2023
Novel Radiotracers for Molecular Imaging of Myocardial Inflammation: an Update Focused on Clinical Translation of Non-18F-FDG Radiotracers
Shi T, Miller E. Novel Radiotracers for Molecular Imaging of Myocardial Inflammation: an Update Focused on Clinical Translation of Non-18F-FDG Radiotracers. Current Cardiovascular Imaging Reports 2023, 16: 1-9. PMID: 36926261, PMCID: PMC9996562, DOI: 10.1007/s12410-023-09574-4.Peer-Reviewed Original ResearchCardiac sarcoidosisMyocardial inflammatory diseaseDisease activityImmunomodulatory therapyInflammatory cardiomyopathyMyocardial inflammationInflammatory diseasesPrognostic performanceReviewThe purposeNovel radiotracersMyocarditisSarcoidosisTherapy guidanceInflammationMolecular featuresRadiotracerClinical translationMolecular imagingMolecular mechanismsHigh specificityCardiomyopathyImproved mechanistic insightsPathogenesisTherapyDisease
2022
PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients
Feher A, Miller EJ. PET Myocardial Blood Flow for Post-transplant Surveillance and Cardiac Allograft Vasculopathy in Heart Transplant Recipients. Current Cardiology Reports 2022, 24: 1865-1871. PMID: 36279035, DOI: 10.1007/s11886-022-01804-3.Peer-Reviewed Original ResearchConceptsHeart transplant recipientsAllograft vasculopathyTransplant recipientsPositron emission tomography myocardial perfusionOverall graft survivalPost-transplant surveillanceCardiac allograft vasculopathyHeart transplant patientsTomography myocardial perfusionRecent FindingsMultiple studiesMyocardial perfusion imagingMyocardial blood flowMBF quantificationGraft survivalHeart transplantationTransplant patientsTransplant rejectionSerial monitoringPrognostic performancePET myocardial blood flowBlood flowMyocardial perfusionPerfusion imagingMBF assessmentBlood flow quantification
2021
Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry
Rios R, Miller RJH, Hu LH, Otaki Y, Singh A, Diniz M, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, DiCarli M, Van Kriekinge S, Kavanagh P, Parekh T, Liang JX, Dey D, Berman DS, Slomka P. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovascular Research 2021, 118: 2152-2164. PMID: 34259870, PMCID: PMC9302886, DOI: 10.1093/cvr/cvab236.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsMyocardial perfusion imagingTotal perfusion deficitPrognostic accuracyREFINE SPECT registryImaging variablesTraditional multivariable modelsMultivariable modelStress total perfusion deficitAdverse cardiac eventsReceiver-operating characteristic curveOptimal risk stratificationTomography (SPECT) MPIComparable prognostic accuracyComparable prognostic performanceHigher prognostic accuracySingle photon emissionCardiovascular event predictionCardiac eventsRisk stratificationClinical variablesPerfusion deficitsPrognostic performancePerfusion imagingCollected variables