2024
Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging
Shanbhag A, Miller R, Killekar A, Lemley M, Bednarski B, Van Kriekinge S, Kavanagh P, Feher A, Miller E, Bateman T, Liang J, Builoff V, Berman D, Dey D, Slomka P. Deep learning-based epicardial adipose tissue measurement, maximizing prognostic information from attenuation correction imaging. Progress In Biomedical Optics And Imaging 2024, 12930: 129300b-129300b-6. DOI: 10.1117/12.3007914.Peer-Reviewed Original ResearchEpicardial adipose tissueUngated CTMyocardial infarctionEAT volumeRisk stratificationComputed tomographyEAT volume measurementEpicardial adipose tissue measurementsEpicardial adipose tissue volumeAssociated with risk of cardiovascular eventsRisk of cardiovascular eventsMedian follow-upIncreased risk of deathGated computed tomographyImprove risk stratificationAdipose tissue measurementsAssociated with riskRisk of deathAttenuation correction imagesGated CTChest CTNo significant differencePrognostic informationCardiovascular eventsFollow-upAI-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
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
A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variables
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 perfusionPatientsDiabetes5-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