2022
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
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 scoreMachine learning to predict abnormal myocardial perfusion from pre-test features
Miller RJH, Hauser MT, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Huang C, Liang JX, Han D, Dey D, Berman DS, Slomka PJ. Machine learning to predict abnormal myocardial perfusion from pre-test features. Journal Of Nuclear Cardiology 2022, 29: 2393-2403. PMID: 35672567, PMCID: PMC9588501, DOI: 10.1007/s12350-022-03012-6.Peer-Reviewed Original ResearchMeSH KeywordsHumansMachine LearningMyocardial Perfusion ImagingPerfusionROC CurveTomography, Emission-Computed, Single-PhotonConceptsAbnormal myocardial perfusionAbnormal perfusionMyocardial perfusionDiamond-Forrester modelCAD consortiumConsecutive patientsInternational registryPre-test informationSPECT-MPIClinical informationPhysician's decisionPatientsPerfusionTesting populationExpert visual interpretationRegistryPopulationMethodsWePhysiciansHandling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry
Rios R, Miller RJH, Manral N, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Van Kriekinge SD, Kavanagh PB, Parekh T, Liang JX, Dey D, Berman DS, Slomka PJ. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers In Biology And Medicine 2022, 145: 105449. PMID: 35381453, PMCID: PMC9117456, DOI: 10.1016/j.compbiomed.2022.105449.Peer-Reviewed Original Research
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 variablesDiagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT
Eisenberg E, Miller RJH, Hu LH, Rios R, Betancur J, Azadani P, Han D, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. Journal Of Nuclear Cardiology 2021, 29: 2295-2307. PMID: 34228341, PMCID: PMC9020793, DOI: 10.1007/s12350-021-02698-4.Peer-Reviewed Original ResearchConceptsObstructive coronary artery diseaseCoronary artery diseaseHigh-risk coronary artery diseaseMyocardial perfusion imagingPatient selection algorithmTriple-vessel coronary artery diseasePrediction of CADML thresholdsStress-first protocolInvasive coronary angiographyReceiver operator characteristic curveOperator characteristic curveMyocardial perfusion SPECTArtery diseaseCoronary angiographyAnterior descendingClinical variablesClinical algorithmReader diagnosisRest imagingPerfusion SPECTPerfusion imagingDiagnostic safetyRadiation doseCharacteristic curve
2020
Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT
Hu LH, Miller RJH, Sharir T, Commandeur F, Rios R, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Liang JX, Eisenberg E, Dey D, Berman DS, Slomka PJ. Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. European Heart Journal - Cardiovascular Imaging 2020, 22: 705-714. PMID: 32533137, DOI: 10.1093/ehjci/jeaa134.Peer-Reviewed Original ResearchConceptsMajor adverse cardiac eventsPhysician interpretationMACE rateCancellation rateTomography myocardial perfusion imagingAdverse cardiac eventsInternational multicentre registryCause mortality ratesMyocardial perfusion imagingCurrent clinical approachesSingle photon emissionMulticentre registryCardiac eventsClinical dataMortality ratePerfusion imagingClinical approachPatientsML thresholdsRadiation exposureMPI scansPhoton emissionML scoreMLSafety
2019
Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.
Hu LH, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Commandeur F, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019, 21: 549-559. PMID: 31317178, PMCID: PMC7167744, DOI: 10.1093/ehjci/jez177.Peer-Reviewed Original ResearchConceptsEarly coronary revascularizationMyocardial perfusion imagingStress TPDCoronary revascularizationTomography myocardial perfusion imagingTetrofosmin myocardial perfusion imagingSPECT myocardial perfusion imagingInvasive coronary angiographyReceiver operator characteristic curveREFINE SPECT registryPatient-specific explanationsOperator characteristic curveSingle photon emissionMyocardial perfusion SPECTCoronary angiographyIndividual patientsImaging variablesPatientsPerfusion SPECTPerfusion imagingClinical settingCharacteristic curveRevascularizationExpert interpretationStress test
2018
Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)
Slomka PJ, Betancur J, Liang JX, Otaki Y, Hu LH, Sharir T, Dorbala S, Di Carli M, Fish MB, Ruddy TD, Bateman TM, Einstein AJ, Kaufmann PA, Miller EJ, Sinusas AJ, Azadani PN, Gransar H, Tamarappoo BK, Dey D, Berman DS, Germano G. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). Journal Of Nuclear Cardiology 2018, 27: 1010-1021. PMID: 29923104, PMCID: PMC6301135, DOI: 10.1007/s12350-018-1326-4.Peer-Reviewed Original ResearchMeSH KeywordsAgedArtificial IntelligenceAutomationCoronary AngiographyCoronary Artery DiseaseData CollectionDatabases, FactualFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedMachine LearningMaleMiddle AgedMyocardial Perfusion ImagingPrognosisRegistriesReproducibility of ResultsSoftwareTomography, Emission-Computed, Single-PhotonConceptsMyocardial perfusion imagingPharmacologic stressMajor adverse cardiac eventsAdverse cardiac eventsRevascularization resultsMulticenter registryCardiac eventsClinical variablesPrognostic outcomesResultsTo dateClinical dataPrognostic dataDiagnostic cohortSPECT-MPIImaging variablesMyocardial perfusionPrognostic cohortPerfusion imagingPatientsRegistryArtificial intelligence toolsNew artificial intelligence toolsQuantitative softwareCohortClinical image database