2022
Factors Associated With COVID-19 Death in the United States: Cohort Study
Chen U, Xu H, Krause T, Greenberg R, Dong X, Jiang X. Factors Associated With COVID-19 Death in the United States: Cohort Study. JMIR Public Health And Surveillance 2022, 8: e29343. PMID: 35377319, PMCID: PMC9132142, DOI: 10.2196/29343.Peer-Reviewed Original ResearchConceptsCOVID-19-related deathsCohort studyPatient characteristicsElectronic health record data setsLarge national cohort studyNational cohort studyChronic respiratory diseasesProportional hazards modelCOVID-19COVID-19 deathsImmunosuppressive conditionsKidney functionMale sexLung cancerCOVID-19 casesRecent diagnosisRisk factorsRespiratory diseaseCardiac diseaseOrgan transplantsHigh incidenceHazards modelNeurological diseasesOlder ageDiseaseAnalysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort
Lu Y, Van Zandt M, Liu Y, Li J, Wang X, Chen Y, Chen Z, Cho J, Dorajoo SR, Feng M, Hsu MH, Hsu JC, Iqbal U, Jonnagaddala J, Li YC, Liaw ST, Lim HS, Ngiam KY, Nguyen PA, Park RW, Pratt N, Reich C, Rhee SY, Sathappan SMK, Shin SJ, Tan HX, You SC, Zhang X, Krumholz HM, Suchard MA, Xu H. Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort. JAMA Network Open 2022, 5: e223877. PMID: 35323951, PMCID: PMC8948532, DOI: 10.1001/jamanetworkopen.2022.3877.Peer-Reviewed Original ResearchConceptsDual combination therapyUse of ACEIAntihypertensive drug classesProportion of patientsKhoo Teck Puat HospitalCombination therapyUniversity Hospital databaseHospital databaseDrug classesDual combinationSouth Western Sydney Local Health DistrictWestern Sydney Local Health DistrictPatients age 65 yearsSydney Local Health DistrictElectronic health record databasePatients age 18Local Health DistrictAge 65 yearsTreatment of hypertensionHealth record databaseARB monotherapyTreatment escalationAdult patientsCohort studyCombination regimen
2021
Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database
Dong X, Zhou Y, Shu X, Bernstam E, Stern R, Aronoff D, Xu H, Lipworth L. Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database. Microbiology Spectrum 2021, 9: 10.1128/spectrum.00327-21. PMID: 34406805, PMCID: PMC8552669, DOI: 10.1128/spectrum.00327-21.Peer-Reviewed Original ResearchConceptsPositive SARS-CoV-2 testSARS-CoV-2 testSecond positive testElectronic health record databaseCases of reinfectionHealth record databasePositive testPositive SARS-CoV-2 PCR test resultsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testingSARS-CoV-2 PCR test resultsRecord databaseSevere acute respiratory syndrome coronavirus 2Intensive care unit admissionAcute respiratory syndrome coronavirus 2SARS-CoV-2 infectionRespiratory syndrome coronavirus 2Long-term health consequencesLarge electronic health record databasePotential long-term health consequencesCare unit admissionOverweight/obeseChronic medical conditionsPositive molecular testCOVID-19 patientsSyndrome coronavirus 2
2020
Learning from local to global: An efficient distributed algorithm for modeling time-to-event data
Duan R, Luo C, Schuemie M, Tong J, Liang C, Chang H, Boland M, Bian J, Xu H, Holmes J, Forrest C, Morton S, Berlin J, Moore J, Mahoney K, Chen Y. Learning from local to global: An efficient distributed algorithm for modeling time-to-event data. Journal Of The American Medical Informatics Association 2020, 27: 1028-1036. PMID: 32626900, PMCID: PMC7647322, DOI: 10.1093/jamia/ocaa044.Peer-Reviewed Original Research
2019
Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison
Zhang Y, Tiryaki F, Jiang M, Xu H. Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison. BMC Medical Informatics And Decision Making 2019, 19: 77. PMID: 30943955, PMCID: PMC6448179, DOI: 10.1186/s12911-019-0783-2.Peer-Reviewed Original Research
2017
Evaluating the role of race and medication in protection of uterine fibroids by type 2 diabetes exposure
Velez Edwards D, Hartmann K, Wellons M, Shah A, Xu H, Edwards T. Evaluating the role of race and medication in protection of uterine fibroids by type 2 diabetes exposure. BMC Women's Health 2017, 17: 28. PMID: 28399866, PMCID: PMC5387248, DOI: 10.1186/s12905-017-0386-y.Peer-Reviewed Original ResearchConceptsT2D medicationsUF riskAnnual healthcare costsElectronic medical record algorithmCase-control studyLarge clinical populationFurther mechanistic researchBackgroundUterine fibroidsT2D diagnosisDiabetes exposureMedication typeDiabetes presenceInsulin usersUterine fibroidsInsulin treatmentProtective associationStratified analysisClinical cohortConclusionsThese dataProtective effectLarge cohortCase-control statusHealthcare costsT2DMedications
2015
Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer
Zhang Y, Franzini L, Chan W, Xu H, Du X. Effects of Health Insurance on Tumor Stage, Treatment, and Survival in Large Cohorts of Patients with Breast and Colorectal Cancer. Journal Of Health Care For The Poor And Underserved 2015, 26: 1336-1358. PMID: 26548682, DOI: 10.1353/hpu.2015.0119.Peer-Reviewed Original ResearchConceptsRisk of mortalityColorectal cancerTumor stagePrivate health insuranceCancer patientsHealth insuranceCancer-directed surgeryColorectal cancer patientsTexas Cancer RegistryInsurance coverageAdditional private health insuranceBreast cancer patientsHealth insurance statusHealth insurance coverageOverall survivalCancer RegistryInsurance statusBreast cancerLarge cohortHigh riskMedicare beneficiariesPatientsCancerChemotherapySurgery
2014
Genotype and risk of major bleeding during warfarin treatment
Kawai V, Cunningham A, Vear S, Van Driest S, Oginni A, Xu H, Jiang M, Li C, Denny J, Shaffer C, Bowton E, Gage B, Ray W, Roden D, Stein C. Genotype and risk of major bleeding during warfarin treatment. Pharmacogenomics 2014, 15: 1973-1983. PMID: 25521356, PMCID: PMC4304738, DOI: 10.2217/pgs.14.153.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBiological Specimen BanksCytochrome P-450 CYP2C9Cytochrome P-450 Enzyme SystemCytochrome P450 Family 4Dose-Response Relationship, DrugEthnicityFemaleGene FrequencyGenetic Association StudiesGenetic VariationGenotypeHemorrhageHumansMaleMiddle AgedRisk FactorsVitamin K Epoxide ReductasesWarfarinValidating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality
Xu H, Aldrich M, Chen Q, Liu H, Peterson N, Dai Q, Levy M, Shah A, Han X, Ruan X, Jiang M, Li Y, St Julien J, Warner J, Friedman C, Roden D, Denny J. Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality. Journal Of The American Medical Informatics Association 2014, 22: 179-191. PMID: 25053577, PMCID: PMC4433365, DOI: 10.1136/amiajnl-2014-002649.Peer-Reviewed Original ResearchConceptsType 2 diabetes patientsElectronic health recordsCancer patientsCancer mortalityDiabetes patientsEHR dataNon-diabetic cancer patientsCox proportional hazards modelDrug exposure informationOral hypoglycemic medicationsCharlson Comorbidity IndexNon-diabetic patientsUse of metforminCancer diagnosisHealth recordsSite-specific cancersBody mass indexProportional hazards modelVanderbilt University Medical CenterUniversity Medical CenterLarge electronic health recordHypoglycemic medicationsCause mortalityComorbidity indexInsulin use
2013
Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI data
2012
Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping
Schildcrout J, Denny J, Bowton E, Gregg W, Pulley J, Basford M, Cowan J, Xu H, Ramirez A, Crawford D, Ritchie M, Peterson J, Masys D, Wilke R, Roden D. Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping. Clinical Pharmacology & Therapeutics 2012, 92: 235-242. PMID: 22739144, PMCID: PMC3785311, DOI: 10.1038/clpt.2012.66.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterAdverse eventsPreemptive genotypingPotential adverse eventsUniversity Medical CenterHome patientsPharmacogenetic associationsMedical CenterVariant allelesMedicationsDrug outcomesPatient safetyDrug decision makingRelevant genetic variantsRoutine integrationTarget drugsGenetic variantsOutcomesFrequency of opportunitiesGenotypingSafetyPrescribingPatientsCohortPharmacogeneticsPredicting warfarin dosage in EuropeanAmericans and AfricanAmericans using DNA samples linked to an electronic health record
Ramirez A, Shi Y, Schildcrout J, Delaney J, Xu H, Oetjens M, Zuvich R, Basford M, Bowton E, Jiang M, Speltz P, Zink R, Cowan J, Pulley J, Ritchie M, Masys D, Roden D, Crawford D, Denny J. Predicting warfarin dosage in EuropeanAmericans and AfricanAmericans using DNA samples linked to an electronic health record. Pharmacogenomics 2012, 13: 407-418. PMID: 22329724, PMCID: PMC3361510, DOI: 10.2217/pgs.11.164.Peer-Reviewed Original ResearchAdultAgedAged, 80 and overAnticoagulantsAryl Hydrocarbon HydroxylasesBlack or African AmericanCalcium-Binding ProteinsCytochrome P-450 CYP2C9Cytochrome P-450 Enzyme SystemCytochrome P450 Family 4Dose-Response Relationship, DrugDrug Administration ScheduleElectronic Health RecordsFemaleHumansMaleMiddle AgedMixed Function OxygenasesPolymorphism, Single NucleotideSubstance-Related DisordersVitamin K Epoxide ReductasesWarfarinWhite PeopleThe use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients
Birdwell K, Grady B, Choi L, Xu H, Bian A, Denny J, Jiang M, Vranic G, Basford M, Cowan J, Richardson D, Robinson M, Ikizler T, Ritchie M, Stein C, Haas D. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenetics And Genomics 2012, 22: 32-42. PMID: 22108237, PMCID: PMC3237759, DOI: 10.1097/fpc.0b013e32834e1641.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsATP Binding Cassette Transporter, Subfamily BATP Binding Cassette Transporter, Subfamily B, Member 1Body WeightCytochrome P-450 CYP3ADatabases, Nucleic AcidDose-Response Relationship, DrugDrug MonitoringElectronic Health RecordsFemaleGenetic Association StudiesGenotypeHemoglobinsHumansImmunosuppressive AgentsKidney TransplantationLinkage DisequilibriumMaleMiddle AgedPolymorphism, Single NucleotidePregnane X ReceptorReceptors, SteroidTacrolimusConceptsTacrolimus dose requirementsKidney transplant recipientsDose requirementsElectronic medical recordsBlood concentrationsTransplant recipientsMedical recordsCYP3A5 rs776746Electronic medical record dataInterindividual pharmacokinetic variabilityTacrolimus blood concentrationsNarrow therapeutic indexDNA biobanksMedical record dataTherapeutic drug monitoringDrug-metabolizing enzymesKidney transplantationClinical factorsPrimary outcomeImmunosuppressive drugsPharmacokinetic variabilityTacrolimus clearanceClinical covariatesPharmacogenomic predictorsTherapeutic index