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Observational ‘Big Data’ Studies Using EHRs Identify Therapeutic Effects of Drug Combinations

August 17, 2015
by David Raths
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Studying EHR data, Regenstrief researchers find beneficial effect from drug combination for patients with hypertension
Regenstrief Institute investigator Wanzhu Tu, Ph.D.

“Big data” studies involving the electronic medical records of thousands of patients over many years are starting to reveal new insights about the impact of drug combinations that may be unattainable through traditional clinical trials.

A study led by researchers from the Regenstrief Institute and Indiana University has found that a drug commonly prescribed to conserve potassium in the blood also significantly lowers blood pressure when taken in conjunction with a diuretic frequently prescribed to patients with hypertension. The combination of the two drugs, both available as generics, has been shown to consistently amplify blood pressure reduction in patients with or without the presence of other antihypertensive agents such as ACE inhibitors and calcium channel blockers.

Using de-identified information from the electronic medical records of 17,291 hypertensive patients who were prescribed the drug hydrochlorothiazide with or without triamterene between 2004 and 2012, the researchers have demonstrated triamterene's ability to enhance the blood pressure-lowering effect of hydrochlorothiazide, a commonly prescribed diuretic.

"This study is a perfect example of how we can learn about the previously unknown therapeutic effects of drugs from big data. In this case, big electronic medical record data are being used to answer questions that may otherwise be unanswerable," said Wanzhu Tu, Ph.D., first author of the new study published online ahead of print in the Journal of General Internal Medicine, in a prepared statement.

"It is unlikely that a large clinical trial would be conducted to reexamine the blood pressure effect of triamterene, a drug that has been on the market since 1965,” he said. “Yet smaller clinical trials simply do not provide sufficient power to determine the drug's effect. Observational studies based on big data, like ours, provide a viable alternative."

Tu is a Regenstrief Institute investigator, an IU Center for Aging Research scientist and a professor of biostatistics at the IU School of Medicine.