Ronald George Hauser III, MD
Associate Professor of Laboratory MedicineCards
About
Research
Publications
2024
Veteran's Health Administration HIV Care Continuum: 2019 vs 2022
Maier M, Beste L, Lowy E, Hauser R, Van Epps P, Yakovchenko V, Rogal S, Chartier M, Ross D. Veteran's Health Administration HIV Care Continuum: 2019 vs 2022. Open Forum Infectious Diseases 2024, 11: ofae382. PMID: 39086463, PMCID: PMC11288371, DOI: 10.1093/ofid/ofae382.Peer-Reviewed Original ResearchHuman immunodeficiency virusHIV care continuumViral suppressionVL resultsViral loadCare continuumFactors associated with viral suppressionTime of HIV diagnosisHuman immunodeficiency virus careRetrospective cohort analysisMultivariate logistic regressionVirologic suppressionImmunodeficiency virusHIV diagnosisVeterans Health AdministrationReceipt of careCohort analysisHealth care institutionsPWHCare qualityVHA careHealth AdministrationCare institutionsCareVHABlood type as a risk factor for pancreatic ductal adenocarcinoma.
Rahimi Larki N, Skanderson M, Tate J, Levinson R, Hauser R, Brandt C, Yang Y, Justice A, Wang L. Blood type as a risk factor for pancreatic ductal adenocarcinoma. Journal Of Clinical Oncology 2024, 42: 10559-10559. DOI: 10.1200/jco.2024.42.16_suppl.10559.Peer-Reviewed Original ResearchPancreatic ductal adenocarcinoma riskVeterans Health AdministrationRisk of pancreatic ductal adenocarcinomaNon-O blood typeNeighborhood-level socioeconomic dataIntegrated healthcare systemHigh risk of pancreatic ductal adenocarcinomaPancreatic ductal adenocarcinomaAssociated with higher riskAssociated with increased riskUnited StatesHealth AdministrationOutpatient encountersHealthcare systemBaseline ageAlcohol useIndex dateAssociation of blood typeCancer deathWhite populationSocioeconomic dataBlack patientsDiverse populationsRisk factorsBlood typeEstimating risk for pancreatic cancer among 9.4 million veterans in care.
Wang L, Rahimi Larki N, Skanderson M, Tate J, Hauser R, Brandt C, Yang Y, Justice A. Estimating risk for pancreatic cancer among 9.4 million veterans in care. Journal Of Clinical Oncology 2024, 42: 10544-10544. DOI: 10.1200/jco.2024.42.16_suppl.10544.Peer-Reviewed Original ResearchVeterans Health AdministrationGeneral populationAlcohol useIntegrated health systemElectronic health recordsTen-year riskHistory of cancerLoss to follow-upFollow-upEvaluated model discriminationMedian baseline ageCox proportional hazards modelsRisk prediction modelHealth recordsProportional hazards modelHealth systemHealth AdministrationMultivariate Cox proportional hazards modelSmoking statusCharlson Comorbidity IndexBaseline ageClinical reasoningRange of risksHazards modelFinal predictorsDevelopment and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration
Mezzacappa C, Larki N, Skanderson M, Park L, Brandt C, Hauser R, Justice A, Yang Y, Wang L. Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration. Digestive Diseases And Sciences 2024, 69: 1507-1513. PMID: 38453743, DOI: 10.1007/s10620-024-08324-w.Peer-Reviewed Original ResearchElectronic health recordsVeterans Health AdministrationHealth AdministrationElectronic health records data elementsElectronic health record dataDiagnosis of exocrine pancreatic cancerNational Cancer RegistryCancer RegistryHealth recordsExocrine pancreatic cancerOncology settingOutpatient encountersInpatient encountersData elementsExpert adjudicationPancreatic ductal adenocarcinomaEpidemiological studiesRandom sampleInterquartile rangeIdentification of patientsRange of patientsPancreatic cancerVeteransLate diagnosisExcellent PPVCombining Charlson comorbidity and VACS indices improves prognostic accuracy for all-cause mortality for patients with and without HIV in the Veterans Health Administration
McGinnis K, Justice A, Marconi V, Rodriguez-Barradas M, Hauser R, Oursler K, Brown S, Bryant K, Tate J, Study F. Combining Charlson comorbidity and VACS indices improves prognostic accuracy for all-cause mortality for patients with and without HIV in the Veterans Health Administration. Frontiers In Medicine 2024, 10: 1342466. PMID: 38356736, PMCID: PMC10864663, DOI: 10.3389/fmed.2023.1342466.Peer-Reviewed Original ResearchCharlson Comorbidity IndexVACS IndexHIV RNAVeterans Health AdministrationComorbid diseasesNon-AIDS conditionsCongestive heart failureAssociated with CD4Risk of mortalityMedian ageHepatitis C.Older age groupsCharlson comorbidityCD4Prognostic accuracyHeart failurePWoHComorbidity indexVA careKidney diseasePWHHIVMatched comparatorsBaseline predictorsClinical biomarkersDecreasing alloimmunization‐specific mortality in sickle cell disease in the United States: Cost‐effectiveness of a shared transfusion resource
Ito S, Pandya A, Hauser R, Krishnamurti L, Stites E, Tormey C, Krumholz H, Hendrickson J, Goshua G. Decreasing alloimmunization‐specific mortality in sickle cell disease in the United States: Cost‐effectiveness of a shared transfusion resource. American Journal Of Hematology 2024, 99: 570-576. PMID: 38279581, DOI: 10.1002/ajh.27211.Peer-Reviewed Original ResearchSickle cell diseaseDelayed hemolytic transfusion reactionQuality-adjusted life expectancyAlloimmunized patientsPatient populationRed blood cell alloimmunizationCell diseaseCost-effective interventionMedical expenditure of patientsHealth system perspectiveExpenditure of patientsIncremental cost-effectiveness ratioHemolytic transfusion reactionsUnited StatesMarkov cohort simulationCost-effectiveAverage patient populationCost-effectiveness ratioBirth cohortAnalytical time horizonAntibody historyCohort simulationTransfusionTransfusion reactionsLife expectancy
2023
Crawling toward obsolescence: The extended lifespan of amylase for pancreatitis
Kanaparthy N, Loza A, Hauser R. Crawling toward obsolescence: The extended lifespan of amylase for pancreatitis. PLOS ONE 2023, 18: e0296180. PMID: 38127992, PMCID: PMC10734915, DOI: 10.1371/journal.pone.0296180.Peer-Reviewed Original ResearchMachine learning to develop a predictive model of pressure injury in persons with spinal cord injury
Luther S, Thomason S, Sabharwal S, Finch D, McCart J, Toyinbo P, Bouayad L, Lapcevic W, Hahm B, Hauser R, Matheny M, Powell-Cope G. Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury. Spinal Cord 2023, 61: 513-520. PMID: 37598263, DOI: 10.1038/s41393-023-00924-z.Peer-Reviewed Original ResearchConceptsPressure injuriesAmerican Spinal Cord Injury Association Impairment ScaleSCI/D CentersSpinal cord injury/diseaseReceiver-operating curve analysisNew pressure injuryModifiable risk factorsElectronic health record dataSCI/DSpinal cord injuryHealth record dataInjury/diseaseTwo-step logistic regressionLogistic regression modelsCohort studyRegression modelsStudy designACord injurySevere gradesRisk factorsImpairment ScaleHigh riskClinical implicationsTotal daysAnnual examPROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data
Iscoe M, Loza A, Turbiville D, Campbell S, Peaper D, Balbuena-Merle R, Hauser R. PROSER: A Web-Based Peripheral Blood Smear Interpretation Support Tool Utilizing Electronic Health Record Data. American Journal Of Clinical Pathology 2023, 160: 98-105. PMID: 37026746, DOI: 10.1093/ajcp/aqad024.Peer-Reviewed Original ResearchConceptsQuality improvement studyElectronic health recordsLaboratory valuesWeb-based clinical decision support toolClinical decision support toolElectronic health record dataHealth record dataImprovement studyResident trainingBlood smear interpretationClinical outcomesMorphologic findingsAcademic hospitalCorresponding reference rangesMedication informationReference rangeMicroscopy findingsCDS toolsIntervention effectsPathology practiceSmear interpretationHealth recordsRecord dataPathologistsPatientsAssociations between ABO non‐identical platelet transfusions and patient outcomes—A multicenter retrospective analysis
Bougie D, Reese S, Birch R, Bookwalter D, Mitchell P, Roh D, Kreuziger L, Cable R, Goel R, Gottschall J, Hauser R, Hendrickson J, Hod E, Josephson C, Kahn S, Kleinman S, Mast A, Ness P, Roubinian N, Sloan S, Study‐IV‐Pediatric F. Associations between ABO non‐identical platelet transfusions and patient outcomes—A multicenter retrospective analysis. Transfusion 2023, 63: 960-972. PMID: 36994786, PMCID: PMC10175171, DOI: 10.1111/trf.17319.Peer-Reviewed Original ResearchConceptsPlatelet transfusionsHazard ratioPatient outcomesMulticenter retrospective analysisPlatelet transfusion requirementsGroup O recipientsRecipient's blood groupRisk of mortalitySpecific patient populationsBlood group ARecipient EpidemiologyTransfusion requirementsB recipientsOverall cohortProspective studyO recipientsPatient populationRetrospective analysisPlatelet dosesGroup ATransfusionABO antigensABO groupPatient exposureSignificant association