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
2020
Survival Following Implantable Cardioverter‐Defibrillator Implantation in Patients With Amyloid Cardiomyopathy
Higgins AY, Annapureddy AR, Wang Y, Minges KE, Lampert R, Rosenfeld LE, Jacoby DL, Curtis JP, Miller EJ, Freeman JV. Survival Following Implantable Cardioverter‐Defibrillator Implantation in Patients With Amyloid Cardiomyopathy. Journal Of The American Heart Association 2020, 9: e016038. PMID: 32867553, PMCID: PMC7726970, DOI: 10.1161/jaha.120.016038.Peer-Reviewed Original ResearchConceptsImplantable cardioverter defibrillator implantationCardioverter-defibrillator implantationNonischemic cardiomyopathyCardiac amyloidosisDiabetes mellitusCerebrovascular diseaseVentricular tachycardiaMultivariable Cox proportional hazards regression modelsCox proportional hazards regression modelProportional hazards regression modelsKaplan-Meier survival curvesCox proportional hazards modelPropensity-matched cohortOutcomes of patientsHazards regression modelsProportional hazards modelCause mortalityICD implantationRenal functionMultivariable analysisConclusions MortalityRisk factorsRegistry dataAmyloid cardiomyopathyHigh risk
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 perfusionPatientsDiabetes