-prolonging drug-drug interactions: 1.8 million adverse event reports from the U.S. Food and Drug Administration's Adverse Event Reporting System and 1.6 million electrocardiograms from 382,221 patients treated at New York-Presbyterian/CUMC between 1996 and 2014. A computer can evaluate millions of data points all at once and flag the most likely drug-drug interactions.

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