TY - JOUR
T1 - Prognostic value of pulmonary transit time and pulmonary blood volume estimation using myocardial perfusion CMR
AU - Seraphim, Andreas
AU - Knott, Kristopher D.
AU - Menacho, Katia
AU - Augusto, João B.
AU - Davies, Rhodri
AU - Pierce, Iain
AU - Joy, George
AU - Bhuva, Anish N.
AU - Xue, Hui
AU - Treibel, Thomas A.
AU - Cooper, Jackie A.
AU - Petersen, Steffen E.
AU - Fontana, Marianna
AU - Hughes, Alun D.
AU - Moon, James C.
AU - Manisty, Charlotte
AU - Kellman, Peter
N1 - Funding Information:
This study was supported by a Clinical Training Research Fellowship (to Dr. Seraphim) from the British Heart Foundation (FS/18/83/34025) and directly and indirectly from the National Institute for Health Research Biomedical Research Centres at University College London Hospitals and Barts Health National Health Service Trusts. This study was also supported by the National Heart, Lung and Blood Institute, National Institutes of Health by the Division of Intramural Research (Z1A- HL006214-05 and Z1A- HL006242-02). This work forms part of the research areas contributing to the translational research portfolio of the Biomedical Research Centre at Barts, which is supported and funded by the National Institute for Health Research. Prof. Petersen has served as a consultant for and is a shareholder of Circle Cardiovascular Imaging Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Publisher Copyright:
© 2021 The Authors
PY - 2021/11
Y1 - 2021/11
N2 - Objectives: The purpose of this study was to explore the prognostic significance of PTT and PBVi using an automated, inline method of estimation using CMR. Background: Pulmonary transit time (PTT) and pulmonary blood volume index (PBVi) (the product of PTT and cardiac index), are quantitative biomarkers of cardiopulmonary status. The development of cardiovascular magnetic resonance (CMR) quantitative perfusion mapping permits their automated derivation, facilitating clinical adoption. Methods: In this retrospective 2-center study of patients referred for clinical myocardial perfusion assessment using CMR, analysis of right and left ventricular cavity arterial input function curves from first pass perfusion was performed automatically (incorporating artificial intelligence techniques), allowing estimation of PTT and subsequent derivation of PBVi. Association with major adverse cardiovascular events (MACE) and all-cause mortality were evaluated using Cox proportional hazard models, after adjusting for comorbidities and CMR parameters. Results: A total of 985 patients (67% men, median age 62 years [interquartile range (IQR): 52 to 71 years]) were included, with median left ventricular ejection fraction (LVEF) of 62% (IQR: 54% to 69%). PTT increased with age, male sex, atrial fibrillation, and left atrial area, and reduced with LVEF, heart rate, diabetes, and hypertension (model r2 = 0.57). Over a median follow-up period of 28.6 months (IQR: 22.6 to 35.7 months), MACE occurred in 61 (6.2%) patients. After adjusting for prognostic factors, both PTT and PBVi independently predicted MACE, but not all-cause mortality. There was no association between cardiac index and MACE. For every 1 × SD (2.39-s) increase in PTT, the adjusted hazard ratio for MACE was 1.43 (95% confidence interval [CI]: 1.10 to 1.85; p = 0.007). The adjusted hazard ratio for 1 × SD (118 ml/m2) increase in PBVi was 1.42 (95% CI: 1.13 to 1.78; p = 0.002). Conclusions: Pulmonary transit time (and its derived parameter pulmonary blood volume index), measured automatically without user interaction as part of CMR perfusion mapping, independently predicted adverse cardiovascular outcomes. These biomarkers may offer additional insights into cardiopulmonary function beyond conventional predictors including ejection fraction.
AB - Objectives: The purpose of this study was to explore the prognostic significance of PTT and PBVi using an automated, inline method of estimation using CMR. Background: Pulmonary transit time (PTT) and pulmonary blood volume index (PBVi) (the product of PTT and cardiac index), are quantitative biomarkers of cardiopulmonary status. The development of cardiovascular magnetic resonance (CMR) quantitative perfusion mapping permits their automated derivation, facilitating clinical adoption. Methods: In this retrospective 2-center study of patients referred for clinical myocardial perfusion assessment using CMR, analysis of right and left ventricular cavity arterial input function curves from first pass perfusion was performed automatically (incorporating artificial intelligence techniques), allowing estimation of PTT and subsequent derivation of PBVi. Association with major adverse cardiovascular events (MACE) and all-cause mortality were evaluated using Cox proportional hazard models, after adjusting for comorbidities and CMR parameters. Results: A total of 985 patients (67% men, median age 62 years [interquartile range (IQR): 52 to 71 years]) were included, with median left ventricular ejection fraction (LVEF) of 62% (IQR: 54% to 69%). PTT increased with age, male sex, atrial fibrillation, and left atrial area, and reduced with LVEF, heart rate, diabetes, and hypertension (model r2 = 0.57). Over a median follow-up period of 28.6 months (IQR: 22.6 to 35.7 months), MACE occurred in 61 (6.2%) patients. After adjusting for prognostic factors, both PTT and PBVi independently predicted MACE, but not all-cause mortality. There was no association between cardiac index and MACE. For every 1 × SD (2.39-s) increase in PTT, the adjusted hazard ratio for MACE was 1.43 (95% confidence interval [CI]: 1.10 to 1.85; p = 0.007). The adjusted hazard ratio for 1 × SD (118 ml/m2) increase in PBVi was 1.42 (95% CI: 1.13 to 1.78; p = 0.002). Conclusions: Pulmonary transit time (and its derived parameter pulmonary blood volume index), measured automatically without user interaction as part of CMR perfusion mapping, independently predicted adverse cardiovascular outcomes. These biomarkers may offer additional insights into cardiopulmonary function beyond conventional predictors including ejection fraction.
KW - First pass perfusion
KW - Outcomes
KW - Pulmonary blood volume
UR - http://www.scopus.com/inward/record.url?scp=85107272142&partnerID=8YFLogxK
U2 - 10.1016/j.jcmg.2021.03.029
DO - 10.1016/j.jcmg.2021.03.029
M3 - Article
C2 - 34023269
AN - SCOPUS:85107272142
SN - 1936-878X
VL - 14
SP - 2107
EP - 2119
JO - JACC: Cardiovascular Imaging
JF - JACC: Cardiovascular Imaging
IS - 11
ER -
