Impact of Rate-Pressure Product Correction of Myocardial Blood Flow on the Prognostic Relevance in Patients with Heart Failure

Presenting Author(s)

• 

  Masafumi Takafuji, MD, PhD

  Clinical Research Fellow
  Leeds Institute of Cardiovascular and Metabolic Medicine
  Leeds, England, United Kingdom

Primary Author(s)

• 

  Masafumi Takafuji, MD, PhD

  Clinical Research Fellow
  Leeds Institute of Cardiovascular and Metabolic Medicine
  Leeds, England, United Kingdom

Co-Author(s)

• TA

  Thomas Anderton, MB

  CRF
  LICAMM
  Leeds, England, United Kingdom
• MG

  Michelle Gibbs

  Medical Student
  University of Leeds, England, United Kingdom
• CS

  Ching-Hui Sia, MD

  Dr
  Leeds Institute of Cardiovascular and Metabolic Medicine, United Kingdom
• 

  Bradley Chambers

  Clinical Research Fellow
  University of Leeds
  Harewood, England, United Kingdom
• 

  Maryum Farooq, MB

  Research Fellow
  Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds
  Leeds, England, United Kingdom
• CS

  Chin Soo, MB

  CRF
  LICAMM
  Leeds, England, United Kingdom
• MA

  Mehak Asad, MD, MPH

  Clinical research fellow
  University of Leeds, Multidisciplinary Cardiovascular Research Centre and Biomedical Imaging Science Department, Leeds Institute of Cardiovascular and Metabolic Medicine, LS2 9JT, United Kingdom
  Leeds, England, United Kingdom
• ML

  May Lwin, MD

  Clinical Research Fellow
  University of Leeds
  Leeds, England, United Kingdom
• ED

  Erica Dall'Armellina, PhD

  Associate Professor and Honorary Consultant Cardiologist
  University of Leeds, England, United Kingdom
• 

  John Greenwood, MD, PhD, FSCMR

  Director
  Baker Heart and Diabetes Institute
  Melbourne, Victoria, Australia
• 

  Pankaj Garg, MD, PhD

  Consultant Clinical Associate Professor
  University of East Anglia, United Kingdom
• 

  Peter Kellman, PhD

  Senior Scientist
  National Institutes of Health
  Bethesda, Maryland, United States
• 

  Peter P. Swoboda, PhD

  Consultant Cardiologist
  Leeds Institute of Cardiovascular and Metabolic Medicine
  Leeds, England, United Kingdom
• 

  Sven Plein, MD, PhD

  Professor of Cardiology
  Leeds Institute of Cardiovascular and Metabolic Medicine
  Leeds, England, United Kingdom

Background: Quantitative perfusion cardiac magnetic resonance (QP-CMR) enables the quantification of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR), with evidence of additive prognostic benefit over traditional CMR markers [1]. For Positron Emission Tomography quantitative perfusion imaging, international guidelines recommend correcting rest MBF using the rate-pressure product (RPP=heart rate × systolic blood pressure), as elevated RPP can lead to increased rest MBF. No such guidance exists for QP-CMR. We aimed to assess the effect of RPP correction on MBF values and its prognostic value of QP-CMR in patients with heart failure (HF).

Methods: We prospectively recruited 641 patients with HF referred for clinical CMR and 184 healthy volunteers. All participants underwent CMR with standard volumes, adenosine stress/rest perfusion, and late gadolinium enhancement (LGE) (Prisma 3.0T, Siemens). QP values were produced using an automated, in-line dual sequence method[2]. Rest and stress MBF were corrected for RPP using the formula MBF_corr=rest MBF_uncorr×10,000/RPP. Corrected MPR was defined as uncorrected stress MBF divided by rest MBF_corr. The primary endpoint was a composite of heart failure hospitalization (HFH) and all-cause mortality. Associations of corrected and uncorrected MBF/MPR with the primary endpoint were assessed using Cox regression and Kaplan–Meier curves with the log-rank test.

Results: RPP correction increased rest MBF and decreased MPR, but to a greater extent in healthy volunteers than in patients. During a median follow-up of 3.5 years, 101 events occurred (55 deaths and 46 HFH). Patients who reached the primary endpoint had significantly higher rest MBF_uncorr and lower MPR_uncorr than both patients who did not reach the primary endpoint and healthy volunteers. Rest MBF_corr and MPR_corr did not differ significantly between HF groups, but healthy volunteers had higher rest MBF_corr and MPR_corr than those with HF with and without events (Table 1). In cox regression analysis, rest MBF_uncorr (adjusted HR per 1 mL/g/min increase, 3.97, 95% CI 1.93–8.16, p< 0.001) and MPR_uncorr (adjusted HR per 1 unit increase, 0.74, 95% CI 0.57–0.95, p=0.024) were significantly associated with the primary endpoint in both univariate and multivariate models. However, after RPP correction, these associations were no longer statistically significant (Table 2). Kaplan–Meier analysis revealed significant differences in event-free survival for rest MBF_uncorr (p=0.005), stress MBF (p=0.005), MPR_uncorr (p< 0.001), and MPR_corr (p=0.044), but not for rest MBF_corr (p=0.076) (Figure 1).

Conclusion: We have found uncorrected rest MBF and MPR assessed by QP-CMR to be independently associated with adverse outcomes in patients with HF. Correcting rest MBF and MPR with RPP changed MBF and MPR values and attenuated their association in a large cohort of patients with HF. Our findings support the use of uncorrected QP values in patients with HF.

Table 1. Quantitative perfusion parameters in patients with heart failure and healthy volunteers. *Significantly different from healthy volunteers (Steel–Dwass test, p < 0.05). †Significantly different from HF patients without events (Steel–Dwass test, p < 0.05). **p-values are from Kruskal–Wallis test.

Variable, median (IQR)	Heart failure patients with event	Heart failure patients without event	Healthy volunteers	p**
Rest MBFuncorr, mL/g/min	0.70 (0.58–0.87)*†	0.61 (0.52–0.73)*	0.57 (0.48–0.68)	< 0.001
Rest MBFcorr, mL/g/min	0.77 (0.64–0.97)*	0.73 (0.61–0.92)*	0.92 (0.78–1.06)	< 0.001
Rest RPP, bpm×mmHg	9006 (7245–10952)*†	8255 (6964–9773)*	6336 (5518–7403)	< 0.001
Stress MBF, mL/g/min	1.48 (1.12–1.93)*	1.57 (1.25–1.95)*	2.16 (1.81–2.49)	< 0.001
Stress RPP, bpm×mmHg	9711 (8345–11512)*	10147 (8362–12081)*	8570 (7526–10113)	< 0.001
MPRuncorr	2.04 (1.43–2.72)*†	2.53 (2.03–3.14)*	3.63 (3.08–4.40)	< 0.001
MPRcorr	1.93 (1.38–2.42)*	2.07 (1.58–2.69)*	2.31 (2.00–2.79)	< 0.001

Table 2. Cox regression for perfusion parameters and the primary endpoint. * Model adjusted for age, sex, LVEF and presence of ischemia and ischemic LGE. MBF, myocardial blood flow, MPR, myocardial perfusion reserve.

	Primary outcome: Composite of Death or HFH
Predictor	Unadjusted HR (95% CI)	P value	Adjusted HR* (95%CI)	P value
Rest MBFuncorr	3.43 (1.52–6.84)	0.001	3.97 (1.93–8.16)	< 0.001
Rest MBFcorr	1.13 (0.52–2.31)	0.749	1.29 (0.63–2.62)	0.489
Stress MBF	0.68 (0.47–0.99)	0.045	0.90 (0.60–1.33)	0.605
MPRuncorr	0.65 (0.51–0.84)	0.001	0.74 (0.57–0.95)	0.024
MPRcorr	0.75 (0.58–0.96)	0.028	0.84 (0.65–1.07)	0.179

Figure 1. Kaplan–Meier curves of quantitative perfusion parameters for predicting the primary endpoints. Cutoff values were defined as the 5th percentiles of healthy volunteers.