2024
The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).
Miller R, Lemley M, Shanbhag A, Ramirez G, Liang J, Builoff V, Kavanagh P, Sharir T, Hauser M, Ruddy T, Fish M, Bateman T, Acampa W, Einstein A, Dorbala S, Di Carli M, Feher A, Miller E, Sinusas A, Halcox J, Martins M, Kaufmann P, Dey D, Berman D, Slomka P. The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0). Journal Of Nuclear Medicine 2024, 65: 1795-1801. PMID: 39362762, PMCID: PMC11533915, DOI: 10.2967/jnumed.124.268292.Peer-Reviewed Original ResearchCoronary artery calciumCT attenuation correction imagesStress total perfusion deficitMyocardial perfusion imagingTotal perfusion deficitAttenuation correction imagesPerfusion imagingREFINE SPECTImprove prediction of adverse outcomesPerfusion deficitsSPECT myocardial perfusion imagingIncreased risk of MACEPredictive of adverse outcomesMedian follow-upInvasive coronary angiographyRisk of MACEAdverse cardiovascular eventsCAC scoreExperience MACECoronary angiographyArtery calciumInternational registryCardiovascular eventsFollow-upClinical data
2023
Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, Liang J, Huang C, Sharir T, Dorbala S, Di Carli M, Einstein A, Sinusas A, Miller E, Bateman T, Fish M, Ruddy T, Acampa W, Hauser M, Kaufmann P, Dey D, Berman D, Slomka P. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 50: 2656-2668. PMID: 37067586, PMCID: PMC10317876, DOI: 10.1007/s00259-023-06218-z.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingCause mortalityTotal perfusion deficitArtery diseaseRisk stratificationPerfusion deficitsHigher body mass indexTomography myocardial perfusion imagingMore diabetes mellitusSPECT myocardial perfusion imagingBetter risk stratificationRisk-stratify patientsBody mass indexREFINE SPECT registrySingle photon emissionDiabetes mellitusMass indexExternal cohortStress imagingMyocardial perfusionPatientsRisk phenotypePerfusion imagingImaging characteristics
2022
Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning
Singh A, Miller RJH, Otaki Y, Kavanagh P, Hauser MT, Tzolos E, Kwiecinski J, Van Kriekinge S, Wei CC, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Huang C, Han D, Dey D, Berman DS, Slomka PJ. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning. JACC Cardiovascular Imaging 2022, 16: 209-220. PMID: 36274041, PMCID: PMC10980287, DOI: 10.1016/j.jcmg.2022.07.017.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingTotal perfusion deficitNonfatal myocardial infarctionMyocardial infarctionPerfusion imagingTomography myocardial perfusion imagingIschemic total perfusion deficitStress total perfusion deficitTesting groupReceiver-operating characteristic curvePatient-level riskPrediction of deathSingle photon emissionLogistic regression modelsCause mortalityPrimary outcomeHighest quartileRisk stratificationAbnormal perfusionNormal perfusionPerfusion deficitsAdverse event predictionPrognostic accuracyHigh riskMyocardial perfusionMitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images
Miller RJH, Singh A, Otaki Y, Tamarappoo BK, Kavanagh P, Parekh T, Hu LH, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli MF, Liang JX, Dey D, Berman DS, Slomka PJ. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 387-397. PMID: 36194270, PMCID: PMC10042590, DOI: 10.1007/s00259-022-05972-w.Peer-Reviewed Original ResearchConceptsCoronary artery diseaseMyocardial perfusion imagingArtery diseaseInvasive angiographyObstructive coronary artery diseaseDisease probabilityLow-risk patientsLow-risk populationHigh-risk populationTotal perfusion deficitHigh diagnostic accuracyS-TPDPerfusion deficitsPatient managementPatientsPerfusion imagingDiagnostic accuracyPerfusion SPECT imagesLower likelihoodGood calibrationCharacteristic curveAngiographySPECT imagesSelection biasDiseaseExplainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.
Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, Kavanagh P, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Carli MD, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2022, 63: 1768-1774. PMID: 35512997, PMCID: PMC9635672, DOI: 10.2967/jnumed.121.263686.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingCoronary artery diseaseObstructive coronary artery diseasePhysician interpretationDiagnostic accuracyPerfusion imagingLeft main arteryOverall net reclassification improvementInvasive coronary angiographyNet reclassification improvementTotal perfusion deficitArtery diseaseCoronary angiographyMedian agePhysician diagnosisReclassification improvementPerfusion deficitsClinical historyCoronary segmentsRepresentative cohortMeaningful improvementsMain arteryPatientsDL resultsQuantitative perfusionPrevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry
Han D, Rozanski A, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Dey D, Berman DS, Slomka PJ. Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry. Journal Of Nuclear Cardiology 2022, 29: 3221-3232. PMID: 35174442, PMCID: PMC9378748, DOI: 10.1007/s12350-021-02829-x.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial ischemiaStress testingCAD risk factorsMulticenter international registryPredictor of ischemiaCardiac stress testingNuclear stress testingChi-square testConclusionThe prevalenceMulticenter registryOverall cohortTypical anginaCAD statusPerfusion deficitsInternational registryMale genderRisk factorsLVEFIschemiaPatientsImaging characteristicsPotent predictorPrevalenceRegistry
2021
Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Tzolos E, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Dey D, Berman DS, Slomka PJ. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry. Journal Of Nuclear Cardiology 2021, 29: 3003-3014. PMID: 34757571, PMCID: PMC9085969, DOI: 10.1007/s12350-021-02810-8.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsCoronary artery diseaseTotal perfusion deficitCardiac stress testingStress testingComparison of diabetesAdverse cardiovascular eventsStress test patientsCardiac stress test patientsREFINE SPECT registryDM statusMACE riskBackgroundDiabetes mellitusCardiovascular eventsArtery diseaseVentricular functionPrognostic predictorClinical variablesPerfusion deficitsChi-square analysisSPECT-MPIPatientsTest patientsPropensity scorePotent predictorPrognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry
Kuronuma K, Miller RJH, Otaki Y, Van Kriekinge SD, Diniz MA, Sharir T, Hu LH, Gransar H, Liang JX, Parekh T, Kavanagh PB, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry. Circulation Cardiovascular Imaging 2021, 14: e012386. PMID: 34281372, PMCID: PMC8978932, DOI: 10.1161/circimaging.120.012386.Peer-Reviewed Original ResearchMeSH KeywordsAgedCanadaCoronary CirculationDisease ProgressionFemaleHumansIncidenceIsraelMaleMiddle AgedMyocardial IschemiaMyocardial Perfusion ImagingPredictive Value of TestsPrognosisRegistriesRisk AssessmentRisk FactorsStroke VolumeTomography, Emission-Computed, Single-PhotonUnited StatesVentricular Function, LeftConceptsMajor adverse cardiac eventsVentricular ejection fractionTotal perfusion deficitSingle photon emissionAdverse cardiac eventsEjection fractionPerfusion deficitsCardiac eventsPrognostic valueTomography myocardial perfusion imagingVentricular ejection fraction assessmentHighest decile groupProportional hazards analysisTomography myocardial perfusionIndependent prognostic significanceIndependent prognostic valueLarge multinational registryEjection fraction assessmentMyocardial perfusion imagingREFINE SPECT registryAdditional radiation exposureConventional single-photon emissionMACE riskMACE rateMultinational registryDetermining 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 variablesClinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease
Otaki Y, Singh A, Kavanagh P, Miller RJH, Parekh T, Tamarappoo BK, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Cadet S, Liang JX, Dey D, Berman DS, Slomka PJ. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease. JACC Cardiovascular Imaging 2021, 15: 1091-1102. PMID: 34274267, PMCID: PMC9020794, DOI: 10.1016/j.jcmg.2021.04.030.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingStandard clinical softwareStress total perfusion deficitObstructive CADTomography myocardial perfusion imagingSPECT myocardial perfusion imagingStress myocardial perfusionCoronary artery diseaseReceiver-operating characteristic curveInvasive coronary angiographyClinical softwareReader diagnosisSingle photon emissionTypical clinical workflowArtery diseaseCoronary angiographyPerfusion deficitsDiagnostic findingsMyocardial perfusionPerfusion imagingVentricular volumeStandard clinical workstationPatientsDiagnostic accuracy
2020
Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia
Azadani PN, Miller RJH, Sharir T, Diniz MA, Hu LH, Otaki Y, Gransar H, Liang JX, Eisenberg E, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Impact of Early Revascularization on Major Adverse Cardiovascular Events in Relation to Automatically Quantified Ischemia. JACC Cardiovascular Imaging 2020, 14: 644-653. PMID: 32828784, PMCID: PMC7987223, DOI: 10.1016/j.jcmg.2020.05.039.Peer-Reviewed Original ResearchConceptsIschemic total perfusion deficitTotal perfusion deficitEarly revascularizationSPECT-MPIMajor adverse cardiovascular eventsPropensity scoreMultivariable Cox modelingAdverse cardiovascular eventsSingle-center dataContemporary cardiology practiceSingle photon emissionCardiovascular eventsPrimary outcomeCardiac deathConsecutive patientsCox modelingMulticenter studyPerfusion deficitsInternational registryRevascularizationCardiology practicePatientsIschemiaSignificant associationMACE
2019
Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry
Han D, Rozanski A, Gransar H, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Hu LH, Germano G, Dey D, Berman DS, Slomka PJ. Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry. Diabetes Care 2019, 43: 453-459. PMID: 31776140, PMCID: PMC6971784, DOI: 10.2337/dc19-1360.Peer-Reviewed Original ResearchMeSH KeywordsAgedAngina, UnstableCohort StudiesCoronary Artery DiseaseDiabetes MellitusDiabetic AngiopathiesFemaleHumansMaleMiddle AgedMyocardial InfarctionMyocardial IschemiaMyocardial Perfusion ImagingPrevalencePrognosisPropensity ScoreRegistriesRisk FactorsTomography, Emission-Computed, Single-PhotonConceptsMajor adverse cardiovascular eventsTotal perfusion deficitMACE riskMyocardial ischemic burdenAdverse cardiovascular eventsTomography myocardial perfusionREFINE SPECT registrySingle photon emissionIschemic burdenMinimal ischemiaCardiovascular eventsCause mortalityLate revascularizationPrognostic impactUnstable anginaSignificant ischemiaPerfusion deficitsMyocardial infarctionMyocardial ischemiaRisk factorsCardiovascular diseaseHigh riskMyocardial perfusionPatientsDiabetesTransient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry.
Miller RJH, Hu LH, Gransar H, Betancur J, Eisenberg E, Otaki Y, Sharir T, Fish MB, Ruddy TD, Dorbala S, Carli MD, Einstein AJ, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman T, Germano G, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Transient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019, 21: 567-575. PMID: 31302679, PMCID: PMC7167750, DOI: 10.1093/ehjci/jez172.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsTransient ischemic dilationWall motion abnormalitiesModerate ischaemiaMyocardial perfusion imagingMild ischaemiaIschemic dilationMultivariable Cox proportional hazards analysisHigh-risk imaging featuresCox proportional hazards analysisTomography myocardial perfusion imagingAdverse cardiovascular eventsHigh-risk featuresGroup of patientsProportional hazards analysisIncremental prognostic utilityTotal perfusion deficitREFINE SPECT registrySingle photon emissionMedian followCardiovascular eventsCardiovascular riskMultivariable analysisPrognostic utilityPerfusion deficits5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects Results From REFINE SPECT
Otaki Y, Betancur J, Sharir T, Hu LH, Gransar H, Liang JX, Azadani PN, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Tamarappoo BK, Germano G, Dey D, Berman DS, Slomka PJ. 5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects Results From REFINE SPECT. JACC Cardiovascular Imaging 2019, 13: 774-785. PMID: 31202740, PMCID: PMC6899217, DOI: 10.1016/j.jcmg.2019.02.028.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsTotal perfusion deficitStress total perfusion deficitMACE rateHazard ratioRate of MACECox proportional hazards analysisTomography myocardial perfusion imagingAdjusted hazard ratioAdverse cardiac eventsNonfatal myocardial infarctionProportional hazards analysisMyocardial perfusion imagingSingle photon emissionCardiac eventsLate revascularizationUnstable anginaRisk stratificationPrognostic valueKaplan-MeierPerfusion abnormalitiesPerfusion deficitsMyocardial infarctionPrognostic studiesMyocardial perfusion
2018
Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
Betancur J, Hu LH, Commandeur F, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Otaki Y, Liang JX, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study. Journal Of Nuclear Medicine 2018, 60: 664-670. PMID: 30262516, PMCID: PMC6495237, DOI: 10.2967/jnumed.118.213538.Peer-Reviewed Original ResearchConceptsTotal perfusion deficitMyocardial perfusion imagingSPECT myocardial perfusion imagingCoronary artery diseaseObstructive diseaseClinical readsArtery diseaseCoronary arteryPerfusion imagingTc-sestamibi myocardial perfusion imagingObstructive coronary artery diseaseLeft main coronary arteryStress myocardial perfusion imagingStress total perfusion deficitMain coronary arteryMajor coronary arteriesLeft ventricular myocardiumHypoperfusion severityRadiotracer countsMulticenter studyPerfusion deficitsNormal limitsVessel sensitivityPatient sensitivityPatientsDeep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study
Betancur J, Commandeur F, Motlagh M, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study. JACC Cardiovascular Imaging 2018, 11: 1654-1663. PMID: 29550305, PMCID: PMC6135711, DOI: 10.1016/j.jcmg.2018.01.020.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overCoronary CirculationCoronary StenosisDeep LearningFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedMyocardial Perfusion ImagingOrganophosphorus CompoundsOrganotechnetium CompoundsPredictive Value of TestsRadiopharmaceuticalsRegistriesTechnetium Tc 99m SestamibiTomography, Emission-Computed, Single-PhotonConceptsTotal perfusion deficitMyocardial perfusion imagingCoronary artery diseaseObstructive diseaseArtery diseaseVessel coronary artery diseaseTomography myocardial perfusion imagingTetrofosmin myocardial perfusion imagingLarge multicenter populationStress total perfusion deficitReceiver-operating characteristic curveInvasive coronary angiographyLeft ventricular myocardiumSingle photon emissionMyocardial perfusion SPECTMonths of MPIMulticenter populationObstructive stenosisCoronary angiographyMulticenter studyCoronary arteryPerfusion deficitsNormal limitsVessel sensitivityCurrent clinical methods