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 obesityPredicting risk of late age-related macular degeneration using deep learning
Peng Y, Keenan T, Chen Q, Agrón E, Allot A, Wong W, Chew E, Lu Z. Predicting risk of late age-related macular degeneration using deep learning. Npj Digital Medicine 2020, 3: 111. PMID: 32904246, PMCID: PMC7453007, DOI: 10.1038/s41746-020-00317-z.Peer-Reviewed Original ResearchLate age-related macular degenerationAge-related macular degenerationHigher prognostic accuracyClinical standardsMacular degenerationPrognostic accuracyIndependent cohortLargest longitudinal clinical trialsProbability of progressionSight-threatening stagesColor fundus photographsLongitudinal clinical trialsAMD patientsRetinal specialistsClinical trialsFundus photographsSpecialty clinicHigh riskClinical actionsSurvival analysisMedical interventionsIndividual riskAREDS2AREDSExternal validation
2014
Comparing 3-T multiparametric MRI and the Partin tables to predict organ-confined prostate cancer after radical prostatectomy
Gupta RT, Faridi KF, Singh AA, Passoni NM, Garcia-Reyes K, Madden JF, Polascik TJ. Comparing 3-T multiparametric MRI and the Partin tables to predict organ-confined prostate cancer after radical prostatectomy. Urologic Oncology Seminars And Original Investigations 2014, 32: 1292-1299. PMID: 24863013, DOI: 10.1016/j.urolonc.2014.04.017.Peer-Reviewed Original ResearchConceptsNegative predictive valuePositive predictive valueOrgan-confined prostate cancerExtracapsular extensionOC diseasePartin tablesRadical prostatectomyClinical stageProstate cancerPredictive valueMedian prostate-specific antigen levelProstate-specific antigen levelMultiparametric magnetic resonance imagingHigh negative predictive valueHigh positive predictive valueBiopsy Gleason scoreInstitutional review board approvalAvailable clinical parametersDigital rectal examinationSeminal vesicle invasionHigher prognostic accuracyProstate cancer treatmentReview board approvalMagnetic resonance imagingLogistic regression models
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