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 perfusion
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
2020
Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry
Klein E, Miller RJH, Sharir T, Einstein AJ, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Otaki Y, Gransar H, Liang JX, Dey D, Berman DS, Slomka PJ. Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry. Journal Of Nuclear Cardiology 2020, 29: 727-736. PMID: 32929639, PMCID: PMC8497048, DOI: 10.1007/s12350-020-02334-7.Peer-Reviewed Original ResearchConceptsBody mass indexTotal perfusion deficitStress total perfusion deficitMyocardial perfusion imagingSPECT myocardial perfusion imagingPrognostic accuracyObese populationMajor adverse cardiac event risksCox proportional hazards analysisDifferent obesity categoriesRobust risk stratificationCardiac event riskProportional hazards analysisHigher prognostic accuracyREFINE SPECT registrySoft tissue attenuationQuantitative perfusion analysisMACE riskObesity categoriesCardiovascular riskObese patientsAdjusted analysisMass indexRisk stratificationSignificant obesity