2023
Unsupervised 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 characteristicsThe Association Between Body Mass Index And Functional Status In Minority Patients With Heart Failure With Preserved Ejection Fraction: The SCAN-MP Study
Driggin E, Rosenblum H, Teruya S, Rodriguez C, Kurian D, Raiszadeh F, Fine D, Sabogal N, Edmiston J, Massillon D, Winburn M, Miller E, Ruberg F, Maurer M. The Association Between Body Mass Index And Functional Status In Minority Patients With Heart Failure With Preserved Ejection Fraction: The SCAN-MP Study. Journal Of Cardiac Failure 2023, 29: 668-669. DOI: 10.1016/j.cardfail.2022.10.299.Peer-Reviewed Original ResearchBody mass indexKCCQ overall summary scoreExcess fat massFunctional statusOverall summary scoreObesity statusQuality of lifeMinority patientsFunctional capacityObese patientsEjection fractionHeart failureIndependent predictorsMass indexSPPB scoreFat massCardiac amyloidosisSummary scoresMuscle massShort Physical Performance Battery scoreFirst heart failure hospitalizationMedian body mass indexPhysical Performance Battery scoreHigher body mass indexWorse SPPB score
2022
Unsupervised 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 mass
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
2014
Preclinical Left Ventricular Diastolic Dysfunction in Metabolic Syndrome
Ayalon N, Gopal DM, Mooney DM, Simonetti JS, Grossman JR, Dwivedi A, Donohue C, Perez AJ, Downing J, Gokce N, Miller EJ, Liang CS, Apovian CM, Colucci WS, Ho JE. Preclinical Left Ventricular Diastolic Dysfunction in Metabolic Syndrome. The American Journal Of Cardiology 2014, 114: 838-842. PMID: 25084691, PMCID: PMC4162746, DOI: 10.1016/j.amjcard.2014.06.013.Peer-Reviewed Original ResearchConceptsHigher LA diameterLV diastolic dysfunctionVentricular diastolic dysfunctionDiastolic dysfunctionMetabolic syndromeLV massLA diameterLV hypertrophyYounger ageEarly risk factor modificationHigher left atrial diameterLeft ventricular diastolic dysfunctionAntihypertensive medication useLeft atrial diameterRisk factor modificationGender-adjusted analysesHigher LV massBody mass indexTissue Doppler imagingAtrial diameterDiastolic functionBlood pressureMedication useMass indexFactor modification