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More specifically, they want to study whether such medications increase the risk of kidney and cardiovascular disease in CKD patients.
More than one in seven Americans suffer from CKD, according to the U.S. Centers for Disease Control and Prevention (CDC). CKD is a condition where the kidneys gradually lose their capacity to filter blood efficiently. It can cause chronic inflammation and lead to other complications, including heart disease and kidney failure.
Blood pressure-lowering drugs are frequently prescribed for people with CKD because they may also lower inflammation and enhance blood-vessel health, which can help protect the heart and kidneys. It’s unclear, however, if one type of blood-pressure-lowering drug is better than another at preventing heart disease in CKD patients.
Ming Wang, lead investigator and associate professor of biostatistics in the Department of Population and Quantitative Health Sciences at the Case Western Reserve School of Medicine, will analyze data from two large studies: the Chronic Renal Insufficiency Cohort and the Systolic Blood Pressure Intervention Trial.
Wang and co-principal investigator Mahboob Rahman, professor of medicine at the School of Medicine and division chief of nephrology and hypertension at University Hospitals Cleveland Medical Center, will use a novel statistical method of cause-and-effect—called “dynamic propensity trajectory matching”—to evaluate and estimate the effects of high blood-pressure therapies for CKD patients.
“This new technique allows us to examine real-world data, take into account treatment changes over time and use a combination of multiple medications,” Wang said. “Our ultimate objective is to create reliable inference techniques, user-friendly software tools and an application to assist other researchers and medical professionals in using these findings to enhance the treatment of patients with chronic kidney disease.”
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