2022
The All of Us Research Program: Data quality, utility, and diversity
Ramirez A, Sulieman L, Schlueter D, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman K, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark C, Cohn E, Ohno-Machado L, Schully S, Ahmedani B, Argos M, Cronin R, O’Donnell C, Fouad M, Goldstein D, Greenland P, Hebbring S, Karlson E, Khatri P, Korf B, Smoller J, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney S, Gebo K, Denny J, Carroll R, Glazer D, Harris P, Hripcsak G, Philippakis A, Roden D, Program T, Ahmedani B, Johnson C, Ahsan H, Antoine-LaVigne D, Singleton G, Anton-Culver H, Topol E, Baca-Motes K, Steinhubl S, Wade J, Begale M, Jain P, Sutherland S, Lewis B, Korf B, Behringer M, Gharavi A, Goldstein D, Hripcsak G, Bier L, Boerwinkle E, Brilliant M, Murali N, Hebbring S, Farrar-Edwards D, Burnside E, Drezner M, Taylor A, Channamsetty V, Montalvo W, Sharma Y, Chinea C, Jenks N, Cicek M, Thibodeau S, Holmes B, Schlueter E, Collier E, Winkler J, Corcoran J, D’Addezio N, Daviglus M, Winn R, Wilkins C, Roden D, Denny J, Doheny K, Nickerson D, Eichler E, Jarvik G, Funk G, Philippakis A, Rehm H, Lennon N, Kathiresan S, Gabriel S, Gibbs R, Rico E, Glazer D, Grand J, Greenland P, Harris P, Shenkman E, Hogan W, Igho-Pemu P, Pollan C, Jorge M, Okun S, Karlson E, Smoller J, Murphy S, Ross M, Kaushal R, Winford E, Wallace F, Khatri P, Kheterpal V, Ojo A, Moreno F, Kron I, Peterson R, Menon U, Lattimore P, Leviner N, Obedin-Maliver J, Lunn M, Malik-Gagnon L, Mangravite L, Marallo A, Marroquin O, Visweswaran S, Reis S, Marshall G, McGovern P, Mignucci D, Moore J, Munoz F, Talavera G, O'Connor G, O'Donnell C, Ohno-Machado L, Orr G, Randal F, Theodorou A, Reiman E, Roxas-Murray M, Stark L, Tepp R, Zhou A, Topper S, Trousdale R, Tsao P, Weidman L, Weiss S, Wellis D, Whittle J, Wilson A, Zuchner S, Zwick M. The All of Us Research Program: Data quality, utility, and diversity. Patterns 2022, 3: 100570. PMID: 36033590, PMCID: PMC9403360, DOI: 10.1016/j.patter.2022.100570.Peer-Reviewed Original Research
2018
Analysis of treatment pathways for three chronic diseases using OMOP CDM
Zhang X, Wang L, Miao S, Xu H, Yin Y, Zhu Y, Dai Z, Shan T, Jing S, Wang J, Zhang X, Huang Z, Wang Z, Guo J, Liu Y. Analysis of treatment pathways for three chronic diseases using OMOP CDM. Journal Of Medical Systems 2018, 42: 260. PMID: 30421323, PMCID: PMC6244882, DOI: 10.1007/s10916-018-1076-5.Peer-Reviewed Original ResearchConceptsTreatment pathwaysChronic diseasesStudy of drugsClinical data repositoryClinical treatmentDifferent medical institutionsProportion of monotherapyFirst-line medicationMedical institutionsFirst Affiliated HospitalType 2 diabetesNanjing Medical UniversityDifferent treatment pathwaysMost patientsCommon medicationsAffiliated HospitalMedicationsNational guidelinesMedication informationLocal hospitalMedical UniversitySame diseaseDiseasePatientsNew drugsAssociation of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin
Vashisht R, Jung K, Schuler A, Banda J, Park R, Jin S, Li L, Dudley J, Johnson K, Shervey M, Xu H, Wu Y, Natrajan K, Hripcsak G, Jin P, Van Zandt M, Reckard A, Reich C, Weaver J, Schuemie M, Ryan P, Callahan A, Shah N. Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin. JAMA Network Open 2018, 1: e181755. PMID: 30646124, PMCID: PMC6324274, DOI: 10.1001/jamanetworkopen.2018.1755.Peer-Reviewed Original ResearchConceptsDPP-4 inhibitorsDipeptidyl peptidase-4 inhibitorsFirst-line therapyPeptidase-4 inhibitorsSecond-line drugsType 2 diabetesMyocardial infarctionEye disordersKidney disordersDrug classesSecond-line treatment choiceTotal hemoglobinObservational Health Data SciencesSecond-line treatment optionNew-user cohort studyEffectiveness of sulfonylureasSecond-line treatmentHemoglobin A1c levelsUse of sulfonylureasHealth Data SciencesLarge international studyElectronic medical recordsRoutine medical practiceInsurance claims dataCohort study
2016
Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records
Kolek M, Graves A, Xu M, Bian A, Teixeira P, Shoemaker M, Parvez B, Xu H, Heckbert S, Ellinor P, Benjamin E, Alonso A, Denny J, Moons K, Shintani A, Harrell F, Roden D, Darbar D. Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records. JAMA Cardiology 2016, 1: 1007-1013. PMID: 27732699, PMCID: PMC5293184, DOI: 10.1001/jamacardio.2016.3366.Peer-Reviewed Original ResearchIncident atrial fibrillationElectronic medical recordsCHARGE-AF modelAtrial fibrillationRisk prediction modelMedical recordsEMR cohortHistory of AFInternal medicine outpatient clinicProspective cohort studyDiastolic blood pressureMedicine outpatient clinicIndividuals 40 yearsType 2 diabetesHigh-risk individualsVanderbilt University Medical CenterUniversity Medical CenterLow-risk individualsPoor calibrationAfrican AmericansFuture risk modelsHealth care expendituresAF managementCohort studyEchocardiographic variables