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HomeData ScienceYale uses ML based PRECISION model for personalized treatment for Hypertension

Yale uses ML based PRECISION model for personalized treatment for Hypertension

A team of researchers using machine learning to study if hypertension patients benefit from intensive blood pressure treatment.

Hypertension, often known as high blood pressure, is one of the most prevalent diseases in the general population, especially in middle-aged and older people. If a person has mild to moderate hypertension, it can initially be treated with lifestyle changes. However, blood pressure drugs would often be taken into consideration if this doesn’t work. Yale University researchers have created a machine learning-based clinical decision support tool to tailor recommendations for blood pressure control treatment goals.

The pressure that pushes blood through arteries when the heart beats, supplying oxygen and nutrients to organs and tissues all over the body, is known as blood pressure. Our organs must maintain a normal blood pressure level in order to function properly and prevent internal injury. Researchers state that hypertension, which develops when there is a continuous blood pressure of more than 140/90 mm Hg, is one of the main causes of heart disease, disability, and early mortality worldwide in the study published earlier this week in The Lancet Digital Health. Though, there has been disagreement over how much blood pressure should be dropped to reduce this risk, particularly for Type 2 diabetes patients for whom clinical trials have shown different outcomes regarding the benefits of aggressive blood pressure control.

This inspired researchers from Yale to create a machine learning-based tool to help people with and without diabetes determine whether to pursue intensive vs conventional blood pressure treatment objectives. Through a data-driven methodology, the innovative clinical decision support tool encourages collaborative decision-making between patients and healthcare professionals.

To determine whether the superiority of intensive vs routine antihypertensive care can be explained by patient characteristics, lead author Dr. Evangelos K. Oikonomou and senior author Dr. Rohan Khera, assistant professor at Yale School of Medicine and director of the Cardiovascular Data Science (CarDS) Lab, gathered data from two randomized clinical trials: SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD BP (Action to Control Cardiovascular Risk in Diabetes Blood Pressure). While SPRINT did not include patients with diabetes, ACCORD BP included only patients with type 2 diabetes mellitus. 

Both studies randomly assigned patients to an intensive or regular systolic blood pressure target of 120 mm Hg or 140 mm Hg.

The SPRINT trial supported the need to decrease blood pressure, but the ACCORD BP trial supported the failure of aggressive blood pressure treatment. This is why the researchers decided to focus on these studies. The researchers used SPRINT data to identify 59 factors, including kidney function, smoking, and statin or aspirin use, to build PREssure Control In Hypertension (PRECISION), an ML model aimed to discover features of individuals who benefited the most from actively reducing blood pressure. Through iterative Cox regression analyses that provided average hazard ratio (HR) estimates weighted for the phenotypic distance of each participant from the index patient of each iteration, they were able to extract personalized treatment effect estimates for the primary outcome, time to first major adverse cardiovascular event (MACE; cardiovascular death, myocardial infarction or acute coronary syndrome, stroke, and acute decompensated heart failure). Then, they used variables that were frequently associated with greater personalized benefit to train an extreme gradient boosting algorithm (known as XGBoost) to predict the customized effect of intensive systolic blood pressure control. 

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The team then evaluated the value of PRECISION when applied to the ACCORD BP trial of patients with type 2 diabetes randomly assigned to receive intensive versus standard systolic blood pressure control. Patients were divided into different groups according to their predicted response to therapy and significant demographic factors (age, sex, cardiovascular disease, and smoking). When compared to conventional treatment, researchers discovered that the tool could identify diabetic patients who benefited from intensive blood pressure management.

According to Khera, it could be difficult to determine the optimal blood pressure targets and treatment plan for individuals who have diabetes and hypertension, they explain that in the study, the team enhanced inference from two important clinical trials using machine learning to evaluate a specific cardiovascular advantage of aggressive blood pressure management. The most important finding is that people with diabetes who benefit from such a treatment method appear to be defined by the benefit profile found in those without diabetes. The research paper reported that intensive systolic blood pressure treatment in SPRINT showed a significant cardiovascular benefit, while corresponding benefits were not shown in ACCORD BP. As opposed to looking at the impacts of the therapies on a population as a whole, this method enabled the team to monitor the effects of the treatments on an individual and individualized level.

According to the researchers, these results suggest that PRECISION can offer trustworthy, useful information to guide decisions about intensive vs. conventional systolic blood pressure treatment among patients with diabetes. Nevertheless, they added that further research in a variety of patient demographics is required to fully comprehend how different factors affect the dangers and advantages of an intensive blood pressure-lowering strategy.

Oikonomou emphasized that, at least until the team prospectively proves its clinical relevance, the proposed machine learning algorithm, PRECISION, is only authorized to be applied for research. It was suggested by the study’s authors that a more comprehensive methodology could be employed to create a more personalized interpretation of clinical trials of diagnostic and therapeutic treatments. Finally, Oikonomou noted that the team is presently investigating the potential of their technology in creating clinical trials that are more intelligent, effective, and safer.

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Preetipadma K
Preetipadma K
Preeti is an Artificial Intelligence aficionado and a geek at heart. When she is not busy reading about the latest tech stories, she will be binge-watching Netflix or F1 races!

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