Safety And Effectiveness Of First-line Antihypertensive Drug Classes

CHANGSHA, China - Aug. 18, 2020 - PRLog -- For those with extremely high blood pressure, or hypertension, there are many initial medication options -- so many that it can be hard to know which one to use. Now, a Yale-coauthored paper in Lancet provides more information about the relative safety and effectiveness of different hypertension drugs in order to inform this critical treatment decision. The study reveals that angiotensin converting enzyme (ACE) inhibitors may not be the best choice for initial treatment.

Unprecedented in scale, the Lancet study pulled together the data of 4.9 million patients from nine institutional databases across four countries. The researchers used that data to compare the safety and effectiveness of the five classes of first-line hypertension drugs, including the popular ACE inhibitors. They were looking at how well each drug prevented the three main health consequences of hypertension -- heart attack, heart failure, and stroke -- and to what extent each drug caused 46 unwanted side-effects.

The big data revealed patterns that would otherwise have taken 22,000 typical observational studies to spot, say the researchers. One key finding was that thiazide or thiazide-like diuretics are better at preventing heart attack, heart failure, and stroke than ACE inhibitors, while also being safer than ACE inhibitors. Taken individually, the differences in safety and effectiveness of the treatments might seem small, say the scientists, but at scale, they become significant. The researchers report that if the 2.4 million people in the study currently using ACE inhibitors had instead been using thiazide or thiazide-like diuretics, more than 3,100 major cardiovascular events could potentially have been avoided.

The paper was co-authored by members of the Observational Health Data Science and Informatics (OHDSI) network, a research and data-sharing collaborative based out of Columbia University, and is part of OHDSI's ongoing Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) project, which uses big-data analysis to conduct observational research studies on hundreds of millions of patient records.

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