2023
Effects of topiramate therapy on serum bicarbonate concentration in a sample of 10,279 veterans
Naps M, Leong S, Hartwell E, Rentsch C, Kranzler H. Effects of topiramate therapy on serum bicarbonate concentration in a sample of 10,279 veterans. Alcohol Clinical And Experimental Research 2023, 47: 438-447. PMID: 36810985, DOI: 10.1111/acer.15011.Peer-Reviewed Original ResearchConceptsSerum bicarbonate concentrationAlcohol use disorderMetabolic acidosisMEq/LAlcohol consumptionTopiramate therapyAlcohol Use Disorders Identification Test-Consumption scoresVeterans Health Administration electronic health record dataBicarbonate concentrationPropensity score-matched control groupPropensity score-matched controlsMean daily dosageSignificant metabolic acidosisElectronic health record dataDiagnosis of AUDHealth record dataBaseline alcohol consumptionDifferences linear regression modelThree-level measureAcid-base balanceTopiramate dosageTopiramate prescriptionsMean followTopiramate treatmentDaily dosageEnhanced Identification of Hispanic Ethnicity Using Clinical Data
Ochoa-Allemant P, Tate J, Williams E, Gordon K, Marconi V, Bensley K, Rentsch C, Wang K, Taddei T, Justice A, Cohorts F. Enhanced Identification of Hispanic Ethnicity Using Clinical Data. Medical Care 2023, 61: 200-205. PMID: 36893404, PMCID: PMC10114212, DOI: 10.1097/mlr.0000000000001824.Peer-Reviewed Original ResearchConceptsBurden of diseaseHispanic patientsCountry of birthClinical dataHispanic ethnicityNon-Hispanic white patientsSex-adjusted prevalenceChronic liver diseaseHuman immunodeficiency virusDemographic characteristicsElectronic health record dataHealth careHealth record dataPrevalence of conditionsUS health care systemMedicare administrative dataHealth care systemWhite patientsLiver diseaseImmunodeficiency virusSelf-reported ethnicityHigh prevalenceGastric cancerHepatocellular carcinomaVeteran population
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 learningAssociation of topiramate prescribed for any indication with reduced alcohol consumption in electronic health record data
Kranzler HR, Leong SH, Naps M, Hartwell EE, Fiellin DA, Rentsch CT. Association of topiramate prescribed for any indication with reduced alcohol consumption in electronic health record data. Addiction 2022, 117: 2826-2836. PMID: 35768956, PMCID: PMC10317468, DOI: 10.1111/add.15980.Peer-Reviewed Original ResearchConceptsAUDIT-C scoresAlcohol use disorderElectronic health record dataHealth record dataUse disordersNEG patientsTopiramate dosageAlcohol Use Disorders Identification Test-Consumption scoresPropensity score-matched groupsHistory of AUDParallel group comparisonPropensity score-matched comparison groupRecord dataBaseline drinking levelsReduced alcohol consumptionHealth care systemTopiramate prescriptionsPre-post differencesAUD historyTopiramate's effectsPatientsRecord diagnosisAlcohol consumptionTopiramateComparison groupGeographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States
Ferguson JM, Justice AC, Osborne TF, Magid HSA, Purnell AL, Rentsch CT. Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States. Scientific Reports 2022, 12: 273. PMID: 34997001, PMCID: PMC8741774, DOI: 10.1038/s41598-021-03967-5.Peer-Reviewed Original Research
2021
Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans
Gerlovin H, Posner DC, Ho YL, Rentsch CT, Tate JP, King JT, Kurgansky KE, Danciu I, Costa L, Linares FA, Goethert ID, Jacobson DA, Freiberg MS, Begoli E, Muralidhar S, Ramoni RB, Tourassi G, Gaziano JM, Justice AC, Gagnon DR, Cho K. Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans. American Journal Of Epidemiology 2021, 190: 2405-2419. PMID: 34165150, PMCID: PMC8384407, DOI: 10.1093/aje/kwab183.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnti-Bacterial AgentsAzithromycinCOVID-19COVID-19 Drug TreatmentDrug Therapy, CombinationFemaleHospitalizationHumansHydroxychloroquineIntention to Treat AnalysisMachine LearningMaleMiddle AgedPharmacoepidemiologyRetrospective StudiesSARS-CoV-2Treatment OutcomeUnited StatesVeteransConceptsUS veteransCOVID-19Veterans Affairs Health Care SystemRecent randomized clinical trialsAdministration of hydroxychloroquineEffectiveness of hydroxychloroquineRisk of intubationEffect of hydroxychloroquineElectronic health record dataRandomized clinical trialsTreatment of patientsUS veteran populationCOVID-19 outcomesCoronavirus disease 2019Health record dataRigorous study designsHealth care systemSurvival benefitTreat analysisEarly therapyHospitalized populationClinical trialsObservational studyDisease 2019Hydroxychloroquine
2020
Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform
Williamson E, Tazare J, Bhaskaran K, Walker A, McDonald H, Tomlinson L, Bacon S, Bates C, Curtis H, Forbes H, Minassian C, Morton C, Nightingale E, Mehrkar A, Evans D, Nicholson B, Leon D, Inglesby P, MacKenna B, Cockburn J, Davies N, Hulme W, Morley J, Douglas I, Rentsch C, Mathur R, Wong A, Schultze A, Croker R, Parry J, Hester F, Harper S, Perera R, Grieve R, Harrison D, Steyerberg E, Eggo R, Diaz-Ordaz K, Keogh R, Evans S, Smeeth L, Goldacre B. Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform. Wellcome Open Research 2020, 5: 243. DOI: 10.12688/wellcomeopenres.16353.1.Peer-Reviewed Original ResearchRisk prediction modelPrimary care electronic health record dataCOVID-19Chronic disease settingsElectronic health record dataHealth record dataPrevalence of infectionCOVID-19 deathsWorld Health OrganizationOpenSAFELY platformAdult patientsPoor outcomeInfection burdenDisease settingsDeath dataInfectious diseasesHealth OrganizationInfection prevalenceRecord dataCohort approachPopulation of interestPrevalenceTime-varying measuresDeathOutcomesAssociation of OPRM1 Functional Coding Variant With Opioid Use Disorder
Zhou H, Rentsch CT, Cheng Z, Kember RL, Nunez YZ, Sherva RM, Tate JP, Dao C, Xu K, Polimanti R, Farrer LA, Justice AC, Kranzler HR, Gelernter J. Association of OPRM1 Functional Coding Variant With Opioid Use Disorder. JAMA Psychiatry 2020, 77: 1072-1080. PMID: 32492095, PMCID: PMC7270886, DOI: 10.1001/jamapsychiatry.2020.1206.Peer-Reviewed Original ResearchConceptsOpioid use disorderUse disordersMendelian randomization analysisAfrican American individualsMAIN OUTCOMEFunctional coding variantSignificant associationCausal associationRandomization analysisElectronic health record dataCurrent opioid crisisAmerican individualsHealth record dataCognitive performanceInternational Statistical ClassificationRelated Health ProblemsPotential causal associationAmerican controlsEuropean American controlsAfrican-American controlsCoding variantBuprenorphine treatmentOUD diagnosisTobacco smokingNinth Revision
2018
Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection
Rentsch CT, Cartwright EJ, Gandhi NR, Brown ST, Rodriguez-Barradas MC, Goetz MB, Marconi VC, Gibert CL, Re VL, Fiellin DA, Justice AC, Tate JP. Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection. Annals Of Epidemiology 2018, 28: 808-811. PMID: 30195616, PMCID: PMC6318448, DOI: 10.1016/j.annepidem.2018.08.007.Peer-Reviewed Original ResearchConceptsHepatitis C virus infectionCorporate Data WarehouseChronic HCV infectionC virus infectionPositive predictive valuePredictive valueHCV infectionHCV treatmentVirus infectionVeterans Health Administration Corporate Data WarehouseChronic hepatitis C virus (HCV) infectionStudy periodModern treatment eraRetrospective cohort studyElectronic health record dataPharmacy fill recordsHealth record dataNegative predictive valueElectronic health recordsAntiviral regimenHCV therapyTreatment eraChart reviewCohort studyAntiviral treatment