2022
Survival analysis of localized prostate cancer with deep learning
Dai X, Park JH, Yoo S, D’Imperio N, McMahon BH, Rentsch CT, Tate JP, Justice AC. Survival analysis of localized prostate cancer with deep learning. Scientific Reports 2022, 12: 17821. PMID: 36280773, PMCID: PMC9592586, DOI: 10.1038/s41598-022-22118-y.Peer-Reviewed Original ResearchConceptsProstate cancer mortalityComposite outcomeCancer mortalityRisk predictionTime-dependent c-statisticsProstate-specific antigen (PSA) testLarge integrated healthcare systemLocalized prostate cancerElectronic health record dataClinical decision-making processProstate cancer patientsIntegrated healthcare systemProstate Cancer Risk PredictionHealth record dataLarge-scale electronic health record dataRisk prediction modelCancer risk predictionAntigen testC-statisticCancer patientsProstate cancerClinical decision systemSurvival analysisVeterans AffairsDeep learning
2020
DNA methylation biomarker selected by an ensemble machine learning approach predicts mortality risk in an HIV-positive veteran population
Shu C, Justice AC, Zhang X, Marconi VC, Hancock DB, Johnson EO, Xu K. DNA methylation biomarker selected by an ensemble machine learning approach predicts mortality risk in an HIV-positive veteran population. Epigenetics 2020, 16: 741-753. PMID: 33092459, PMCID: PMC8216205, DOI: 10.1080/15592294.2020.1824097.Peer-Reviewed Original ResearchConceptsVeterans Aging Cohort StudyMortality risk groupsAging Cohort StudyHigher mortality riskMortality riskRisk groupsCohort studyHigh mortality risk groupLow-mortality risk groupsInflammation response pathwayHIV-positive participantsHuman immunodeficiency virusLow-risk groupImproved life expectancyVACS IndexHIV populationNatural killerImmunodeficiency virusPredictive biomarkersPLWHDNA methylation biomarkersVeteran populationSurvival analysisEpigenome-wide associationHIV
2015
Quality of HIV Care and Mortality Rates in HIV-Infected Patients
Korthuis PT, McGinnis KA, Kraemer KL, Gordon AJ, Skanderson M, Justice AC, Crystal S, Goetz MB, Gibert CL, Rimland D, Fiellin LE, Gaither JR, Wang K, Asch SM, McInnes DK, Ohl ME, Bryant K, Tate JP, Duggal M, Fiellin DA. Quality of HIV Care and Mortality Rates in HIV-Infected Patients. Clinical Infectious Diseases 2015, 62: 233-239. PMID: 26338783, PMCID: PMC4690479, DOI: 10.1093/cid/civ762.Peer-Reviewed Original ResearchConceptsMortality rateIllicit drug useDrug useSurvival analysisDisease severityNational Death Index recordsVeterans Aging Cohort StudyKaplan-Meier survival analysisCox proportional hazards modelAge-adjusted mortality ratesPast-year illicit drug useAging Cohort StudyOverall mortality rateHuman immunodeficiency virusVeterans Health AdministrationUnhealthy alcohol useProportional hazards modelLower age-adjusted mortality ratesHigh-quality careLongitudinal survival analysisHIV qualityUnhealthy alcoholHIV careCohort studyImproved survival
2014
An Adapted Frailty-Related Phenotype and the VACS Index as Predictors of Hospitalization and Mortality in HIV-Infected and Uninfected Individuals
Akgün KM, Tate JP, Crothers K, Crystal S, Leaf DA, Womack J, Brown TT, Justice AC, Oursler KK. An Adapted Frailty-Related Phenotype and the VACS Index as Predictors of Hospitalization and Mortality in HIV-Infected and Uninfected Individuals. JAIDS Journal Of Acquired Immune Deficiency Syndromes 2014, 67: 397-404. PMID: 25202921, PMCID: PMC4213242, DOI: 10.1097/qai.0000000000000341.Peer-Reviewed Original ResearchConceptsHIV-1 RNAUndetectable HIV-1 RNAVACS IndexUninfected individualsHazard ratioC-statisticFrailty-related phenotypePredictors of hospitalizationCohort Study participantsLow physical activityAdverse health outcomesPhysiologic reserveGeriatric syndromesFrailty stateRisk factorsHospitalizationPhysical activityMortality riskHIVHealth behaviorsHealth outcomesSurvival analysisSystem biomarkersStudy participantsMortality