New study suggests MRI-derived pressure measurements indicate a 5-fold increase in heart failure risk.
Pankaj Garg, MD, PhD
Credit: University of East Anglia
Findings from a study of nearly 40,000 adults suggest use of magnetic resonance imaging (MRI) could revolutionize the prediction and stratification of risk for heart failure among the general population.
An analysis of data from the UK Biobank cohort, results of the study suggest pressure estimates obtained from cardiovascular MRI (CMR) scans proved useful in predicting incident heart failure, with those with elevated pressure at a 5 times greater likelihood of developing heart failure.1
“Heart failure is a lethal condition resulting from rising pressures. One of the most significant findings of this study is that MRI-derived pressure measurements can reliably predict if an individual will develop heart failure,” said lead investigator Pankaj Garg, MD, PhD, associate professor in Cardiovascular Medicine at University of East Anglia.2 “This breakthrough suggests that heart MRI could potentially replace invasive diagnostic tests. Participants with higher heart pressure measured by MRI had a 5-fold increased risk of developing heart failure over 6 years.”
Few fields in cardiology can lay claim to the sheer level of advancements witnessed by heart failure specialists in the last decade. Once a field marked by failed trials, cardiologists have seen the advent of effective therapies for heart failure with both reduced or preserved ejection fraction. This has come just in time, as the field stares down the barrel of a burgeoning health crisis, with more than 8 million adults in the US alone projected to develop heart failure by 2030. However, A perfect storm, the field finds itself amidst a physician shortage and a declining number of fellowship applicants in recent years. With the latter in mind, identifying effective screening measures to improve risk stratification among the general population.3,4
Based on previous data demonstrating the ability of CMR to estimate pulmonary capillary wedge pressure (PCWP) non-invasively, Garg and a team of colleagues sought to assess the prognostic value of a CMR-modelled PCWP among the general population. To do so, investigators designed their analysis leveraging data from the UK Biobank cohort and a CMR-modelled PWCP incorporating left atrial volume, left ventricular mass, and patient sex. For the purpose of analysis, logistic regression was used to describe the associations between typical cardiovascular risk factors and raised CMR-modelled PCWP and Cox regression was used to calculate risk of heart failure and major adverse cardiovascular events with typical risk factors and CMR-modelled PCWP.1
A total of 39,163 individuals with image quality suitable for determination of left ventricular mass and left atrial volume were identified for inclusion in the study. This cohort had a median age of 64 (IQR, 58 to 70) years, 47% were males, and 8.1% had elevated CMR-modelled PCWP. Relative to those without elevated CMR-modelled PCWP, those with elevated CMR-modelled PCWP were older (66 [IQR, 59 to 72] vs 64 years [IQR, 58 to 70]; P <.001), more frequently male (57% vs 46%; P <.001), and had a greater body mass index (27.1 [IQR, 24.4 to 30.5] vs 25.1 [IQR, 22.8 to 27.8] kg/m2). Additionally, these patients also had a greater frequency of diabetes (8.1% vs 5.4%; P <.001), hypertension (48% vs 30%; P <.001), and hyperlipidemia (41% vs 34%; P <.001).1
Initial results of the study suggested clinical characteristics independently associated with raised CMR-modelled PCWP included (Odds Ratio [OR], 1.57; 95% confidence interval [CI],1.44 to 1.70; P <.001), BMI (OR, 1.57; 95% CI, 1.52 to 1.62, per SD increment; P <.001), male sex (OR, 1.37; 95% CI 1.26 to 1.47, P <0.001), age (OR, 1.33; 95% CI, 1.27 to 1.41, per decade increment; P <.001), and regular alcohol consumption (OR, 1.10; 95% CI 1.02 to 1.19; P = .012).1
“Additionally, we identified key risk factors for developing high heart pressure: age over 70, high blood pressure, obesity, alcohol consumption and male gender,” added Nay Aung, from the William Harvey Research Institute at Queen Mary University of London.1 “By combining these factors, we developed a model to predict individual heart failure risk. This advancement enables prevention, early detection and treatment of heart failure, which could save many lives.”
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