AI helps ID patients who would benefit most from strict blood pressure control
Who stands to benefit the most from intensive treatment for high blood pressure? It's not always the patients one might expect, according to a new study published in the International Journal of Epidemiology. The study's authors relied on an artificial intelligence technique called “causal forest" to guide their decisions, sharing the promising results.
Causal forest AI models analyze large datasets by creating a “forest” of many decision trees, with the aim of understanding how different patient characteristics are associated with different medical outcomes. Then, the AI uses that understanding to create algorithms that help predict future outcomes—in this instance, to predict whether blood pressure medications might improve health outcomes for an individual with high blood pressure.
“The findings of our study shed light on a powerful machine learning algorithm that enables us to identify individuals who would benefit the most from strict blood pressure control, which can be a crucial element in achieving the precision medicine,” said senior author Yusuke Tsugawa, an associate professor of medicine at the David Geffen School of Medicine at UCLA and of health policy and management at the Fielding School, in a statement about the study.
The current medical approach to treating high blood pressure focuses on a “high-risk” approach, favoring intervention for people at the greatest risk for adverse outcomes, such as people with hypertension. However, the study’s findings suggest that a “high-benefit” approach—which instead targets those who are most likely to benefit from intervention, based on the “causal forest” findings—could be more effective.
“We found that a substantial number of individuals without hypertension benefited from lowering their blood pressure,” said lead author Kosuke Inoue, an associate professor of social epidemiology at Kyoto University in Japan. “By applying the causal forest method, we found that treating individuals with high estimated benefits provided better population health outcomes than the traditional high-risk approach.”
For example, certain individuals with additional underlying risk factors for heart disease, such as diabetes, may be unlikely to benefit from taking medications to lower their blood pressure. Still, the authors noted, approximately 80% of people with blood pressure readings at or above 130 mmHg did benefit from intensive blood pressure control.
The findings could potentially provide a reason to revisit the current prevailing treatment strategy to favor one that is benefit-based as opposed to risk-based, according to the authors. Additionally, the discovery advances efforts to move toward more individualized care.
View the full study here.