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Abstract
Introduction Patients with Type 2 Diabetes Mellitus (T2DM) are at increased risk of cardiovascular disease, including epicardial coronary heart disease, silent myocardial infarction (MI), and coronary microvascular dysfunction (CMD). [1] All of these can be assessed and quantified using cardiac magnetic resonance (CMR), including most recently quantitative myocardial blood flow (MBF). We aimed to determine the prognostic relevance of MBF in patients with T2DM and test the hypothesis that impaired stress MBF and myocardial perfusion reserve (MPR) have independent prognostic value over standard clinical and imaging parameters.
Methods A 4-centre study of patients with T2DM who underwent quantitative perfusion assessment using CMR. Diagnosis of T2DM was based on Hba1c 48 mmol/l or a known diagnosis of T2DM. Image analysis was performed automatically using an artificial intelligence approach deriving global MBF and MPR. [2] Cox proportional hazard models adjusting for comorbidities and CMR parameters sought associations between stress MBF and MPR with death and major adverse cardiovascular events (MACE), including MI, non-fatal stroke, heart failure hospitalisation and death.
Results A total of 630 patients with T2DM were included with a median follow-up of 722 days (interquartile range 493) days. There were 27 (4.3%) deaths and 76 MACE events in 62 (12.1%) patients. Patient data was represented into groups depending on threshold stress MBF values of 1.94 ml/g/min and MPR thresholds of 1.96 using validated data from invasive coronary physiology [3]. Patient demographics are seen in table 1 and CMR data in table 2. Kaplan-Meier curves are seen in figure 1. Stress MBF was associated with mortality and MACE after adjusting for age, LV ejection fraction and HbA1c. The stress MBF adjusted hazard ratios for all cause death and death and MACE were 0.35 (95% CI, 0.13–0.95, P=0.04) and 0.54 (95% CI, 0.30–0.96, P=0.04), respectively. MPR was not significantly associated with death and MACE after adjusting for age, LV ejection fraction and HbA1c; hazard ratio for all cause death and death and MACE was 0.83 (95% CI, 0.41–1.69, P=0.60) and 0.81( 95% CI, 0.53–1.23, p=0.32) respectively. DiscussionIn patients with T2DM, reduced stress MBF measured automatically inline using artificial intelligence quantification of cardiovascular magnetic resonance perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcome.
Patient characteristics divided into groups based on stress myocardial blood flow (MBF) thresholds (more than or less than 1.94ml/g/min). Groups are then compared with one another using the appropriate test (independent t test or chi squared). P value is considered significant at the <0.05. Continuous variables are presented as mean+/- SD. Dichotomous variables are presented as number (%)
CMR parameters based on stress myocardial blood flow (MBF) thresholds (more than or less than 1.94ml/g/min). Groups are compared using the appropriate test (independent t test or chi squared). P value is considered significant at the <0.05. Continuous variables are presented as mean+/- SD. Dichotomous variables are presented as number (%). MBF- Myocardial blood flow; MPR-Myocardial perfusion reserve; LV- Left ventricle; LVEF- Left ventricular ejection fraction; LVEDV- Left ventricular end-diastolic volume; LGE-Late gadolinium enhancement.
Kaplan-Meier survival estimate curves for MACE events using stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Survival curves are represented as <1.94ml/g/min or 1.96 MPR in the blue line and >1.94ml/g/min or 1.96 in the green line. P value is considered significant at <0.05
References 1. Murthy VL, Naya M, Foster CR, et al. Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation. 2012;126(15):1858–1868. doi:10.1161/CIRCULATIONAHA.112.12040
2. Kellman P, Hansen MS, Nielles-Vallespin S et al. Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification. J Cardiovasc Magn Reson. 2017;19:43. doi: 10.1186/s12968-017-0355-5
3. Kotecha T, Martinez-Naharro A, Boldrini M et al. Automated Pixel-Wise Quantitative Myocardial Perfusion Mapping by CMR to Detect Obstructive Coronary Artery Disease and Coronary Microvascular Dysfunction: Validation Against Invasive Coronary Physiology. JACC Cardiovasc Imaging. 2019 Oct;12(10):1958–1969. doi: 10.1016/j.jcmg.2018.12.022. Epub 2019 Feb 13. PMID:
Conflict of Interest Nil