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 dataArtificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging
Feher A, Bednarski B, Miller R, Shanbhag A, Lemley M, Miras L, Sinusas A, Miller E, Slomka P. Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging. Journal Of Nuclear Medicine 2024, 65: jnumed.123.266761. PMID: 38548351, PMCID: PMC11064832, DOI: 10.2967/jnumed.123.266761.Peer-Reviewed Original ResearchConceptsMyocardial perfusion imagingHF hospitalizationHeart failureStress left ventricular ejection fractionPerfusion imagingHF exacerbationPredictive of HF hospitalizationsSPECT/CT myocardial perfusion imagingMyocardial perfusionInternational cohortAcute heart failure exacerbationMedian follow-upVentricular ejection fractionReceiver-operating-characteristic curveClinical risk factorsHeart failure exacerbationExternal validation cohortAcute HF exacerbationPrevent HF hospitalizationsImaging parametersCalcium scoreEjection fractionClinical parametersCT scanValidation cohortAI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Kavanagh P, Feher A, Miller E, Bateman T, Builoff V, Liang J, Newby D, Dey D, Berman D, Slomka P. AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovascular Imaging 2024, 17: 780-791. PMID: 38456877, PMCID: PMC11222053, DOI: 10.1016/j.jcmg.2024.01.006.Peer-Reviewed Original ResearchComputed tomography attenuation correctionMajor adverse cardiovascular eventsCoronary artery calciumLV massMyocardial perfusion imagingPerfusion imagingSingle-photon emission computed tomography/computed tomographyIncreased risk of MACEAssociated with major adverse cardiovascular eventsRisk of Major Adverse Cardiovascular EventsCardiac chamber volumesMedian follow-upCT-based volumesRight ventricular volumesImprove cardiovascular risk assessmentAdverse cardiovascular eventsQuartile of LV massContinuous net reclassification indexCoronary artery diseaseNet reclassification indexCardiovascular risk assessmentTomography/computed tomographyConsecutive patientsImprove risk predictionPrognostic utilityImpact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography
Randazzo M, Elias P, Poterucha T, Sharir T, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Castillo M, Liang J, Miller R, Dey D, Berman D, Slomka P, Einstein A. Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography. European Heart Journal - Cardiovascular Imaging 2024, 25: 996-1006. PMID: 38445511, PMCID: PMC11210974, DOI: 10.1093/ehjci/jeae055.Peer-Reviewed Original ResearchSPECT myocardial perfusion imagingMyocardial perfusion imagingCoronary artery diseaseCardiac sizeDetect obstructive coronary artery diseasePerfusion imagingObstructive coronary artery diseaseDetection of coronary artery diseaseNegative predictive valueLeft ventricular volumeSolid-state scannersSmall cardiac sizeCoronary angiographyMale patientsYounger patientsElderly patientsMale sexDiagnostic performanceVentricular volumeArtery diseaseVisual assessmentPatientsPredictive valueElderly ageSPECTAI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Miller R, Shanbhag A, Killekar A, Lemley M, Bednarski B, Van Kriekinge S, Kavanagh P, Feher A, Miller E, Einstein A, Ruddy T, Liang J, Builoff V, Berman D, Dey D, Slomka P. AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. Npj Digital Medicine 2024, 7: 24. PMID: 38310123, PMCID: PMC10838293, DOI: 10.1038/s41746-024-01020-z.Peer-Reviewed Original ResearchEpicardial adipose tissueMyocardial infarctionPerfusion imagingEpicardial adipose tissue measurementsEpicardial adipose tissue volumeEAT attenuationMedian follow-upIncreased risk of deathEpicardial fat measurementMyocardial perfusion imagingAssociated with cardiovascular riskCoronary artery diseaseAssociated with deathEating measuresRisk of deathEAT volumeLow-dosePrognostic insightsFollow-upCardiovascular riskCardiovascular risk predictionUngated CTArtery diseaseIncreased riskCardiac silhouetteClinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study
Miller R, Bednarski B, Pieszko K, Kwiecinski J, Williams M, Shanbhag A, Liang J, Huang C, Sharir T, Hauser M, Dorbala S, Di Carli M, Fish M, Ruddy T, Bateman T, Einstein A, Kaufmann P, Miller E, Sinusas A, Acampa W, Han D, Dey D, Berman D, Slomka P. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study. EBioMedicine 2024, 99: 104930. PMID: 38168587, PMCID: PMC10794922, DOI: 10.1016/j.ebiom.2023.104930.Peer-Reviewed Original ResearchMyocardial infarctionMyocardial perfusion imagingBlood InstituteNational HeartPharmacologic stressExternal testing cohortNormal imaging resultsRetrospective observational studyCoronary artery diseasePrevious myocardial infarctionRisk of deathNormal perfusion scanBritish Heart FoundationNational InstituteCluster 4 patientsDistinct phenotypesCardiovascular riskArtery diseaseRisk stratificationPerfusion scanNormal perfusionImaging featuresNormal scansMPI patientsHeart Foundation
2023
CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients
Feher A, Pieszko K, Shanbhag A, Lemley M, Bednarski B, Miller R, Huang C, Miras L, Liu Y, Sinusas A, Slomka P, Miller E. CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 51: 695-706. PMID: 37924340, DOI: 10.1007/s00259-023-06484-x.Peer-Reviewed Original ResearchMajor adverse cardiac eventsMyocardial perfusion imagingSPECT myocardial perfusion imagingMACE-free survivalObese patientsPrognostic valueSignificant incremental prognostic valueYale-New Haven HospitalLate coronary revascularizationStress total perfusion deficitAdverse cardiac eventsIncremental prognostic valueNonfatal myocardial infarctionComposite end pointCT myocardial perfusion imagingSuperior prognostic valueSPECT/CT myocardial perfusion imagingTotal perfusion deficitNew Haven HospitalCT attenuation correctionHighest ROC areaStress TPDCoronary revascularizationCardiac eventsPatient populationComparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging
Feher A, Pieszko K, Shanbhag A, Lemley M, Miller R, Huang C, Miras L, Liu Y, Gerber J, Sinusas A, Miller E, Slomka P. Comparison of the prognostic value between quantification and visual estimation of coronary calcification from attenuation CT in patients undergoing SPECT myocardial perfusion imaging. The International Journal Of Cardiovascular Imaging 2023, 40: 185-193. PMID: 37845406, PMCID: PMC466934, DOI: 10.1007/s10554-023-02980-1.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsCoronary artery calcificationMyocardial perfusion imagingCT myocardial perfusion imagingSPECT/CT myocardial perfusion imagingArtery calcificationPrognostic valuePrior coronary stentingAdverse cardiovascular eventsSimilar prognostic valueSPECT myocardial perfusionREFINE SPECT registrySPECT/CTCardiovascular eventsCAC scoringCalcium scoreCoronary calcificationCoronary stentingPrognostic utilitySingle centerCoronary arteryLow doseMyocardial perfusionPerfusion imagingSurvival analysisTime and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging
Pieszko K, Shanbhag A, Singh A, Hauser M, Miller R, Liang J, Motwani M, Kwieciński J, Sharir T, Einstein A, Fish M, Ruddy T, Kaufmann P, Sinusas A, Miller E, Bateman T, Dorbala S, Di Carli M, Berman D, Dey D, Slomka P. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging. Npj Digital Medicine 2023, 6: 78. PMID: 37127660, PMCID: PMC10151323, DOI: 10.1038/s41746-023-00806-x.Peer-Reviewed Original ResearchAcute coronary syndromeMajor adverse cardiovascular eventsMyocardial perfusion imagingCause deathAdverse cardiovascular eventsModifiable risk factorsPrediction of deathPersonalized risk assessmentCardiovascular eventsCoronary syndromeClinical featuresPrognostic valueRisk factorsExternal cohortStandard clinical interpretationPerfusion imagingAbnormality measuresCardiac perfusionTime pointsPatients' explanationsClinical interpretationExternal validationDeathPatientsRisk assessmentUnsupervised 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 characteristicsIN PATIENTS UNDERGOING NUCLEAR MYOCARDIAL PERFUSION IMAGING, RHEUMATOID ARTHRITIS IS ASSOCIATED WITH INCREASED RISK OF ADVERSE CARDIOVASCULAR EVENTS INDEPENDENT OF MYOCARDIAL ISCHEMIA
Pires J, Oikonomou E, Agarwal R, Liu Y, miller E, Sinusas A, Feher A. IN PATIENTS UNDERGOING NUCLEAR MYOCARDIAL PERFUSION IMAGING, RHEUMATOID ARTHRITIS IS ASSOCIATED WITH INCREASED RISK OF ADVERSE CARDIOVASCULAR EVENTS INDEPENDENT OF MYOCARDIAL ISCHEMIA. Journal Of The American College Of Cardiology 2023, 81: 1467. DOI: 10.1016/s0735-1097(23)01911-3.Peer-Reviewed Original Research
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 quantificationDirect 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 biasDiseaseIntegration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging
Feher A, Pieszko K, Miller R, Lemley M, Shanbhag A, Huang C, Miras L, Liu YH, Sinusas AJ, Miller EJ, Slomka PJ. Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging. Journal Of Nuclear Cardiology 2022, 30: 590-603. PMID: 36195826, DOI: 10.1007/s12350-022-03099-x.Peer-Reviewed Original ResearchConceptsMajor adverse cardiovascular eventsMyocardial perfusion imagingAdverse cardiovascular eventsSPECT myocardial perfusion imagingCAC scoringCardiovascular eventsPrediction of MACECoronary artery calcification (CAC) scoringMACE-free survivalClinical risk factorsCoronary artery calciumCT myocardial perfusion imagingReceiver operator characteristic curveSPECT/CT myocardial perfusion imagingSPECT/CTOperator characteristic curveCT myocardial perfusionArtery calciumCAC scoreAnalysis patientsMACE predictionSingle centerHigher event ratesRisk factorsRisk scoreUnsupervised machine learning improves risk stratification of patients with visual normal SPECT myocardial perfusion imaging assessments
Bednarski B, Williams M, Pieszko K, Miller R, Huang C, Kwiecinski J, Sharir T, Di Carli M, Fish M, Ruddy T, Hasuer T, Miller E, Acampa W, Berman D, Slomka P. Unsupervised machine learning improves risk stratification of patients with visual normal SPECT myocardial perfusion imaging assessments. European Heart Journal 2022, 43: ehac544.300. DOI: 10.1093/eurheartj/ehac544.300.Peer-Reviewed Original ResearchMajor adverse cardiac eventsPeak systolic blood pressureSystolic blood pressureHigh-risk clustersRisk stratificationMyocardial perfusion imagingNormal perfusionHazard ratioBlood pressureCox proportional hazards analysisHigher left ventricular massHigher body mass indexTomography myocardial perfusion imagingNormal clinical assessmentRisk-stratified subgroupsAdverse cardiac eventsPrevalence of diabetesProportional hazards analysisBetter risk stratificationCoronary artery diseaseImproved risk stratificationManagement of patientsKaplan-Meier curvesBody mass indexLeft ventricular massDifferences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study
Tamarappoo BK, Otaki Y, Sharir T, Hu LH, Gransar H, Einstein AJ, Fish MB, Ruddy TD, Kaufmann P, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Eisenberg E, Liang JX, Dey D, Berman DS, Slomka PJ. Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study. Circulation Cardiovascular Imaging 2022, 15: e012741. PMID: 35727872, PMCID: PMC9307118, DOI: 10.1161/circimaging.121.012741.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsTotal perfusion defectPrognostic valuePerfusion defectsSingle photon emissionLarge international multicenter studyMyocardial perfusion single-photon emissionPerfusion single-photon emissionStress SPECT myocardial perfusion imagingSPECT myocardial perfusion imagingAdverse cardiac eventsMACE-free survivalMultivariable Cox modelSingle-center studyGreater prognostic valueInternational multicenter studyProportional hazards modelMyocardial perfusion defectsMyocardial perfusion imagingREFINE SPECT registryConventional single-photon emissionCardiac eventsHazard ratioEjection fractionMulticenter studyExplainable 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 perfusion
2021
Prognostic 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 variables