Risk Prediction Tool Shows Promise for Detecting Heart Failure Earlier

A simple scoring tool known as PCP-HF, which estimates an individual’s 10-year risk of heart failure, may be an important addition to primary prevention efforts, researchers suggest.

“We felt that there was a gap in being able to quantify risk of heart failure for primary care patients and wanted to develop something that was similar to how the ASCVD risk score works,” lead author Sadiya Khan, MD (Northwestern University Feinberg School of Medicine, Chicago, IL), told TCTMD.

“Ideally, we would want to be able to identify patients at highest risk and implement more intensive risk-factor strategies or identify novel strategies that may be able to be targeted to prevent heart failure.” Among these strategies is more intensive blood pressure-lowering, especially since the SPRINT trial showed that the benefits of achieving lower pressures were primary driven by reductions in heart failure, she said.

For the study, published online today in the Journal of the American College of Cardiology, Khan and colleagues pooled data from five large trials to create a racially and geographically diverse population of 21,240 men and women without cardiovascular disease from which to derive values for the new PCP-HF score. Patients in the study ranged in age from 30 to 79 years, which Khan said was done to be as inclusive as possible.

The trials were: ARIC, CARDIA, Cardiovascular Health Study, Framingham Heart Study Offspring Cohort, and MESA. The score was then validated in white participants from the PREVEND trial and in black participants from the Jackson Heart Study.

Overall, the score performed well for 10-year risk prediction, with C-statistics of 0.79 for white men and 0.71 for white women and of 0.85 and 0.78 for black men and black women, respectively. Looking at individuals age 55 years, for example, risks vary considerably by sex and race. While the absolute 10-year risk for heart failure is 23% for white men, 25% for black men, and 26% for black women, it is only 14% for white women.

Although age was the strongest independent predictor of risk, “age is not modifiable, so looking at things we can modify is of value” Khan said. She and her colleagues say the score represents “an appealing and inexpensive initial screening tool” for use in the primary care setting.

Subjectivity and Lack of Universal HF Definition

In an accompanying editorial, John G.F. Cleland, MD (University of Glasgow, Scotland), and colleagues say a strategy of targeted intervention based on the model is “unlikely to have an impact on the population incidence of heart failure.” They point out that in the entire cohort the 10-year incidence of heart failure was 12%, and that most of those cases would have occurred in older individuals anyway, suggesting age may be just as effective for screening.

Another issue, the editorialists write, is the lack of a “generally agreed upon definition of heart failure, which remains a diagnosis largely based on a clinical interpretation of subjective criteria that are difficult to verify and validate in retrospect.”

Cleland and colleagues say a universal definition of heart failure based on “objective and verifiable measurements,” such as biomarkers and imaging, is important to pursue because it will enable earlier intervention before symptoms develop.

“It’s a really important point and probably one of the biggest challenges in clinical practice and in research for heart failure, because heart failure is a heterogenous syndrome that can occur as a result of multiple different conditions,” Khan responded. “We struggle with both diagnosis and prediction. I’m not sure whether or not we will succeed in creating a universal definition that is based on biomarkers alone, but I think this is something that we do need to work towards."

To TCTMD, Khan said the most important aspect of the new tool is that it can help “personalize the risk discussion.” For starters, calculating the PCP-HF score is fairly simple. The current online version requires the physician to input the following information: age, gender, race, hypertension treatment (yes or no), fasting glucose value, smoking status, body mass index, systolic BP, diabetes treatment (yes or no), total cholesterol, HDL cholesterol, and QRS duration.

Added to lifestyle modification and other strategies, Khan said having a quantifiable risk estimate in the form of a personalized number may help patients and physicians work together to target the best way to reduce their individual risk. It may even enhance motivation on the part of patients to improve their scores. Her group is hoping to develop PCP-HF into an app, which she said will further “enhance the translation and implementation of the tool.”