Simplifying And Improving Treatment Of Drug-Resistant High Blood Pressure

( — An estimated 75 million Americans have hypertension, or high blood pressure, and 50 million take antihypertensive drugs. But in as many as 20 percent of cases, the drugs don’t bring the blood pressure under control, and most doctors randomly add drug after drug in an expensive, prolonged and often unsuccessful guessing game to see what works.

A new review article in the February Journal of Clinical Hypertension by Dr. Samuel Mann reports on an approach that greatly simplifies and improves the treatment of what is called “resistant hypertension.” He reports that this approach usually brings blood pressure under control more quickly and with fewer drugs and side effects. It can also help physicians supersede this trial-and-error process and more quickly get their patients the treatment they need.

“Despite the availability of many effective antihypertensive drugs, resistant hypertension is a reality for many. Treatment guidelines offer only general recommendations such as reducing sodium intake, increasing drug dosage, and adding drugs but provide clinicians with little guidance on which drugs to select,” says Dr. Mann, professor of clinical medicine at Weill Cornell Medical College and a hypertension specialist at NewYork-Presbyterian Hospital/Weill Cornell Medical Center. “What we have instead is the inefficient and often unsuccessful method of doctors randomly adding drug after drug.”

The Mann Algorithm

Dr. Mann’s paper outlines a simplified decision tree, or algorithm, that can help physicians choose medications that can control the blood pressure in most patients with resistant hypertension. It narrows the treatment choices to either or both of just two options, using drugs that target specific mechanisms that underlie hypertension. Dr. Mann says he developed the algorithm because he believes that basing the choice of medication on the mechanisms driving an individual’s hypertension enables treatment that is a better fit for each patient.


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