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Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.

ABSTRACT: Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95]; P<0.001; automated: HR, 0.94 [95% CI, 0.92-0.96]; P<0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001; automated: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P=0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, -9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77-0.93]) of manual and automated volumetric assessments. Conclusions User-independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.

SUBMITTER: Schuster A

PROVIDER: S-EPMC7726968 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.

Schuster Andreas A Lange Torben T Backhaus Sören J SJ Strohmeyer Carolin C Boom Patricia C PC Matz Jonas J Kowallick Johannes T JT Lotz Joachim J Steinmetz Michael M Kutty Shelby S Bigalke Boris B Gutberlet Matthias M de Waha-Thiele Suzanne S Desch Steffen S Hasenfuß Gerd G Thiele Holger H Stiermaier Thomas T Eitel Ingo I

Journal of the American Heart Association 20200902 18

Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic reson ...[more]

PMID: 32873121

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Project description:BackgroundCardiac magnetic resonance myocardial feature tracking (CMR-FT)-derived global strain assessments provide incremental prognostic information in patients following acute myocardial infarction (AMI). Functional analyses of the remote myocardium (RM) are scarce and whether they provide an additional prognostic value in these patients is unknown.Methods1034 patients following acute myocardial infarction were included. CMR imaging and strain analyses as well as infarct size quantification were performed after reperfusion by primary percutaneous coronary intervention. The occurrence of major adverse cardiac events (MACE) within 12 months after the index event was defined as primary clinical endpoint.ResultsPatients with MACE had significantly lower RM circumferential strain (CS) compared to those without MACE. A cutoff value for RM CS of - 25.8% best identified high-risk patients (p < 0.001 on log-rank testing) and impaired RM CS was a strong predictor of MACE (HR 1.05, 95% CI 1.07-1.14, p = 0.003). RM CS provided further risk stratification among patients considered at risk according to established CMR parameters for (1) patients with reduced left ventricular ejection fraction (LVEF) ≤ 35% (p = 0.038 on log-rank testing), (2) patients with reduced global circumferential strain (GCS) > - 18.3% (p = 0.015 on log-rank testing), and (3) patients with large microvascular obstruction ≥ 1.46% (p = 0.002 on log-rank testing).ConclusionCMR-FT-derived RM CS is a useful parameter to characterize the response of the remote myocardium and allows improved stratification following AMI beyond commonly used parameters, especially of high-risk patients.Trial registrationClinicalTrials.gov, NCT00712101 and NCT01612312 Defining remote segments (R) in the presence of infarct areas (I) for the analysis of remote circumferential strain (CS). Remote CS was significantly lower in patients who suffered major adverse cardiac events (MACE) and a cutoff value for remote CS of - 25.8% best identified high-risk patients. In addition, impaired remote CS ≥ - 25.8 % (Remote -) and preserved remote CS < - 25.8 % (Remote +) enabled further risk stratification when added to established parameters like left ventricular ejection fraction (LVEF), global circumferential strain (GCS) or microvascular obstruction (MVO).

Project description:BackgroundPreclinical data suggest that an acute inflammatory response following myocardial infarction (MI) accelerates systemic atherosclerosis. Using combined positron emission and computed tomography, we investigated whether this phenomenon occurs in humans.Methods and resultsOverall, 40 patients with MI and 40 with stable angina underwent thoracic 18F-fluorodeoxyglucose combined positron emission and computed tomography scan. Radiotracer uptake was measured in aortic atheroma and nonvascular tissue (paraspinal muscle). In 1003 patients enrolled in the Global Registry of Acute Coronary Events, we assessed whether infarct size predicted early (≤30 days) and late (>30 days) recurrent coronary events. Compared with patients with stable angina, patients with MI had higher aortic 18F-fluorodeoxyglucose uptake (tissue-to-background ratio 2.15±0.30 versus 1.84±0.18, P<0.0001) and plasma C-reactive protein concentrations (6.50 [2.00 to 12.75] versus 2.00 [0.50 to 4.00] mg/dL, P=0.0005) despite having similar aortic (P=0.12) and less coronary (P=0.006) atherosclerotic burden and similar paraspinal muscular 18F-fluorodeoxyglucose uptake (P=0.52). Patients with ST-segment elevation MI had larger infarcts (peak plasma troponin 32 300 [10 200 to >50 000] versus 3800 [1000 to 9200] ng/L, P<0.0001) and greater aortic 18F-fluorodeoxyglucose uptake (2.24±0.32 versus 2.02±0.21, P=0.03) than those with non-ST-segment elevation MI. Peak plasma troponin concentrations correlated with aortic 18F-fluorodeoxyglucose uptake (r=0.43, P=0.01) and, on multivariate analysis, independently predicted early (tertile 3 versus tertile 1: relative risk 4.40 [95% CI 1.90 to 10.19], P=0.001), but not late, recurrent MI.ConclusionsThe presence and extent of MI is associated with increased aortic atherosclerotic inflammation and early recurrent MI. This finding supports the hypothesis that acute MI exacerbates systemic atherosclerotic inflammation and remote plaque destabilization: MI begets MI.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifier: NCT01749254.

Project description:ObjectivesThe authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients.BackgroundFully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming.MethodsCMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as ≥70% stenosis. Nonsignificant CAD was defined by: 1) QCA as <70% stenosis; or 2) coronary computed tomography angiography as <30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR.ResultsThe correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good.ConclusionsFully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170).

Dataset Information

Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.

Publications

Fully Automated Cardiac Assessment for Diagnostic and Prognostic Stratification Following Myocardial Infarction.

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