Dataset Information

Imaging of Clinically Unrecognized Myocardial Fibrosis in Patients With Suspected Coronary Artery Disease.

ABSTRACT:

Background

Stress cardiac magnetic resonance (CMR) provides accurate assessment of both myocardial infarction (MI) and ischemia.

Objectives

This study aimed to evaluate the incremental prognostic value of unrecognized myocardial infarction (UMI), detected during assessment of coronary artery disease (CAD) by stress CMR, beyond cardiac function and ischemia.

Methods

In the multicenter SPINS (Stress CMR Perfusion Imaging in the United States) study, 2,349 consecutive patients (63 ± 11 years of age, 53% were male) with suspected CAD were assessed by stress CMR and followed over a median of 5.4 years. UMI was defined as the presence of late gadolinium enhancement consistent with MI in the absence of medical history of MI. This study investigated the association of UMI with all-cause mortality and nonfatal MI (death and/or MI), and major adverse cardiac events (MACE).

Results

UMI was detected in 347 patients (14.8%) and clinically recognized myocardial infarction (RMI) in 358 patients (15.2%). Compared with patients with RMI, patients with UMI had a similar burden of cardiovascular risk factors, but significantly lower left ventricular ejection fraction (p < 0.001) and lower rates of guideline-directed medical therapies, including aspirin (p < 0.001), statin (p < 0.001), and beta-blockers (p = 0.002). During follow-up, 328 deaths and/or MIs and 528 MACE occurred. In univariate analysis, UMI and RMI were strongly associated with death and/or MI (UMI: hazard ratio [HR]: 2.15; 95% confidence interval [CI]: 1.63 to 2.83; p < 0.001; RMI: HR: 2.45; 95% CI: 1.89 to 3.18) and MACE. Compared with patients with RMI, patients with UMI presented an increased risk for heart failure hospitalization (UMI vs. RMI: HR: 2.60; 95% CI: 1.48 to 4.58; p < 0.001). In a multivariate model including ischemia and left ventricular ejection fraction, UMI and RMI maintained robust prognostic association with death and/or MI (UMI: HR: 1.82; 95% CI: 1.37 to 2.42; p < 0.001; RMI: HR: 1.54; 95% CI: 1.14 to 2.09) and MACE.

Conclusions

In a multicenter cohort of patients with suspected CAD, presence of UMI or RMI portended an equally significant risk for death and/or MI, independently of the presence of ischemia. Compared with RMI patients, those with UMI were less likely to receive guideline-directed medical therapies and presented an increased risk for heart failure hospitalization that warrants further study. (Stress CMR Perfusion Imaging in the United States [SPINS]; NCT03192891).

SUBMITTER: Antiochos P

PROVIDER: S-EPMC8691844 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Unrecognized myocardial infarction (UMI) is known to constitute a substantial portion of potentially lethal coronary heart disease. However, the diagnosis of UMI is based on the appearance of incidental Q-waves on 12-lead electrocardiography. Thus, the syndrome of non-Q-wave UMI has not been investigated. Delayed-enhancement cardiovascular magnetic resonance (DE-CMR) can identify MI, even when small, subendocardial, or without associated Q-waves. The aim of this study was to investigate the prevalence and prognosis associated with non-Q-wave UMI identified by DE-CMR.We conducted a prospective study of 185 patients with suspected coronary disease and without history of clinical myocardial infarction who were scheduled for invasive coronary angiography. Q-wave UMI was determined by electrocardiography (Minnesota Code). Non-Q-wave UMI was identified by DE-CMR in the absence of electrocardiographic Q-waves. Patients were followed to determine the prognostic significance of non-Q-wave UMI. The primary endpoint was all-cause mortality. The prevalence of non-Q-wave UMI was 27% (50/185), compared with 8% (15/185) for Q-wave UMI. Patients with non-Q-wave UMI were older, were more likely to have diabetes, and had higher Framingham risk than those without MI, but were similar to those with Q-wave UMI. Infarct size in non-Q-wave UMI was modest (8%+/-7% of left ventricular mass), and left ventricular ejection fraction (LVEF) by cine-CMR was usually preserved (52%+/-18%). The prevalence of non-Q-wave UMI increased with the extent and severity of coronary disease on angiography (p<0.0001 for both). Over 2.2 y (interquartile range 1.8-2.7), 16 deaths occurred: 13 in non-Q-wave UMI patients (26%), one in Q-wave UMI (7%), and two in patients without MI (2%). Multivariable analysis including New York Heart Association class and LVEF demonstrated that non-Q-wave UMI was an independent predictor of all-cause mortality (hazard ratio [HR] 11.4, 95% confidence interval [CI] 2.5-51.1) and cardiac mortality (HR 17.4, 95% CI 2.2-137.4).In patients with suspected coronary disease, the prevalence of non-Q-wave UMI is more than 3-fold higher than Q-wave UMI. The presence of non-Q-wave UMI predicts subsequent mortality, and is incremental to LVEF.Clinicaltrials.gov NCT00493168.

Project description:ImportanceThe role of stress cardiac magnetic resonance (CMR) imaging in clinical decision-making by reclassification of risk across American College of Cardiology/American Heart Association guideline-recommended categories has not been established.ObjectiveTo examine the utility of stress CMR imaging for risk reclassification in patients without a history of coronary artery disease (CAD) who presented with suspected myocardial ischemia.Design, setting, and participantsA retrospective, multicenter cohort study with median follow-up of 5.4 years (interquartile range, 4.6-6.9) was conducted at 13 centers across 11 US states. Participants included 1698 consecutive patients aged 35 to 85 years with 2 or more coronary risk factors but no history of CAD who presented with suspected myocardial ischemia to undergo stress CMR imaging. The study was conducted from February 18, 2019, to March 1, 2020.Main outcomes and measuresCardiovascular (CV) death and nonfatal myocardial infarction (MI). Major adverse CV events (MACE) including CV death, nonfatal MI, hospitalization for heart failure or unstable angina, and late, unplanned coronary artery bypass graft surgery.ResultsOf the 1698 patients, 873 were men (51.4%); mean (SD) age was 62 (11) years, accounting for 67 CV death/nonfatal MIs and 190 MACE. Clinical models of pretest risk were constructed and patients were categorized using guideline-based categories of low (<1% per year), intermediate (1%-3% per year), and high (>3% year) risk. Stress CMR imaging provided risk reclassification across all baseline models. For CV death/nonfatal MI, adding stress CMR-assessed left ventricular ejection fraction, presence of ischemia, and late gadolinium enhancement to a model incorporating the validated CAD Consortium score, hypertension, smoking, and diabetes provided significant net reclassification improvement of 0.266 (95% CI, 0.091-0.441) and C statistic improvement of 0.086 (95% CI, 0.022-0.149). Stress CMR imaging reclassified 60.3% of patients in the intermediate pretest risk category (52.4% reclassified as low risk and 7.9% as high risk) with corresponding changes in the observed event rates of 0.6% per year for low posttest risk and 4.9% per year for high posttest risk. For MACE, stress CMR imaging further provided significant net reclassification improvement (0.361; 95% CI, 0.255-0.468) and C statistic improvement (0.092; 95% CI, 0.054-0.131), and reclassified 59.9% of patients in the intermediate pretest risk group (48.7% reclassified as low risk and 11.2% as high risk).Conclusions and relevanceIn this multicenter cohort of patients with no history of CAD presenting with suspected myocardial ischemia, stress CMR imaging reclassified patient risk across guideline-based risk categories, beyond clinical risk factors. The findings of this study support the value of stress CMR imaging for clinical decision-making, especially in patients at intermediate risk for CV death and nonfatal MI.

Project description:Background. In experimental setting the concept of myocardial preconditioning-by hyperoxia has been introduced and different intracellular protective mechanisms and their effects have been described. To study whether similar protective phenotype can be induced by hyperoxia also in humans, gene expression profile after hyperoxic exposure was analyzed. Methods and Findings. Adult patients were randomized to be ventilated with either FiO2 0.4 (n=14) or 1.0 (n=10) for 60 minutes before coronary artery bypass grafting. A tissue sample from the right atrial appendage was taken for gene analysis and expression profile analysis on genome wide level by gene chip analysis was applied. Exposure to > 96% oxygen for 60 minutes significantly changed the expression of 20 different genes, including upregulation of two different humanins - MTRNR2L2 and MTRNR2L8, and activated a “cell survival” network as detected by Ingenuity Pathway Analyses. Conclusions. Administration of > 96% oxygen for 1 hour changes gene expression in the myocardium of the patients with coronary artery disease and may enhance cell survival capability. Background. In experimental setting the concept of myocardial preconditioning-by hyperoxia has been introduced and different intracellular protective mechanisms and their effects have been described. To study whether similar protective phenotype can be induced by hyperoxia also in humans, gene expression profile after hyperoxic exposure was analyzed. Methods and Findings. Adult patients were randomized to be ventilated with either FiO2 0.4 (n=14) or 1.0 (n=10) for 60 minutes before coronary artery bypass grafting. A tissue sample from the right atrial appendage was taken for gene analysis and expression profile analysis on genome wide level by gene chip analysis was applied. Exposure to > 96% oxygen for 60 minutes significantly changed the expression of 20 different genes, including upregulation of two different humanins - MTRNR2L2 and MTRNR2L8, and activated a “cell survival” network as detected by Ingenuity Pathway Analyses. Conclusions. Administration of > 96% oxygen for 1 hour changes gene expression in the myocardium of the patients with coronary artery disease and may enhance cell survival capability. 24 samples, 14 controls and 10 with intervention

Project description:BACKGROUND:Risk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA). METHODS:Patients with suspected CAD (n?=?203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers. FINDINGS:A total of 196 patients with analyzable protein levels (n?=?332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79?±?0·01, outperforming prediction with generally available clinical characteristics (AUC?=?0·65?±?0·04, p?<?0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC?=?0·85?±?0·05), again outperforming prediction with generally available characteristics (AUC?=?0·70?±?0·04, p?<?0·05). INTERPRETATION:Using machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.