SC Connecticut News

More than 100 million U.S. adults have been diagnosed with hypertension, a leading risk factor for cardiovascular disease. Despite this, over 70% of those affected cannot achieve adequate blood pressure control with a single drug, while current guidelines only recommend first-line therapy.

“The question is: when the first drug is not enough, what is the optimal second drug to add?” said Yuan Lu, ScD, assistant professor of medicine (cardiology) and assistant professor of biomedical informatics and data science and epidemiology (chronic disease). “There are more than 50 drugs across five major classes available for treating hypertension. Conducting clinical trials to compare every possible drug and combination thereof is impractical; it would be incredibly time-consuming and costly. Consequently, this creates a significant gap in evidence.”

Lu recently received a Research Project Grant (R01) from the National Institutes of Health (NIH) for the project “Real-World Evidence to Inform Decisions for Hypertension Treatment Escalation,” aimed at addressing this issue.

Lu and her team will analyze real-world data routinely collected by clinicians to compare the effectiveness of second antihypertensive agents on major cardiovascular events as well as their comparative risk of potential drug-related adverse events. The study will also examine the effectiveness and safety of each second hypertensive agent in different patient subgroups defined by age, sex, race, ethnicity, and comorbidities. This research aims to address disparities among patients with hypertension.

“Clinicians often face this important patient scenario and lack comprehensive, high-quality evidence on how best to guide the implementation of the available drug options for patients into real-world practice,” said Eric Velazquez, MD, Robert W. Berliner Professor of Medicine and chief of Yale Cardiovascular Medicine. “Hypertension impacts nearly every family in the world. It has been a substantial frustration for me that randomized clinical trials such as ACCOMPLISH have not been adequately integrated into everyday care. Yuan’s work is pivotal to ensure our research meets its potential to improve the lives of millions living with hypertension.”

The study will analyze data from over 100 million patients in five electronic health record (EHR) databases. Lu's team collaborates with Observational Health Data Science and Informatics (OHDSI), an international organization aiming to use systematic approaches to improve observational studies through their OMOP Common Data Model.

“By mapping EHR data into a common data model, we can now combine computing power, data science, and clinical knowledge to generate new evidence addressing these important clinical questions,” said Lu. “We hope our research will inform future clinical trials' prioritization by assisting investigators in selecting promising drug combinations for testing.”

Lu joined Yale in 2015 after earning her ScD in Global Health and Population at Harvard School of Public Health. “I was intrigued by this area because instead of treating 20 or 30 patients daily as a doctor would do, I could impact health at the population level,” she said.

She hopes this research will inform clinical guideline development since real-world data from observational studies can complement clinical trials when they are too expensive or unethical to conduct.

“Physicians can’t just wait for clinical trials before helping their patients; they need to keep treating people using the best available information,” said Lu.

Eventually, Lu's team plans to develop a clinical decision support tool incorporating knowledge gained from this project to help doctors quickly see recommendations about combination therapies suitable for individual patients.

“It’s often said that it takes about 17 years to translate about 14% of research findings into routine clinical practice," she noted. "I want to reduce this time frame and increase knowledge translation."

The research team is refining their protocol for publication online via GitHub so interested parties can provide feedback for improvement. Lu sees potential applications for similar studies in other medical areas like diabetes and obesity.

For instance, Lu recently co-authored a paper published in the Journal of the American Heart Association using real-world EHR data to identify prevalence rates and diagnostic codes within large patient populations at Sentara Health systems.

“I feel fortunate coming to Yale where I work closely with clinicians seeing how my work informs their practice,” Lu concluded. “I’m excited every day.”