2025
Detection of emergency department patients at risk of dementia through artificial intelligence
Cohen I, Taylor R, Xue H, Faustino I, Festa N, Brandt C, Gao E, Han L, Khasnavis S, Lai J, Mecca A, Sapre A, Young J, Zanchelli M, Hwang U. Detection of emergency department patients at risk of dementia through artificial intelligence. Alzheimer's & Dementia 2025, 21: e70334. PMID: 40457744, PMCID: PMC12130574, DOI: 10.1002/alz.70334.Peer-Reviewed Original ResearchConceptsElectronic health record dataHealth record dataEmergency departmentDetect dementiaDementia detectionYale New Haven HealthRecord dataRisk of dementiaEmergency department patientsBalance detection accuracyDementia algorithmsImprove patient outcomesCare coordinationCare transitionsDementia diagnosisReal-time applicationsClinical decision-makingClinician supportED usePatient safetyProbable dementiaMachine learning algorithmsED workflowED visitsED encountersThe Impact of Chiropractic Care on Opioid Prescriptions in Veterans Health Administration Patients Receiving Low Back Pain Care
Lisi A, Bastian L, Brandt C, Coleman B, Fenton B, King J, Goulet J. The Impact of Chiropractic Care on Opioid Prescriptions in Veterans Health Administration Patients Receiving Low Back Pain Care. Journal Of General Internal Medicine 2025, 1-9. PMID: 40394439, DOI: 10.1007/s11606-025-09556-w.Peer-Reviewed Original ResearchLow back painPrimary care providersVeterans Health AdministrationChiropractic careOpioid prescriptionsCare usersLow back pain careLow back pain visitsVA primary care providersElectronic health record dataVeterans Health Administration patientsBack pain careDesignCross-sectional analysisHealth record dataReceipt of opioid prescriptionsPain careCare providersPropensity-matched sampleFollow-upBack painHealthcare servicesVHA patientsHealth AdministrationVA patientsPotential confoundersPredicting Agitation Events in the Emergency Department Through Artificial Intelligence
Wong A, Sapre A, Wang K, Nath B, Shah D, Kumar A, Faustino I, Desai R, Hu Y, Robinson L, Meng C, Tong G, Bernstein S, Yonkers K, Melnick E, Dziura J, Taylor R. Predicting Agitation Events in the Emergency Department Through Artificial Intelligence. JAMA Network Open 2025, 8: e258927. PMID: 40332935, PMCID: PMC12059975, DOI: 10.1001/jamanetworkopen.2025.8927.Peer-Reviewed Original ResearchConceptsED visitsEmergency departmentAgitation eventsElectronic health record dataArea under the receiver operating characteristic curvePatient-centered careHealth service utilizationPrimary outcomeHealth record dataUrban health systemED visit dataMode of arrivalPrevention of agitationOutcome of agitationDiverse patient populationsRestraint ordersCross-sectional cohortService utilizationVital signsED sitesHealth systemMain OutcomesRestraint eventsRange of predicted probabilitiesVisit dataA cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department
Marcus S, Cullen S, Schmutte T, Xie M, Liu T, Ungar L, Cardamone N, Williams N, Olfson M. A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department. Journal Of Psychiatric Research 2025, 186: 458-468. PMID: 40318538, DOI: 10.1016/j.jpsychires.2025.04.035.Peer-Reviewed Original ResearchConceptsSelf-harm eventsMental health disordersED visitsHealth disordersBulimia nervosaEmergency departmentNonfatal suicideDiagnosis of bulimia nervosaElectronic health record dataSuicidal self-harmEmergency mental healthcareSuicide-related eventsLogistic regressionHealth record dataMental health diagnosesEmergency department visitsMental health problemsVisits of individualsSuicidal symptomsMental healthcareED episodesHealth recordsSelf-harmShort-term riskHealth diagnosisMapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry
Cohen I, Diao Z, Goyal P, Gupta A, Hawk K, Malcom B, Malicki C, Sharma D, Sweeney B, Weiner S, Venkatesh A, Taylor R. Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry. Journal Of The American College Of Emergency Physicians Open 2025, 6: 100016. PMID: 40012646, PMCID: PMC11853007, DOI: 10.1016/j.acepjo.2024.100016.Peer-Reviewed Original ResearchObservational Medical Outcomes Partnership Common Data ModelCommon data modelOMOP CDMElectronic health record dataData registryObservational Health Data SciencesHealth record dataEmergency department dataData modelDepartment dataRecord dataCommunity forumsAmerican CollegePublic healthDescriptive analysisRegistryData harmonizationData scienceGap analysisData fieldCross-institutional collaborationMapping processMedicine dataPotential challengesHealthUtility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
Davis C, Jinwala Z, Hatoum A, Toikumo S, Agrawal A, Rentsch C, Edenberg H, Baurley J, Hartwell E, Crist R, Gray J, Justice A, Gelernter J, Kember R, Kranzler H, Muralidhar S, Moser J, Deen J, Tsao P, Gaziano J, Hauser E, Kilbourne A, Matheny M, Oslin D, Churby L, Whitbourne S, Brewer J, Shayan S, Selva L, Pyarajan S, Cho K, DuVall S, Brophy M, Stephens B, Connor T, Argyres D, Assimes T, Hung A, Kranzler H, Aguayo S, Ahuja S, Alexander K, Androulakis X, Balasubramanian P, Ballas Z, Beckham J, Bhushan S, Boyko E, Cohen D, Dellitalia L, Faulk L, Fayad J, Fujii D, Gappy S, Gesek F, Greco J, Godschalk M, Gress T, Gupta S, Gutierrez S, Harley J, Hamner M, Hurley R, Iruvanti P, Jacono F, Jhala D, Kinlay S, Landry M, Liang P, Liangpunsakul S, Lichy J, Mahan C, Marrache R, Mastorides S, Mattocks K, Meyer P, Moorman J, Morgan T, Murdoch M, Norton J, Okusaga O, Oursler K, Poon S, Rauchman M, Servatius R, Sharma S, Smith R, Sriram P, Strollo P, Tandon N, Villareal G, Walsh J, Wells J, Whittle J, Whooley M, Wilson P, Xu J, Yeh S, Bast E, Dryden G, Hogan D, Joshi S, Lo T, Morales P, Naik E, Ong M, Petrakis I, Rai A, Yen A. Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder. JAMA Network Open 2025, 8: e2453913. PMID: 39786773, PMCID: PMC11718552, DOI: 10.1001/jamanetworkopen.2024.53913.Peer-Reviewed Original ResearchConceptsOpioid use disorder riskElectronic health record dataHealth record dataInternational Classification of DiseasesOpioid use disorderClassification of diseasesGenetic variantsInternational ClassificationGenetic riskRecord dataRisk of opioid use disorderMillion Veteran ProgramOpioid use disorder diagnosisUse disorderCase-control studyVeteran ProgramMain OutcomesDiagnostic codesClinical careOpioid exposurePharmacy recordsLogistic regressionRisk allelesNagelkerke R2Clinically useful model
2024
A Retrospective Study of Patiromer as Adjunct to Insulin Therapy for Acute Hyperkalemia in the Emergency Department
Goriacko P, Golestaneh L, Di Palo K. A Retrospective Study of Patiromer as Adjunct to Insulin Therapy for Acute Hyperkalemia in the Emergency Department. Open Access Emergency Medicine 2024, 16: 305-312. PMID: 39655083, PMCID: PMC11626971, DOI: 10.2147/oaem.s478693.Peer-Reviewed Original ResearchEmergency departmentElectronic health record dataHealth record dataAcute hyperkalemiaClinical benefitAdjunct to insulin therapyPatients treated with insulinDoses of regular insulinAdequate renal functionIntervention groupED settingAdjunct to insulinNet clinical benefitRetrospective cohort studyStudy of patientsPotassium reductionCohort studyRecord dataSecondary outcomesPatiromer groupPrimary outcomeIntravenous doseRenal functionRetrospective studyOverall cohortStudy protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform
Collaborative T, 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 2024, 5: 243. PMID: 39931522, PMCID: PMC11809169, DOI: 10.12688/wellcomeopenres.16353.2.Peer-Reviewed Original ResearchRisk prediction modelPrimary care electronic health records dataElectronic health record dataTime-varying measuresHealth record dataRisk of poor outcomesOpenSAFELY platformChronic disease settingsRestricted social contactDeath dataCOVID-19 related deathsWorld Health OrganizationCohort approachCOVID-19 deathsRecord dataCOVID-19Population of adult patientsHealth OrganizationRisk predictionOpenSAFELYSocial contactPerceived RiskPolicy changesRelated deathsAdult patientsMulticenter Analysis of the Relationship Between Operative Team Familiarity and Safety and Efficiency Outcomes in Cardiac Surgery
Bauer T, Janda A, Wu X, Ling C, Shook D, Querejeta-Roca G, Shann K, Smith T, Mathis M, Kaneko T, Sundt T, Schonberger R, Harrington S, Dias R, Pagani F, Likosky D, Yule S, Caldwell M, Corso J, Louis N, Krein S, Manojlovich M, Stakich-Alpirez K, Sturmer D, Yalamuri S, Lawton J, Abernathy J, Cleveland J, Clendenen N, Justison G, Fried M, Nemeh H, Fitzsimons M, Dickinson T, Stulak J, de la Cruz K. Multicenter Analysis of the Relationship Between Operative Team Familiarity and Safety and Efficiency Outcomes in Cardiac Surgery. Circulation Cardiovascular Quality And Outcomes 2024, 17: e011065. PMID: 39689169, PMCID: PMC11654451, DOI: 10.1161/circoutcomes.124.011065.Peer-Reviewed Original ResearchMeSH KeywordsAgedAnesthesiologistsCardiac Surgical ProceduresCardiopulmonary BypassClinical CompetenceCooperative BehaviorFemaleHealth Knowledge, Attitudes, PracticeHumansInterdisciplinary CommunicationMaleMiddle AgedOperative TimeOutcome and Process Assessment, Health CarePatient Care TeamPatient SafetyPostoperative ComplicationsQuality Indicators, Health CareRegistriesRetrospective StudiesRisk AssessmentRisk FactorsSurgeonsTime FactorsTreatment OutcomeUnited StatesConceptsSociety of Thoracic SurgeonsCardiopulmonary bypass durationTeam familiarityBypass durationElectronic health record dataHealth record dataEffective team dynamicsSociety of Thoracic Surgeons morbidityCardiac Surgery RegistryComposite major morbidityCardiac surgical proceduresMortality measuresLinear regression modelsCrude analysisQuaternary hospitalRecord dataRisk adjustmentPrimary outcomePatient outcomesProcedural efficiencyMajor morbidityCardiac surgeryCardiac operationsMulticenter analysisPatient ageTrends in Opioid Use Disorder in the Veterans Health Administration, 2005-2022
Gorfinkel L, Malte C, Fink D, Mannes Z, Wall M, Olfson M, Livne O, Keyhani S, Keyes K, Martins S, Cerdá M, Gutkind S, Maynard C, Saxon A, Simpson T, Gonsalves G, Lu H, McDowell Y, Hasin D. Trends in Opioid Use Disorder in the Veterans Health Administration, 2005-2022. JAMA Network Open 2024, 7: e2451821. PMID: 39705031, PMCID: PMC11662256, DOI: 10.1001/jamanetworkopen.2024.51821.Peer-Reviewed Original ResearchConceptsVeterans Health AdministrationOpioid use disorder diagnosisNon-Hispanic white patientsOpioid use disorderPrevalence of opioid use disorderCross-sectional studyHealth AdministrationVHA Corporate Data WarehouseVeterans Health Administration facilitiesElectronic health record dataNational Veterans Health AdministrationVeterans Health Administration patientsRecord dataNon-Hispanic black patientsElectronic medical record dataContinued public health effortsWhite patientsHealth record dataPublic health effortsCorporate Data WarehouseMedical record dataMultivariate logistic regression modelUse disorderLogistic regression modelsBurden of opioid use disorderRandomized in error in pragmatic clinical trials
Tong G, Coronado G, Li C, Li F. Randomized in error in pragmatic clinical trials. Contemporary Clinical Trials 2024, 148: 107764. PMID: 39603383, PMCID: PMC11752791, DOI: 10.1016/j.cct.2024.107764.Peer-Reviewed Original ResearchPragmatic clinical trialsElectronic health record dataHealth record dataPatient-reported dataExclusion of participantsSelection biasPatients' medical recordsAverage treatment effectOutcomes of participantsUsual carePragmatic trialTreatment effectsIntervention effectsIntention-to-treatOutcomes FrameworkPost-randomization exclusionsPotential outcomes frameworkRecord dataCovariate-adjusted estimatesMedical recordsParticipantsClinical trialsInterventionTrialsArm beingDisparities in Receipt of Medications for Opioid Use Disorder Before and During the COVID-19 Pandemic in the US Veterans Health Administration
Sung M, León C, Reisman J, Gordon K, Kerns R, Li W, Liu W, Mitra A, Yu H, Becker W. Disparities in Receipt of Medications for Opioid Use Disorder Before and During the COVID-19 Pandemic in the US Veterans Health Administration. Substance Abuse 2024, 46: 369-376. PMID: 39569566, DOI: 10.1177/29767342241293334.Peer-Reviewed Original ResearchMOUD receiptElixhauser Comorbidity IndexOpioid use disorderSocial determinants of healthVeterans Health Administration electronic health record dataElectronic health record dataUS Veterans Health AdministrationDeterminants of healthVeterans Health AdministrationHealth record dataProportion of veteransReceipt of medicationsSubstance useComorbidity indexTargeted outreach effortsUse disorderCOVID-19 pandemicProportion of patientsSocial determinantsReceipt of MOUDHealth AdministrationRecord dataEffect sizeMOUD engagementCalculated proportionsIntegrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability
Friligkou E, Koller D, Pathak G, Miller E, Lampert R, Stein M, Polimanti R. Integrating genome-wide information and wearable device data to explore the link of anxiety and antidepressants with pulse rate variability. Molecular Psychiatry 2024, 30: 2309-2315. PMID: 39558002, PMCID: PMC12107450, DOI: 10.1038/s41380-024-02836-7.Peer-Reviewed Original ResearchPolygenic risk scoresMendelian randomizationReuptake inhibitorsOne-sample Mendelian randomizationElectronic health record dataOne-sample MRHealth record dataMillion Veteran ProgramPotential causal effectSerotonin reuptake inhibitorsNorepinephrine reuptake inhibitorsGenome-wide association studiesEffects of anxietyImpact of anxietyWearable device dataUK BiobankVeteran ProgramAntidepressant prescriptionsAnxiety disordersAntidepressant medicationAntidepressant useAntidepressant exposureTricyclic antidepressantsPRS-CSRecord dataAppointment non-attendance is associated with disease modifying therapy persistence the following year
Gromisch E, Turner A, Leipertz S, Beauvais J, Haselkorn J. Appointment non-attendance is associated with disease modifying therapy persistence the following year. Multiple Sclerosis And Related Disorders 2024, 92: 106179. PMID: 39571216, DOI: 10.1016/j.msard.2024.106179.Peer-Reviewed Original ResearchAppointment non-attendanceNon-attendanceElectronic health record dataVeterans with MSHealth record dataDisease modifying therapiesHealthcare providersNon-attendance behaviorInitial disease modifying therapiesMultiple sclerosisRecord dataLogistic regressionAppointmentProportion of NSNon-persistenceAssociated with non-persistenceVeteransTime frameDemographicsData repositoriesYearsPersonsHealthcareModifying therapiesProportionNatural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsGuideline concordant opioid therapy in Veterans receiving VA and community care
Ma P, Cheng Y, Goulet J, Sandbrink F, Brandt C, Spevak C, Kean J, Becker W, Libin A, Shara N, Sheriff H, Houston J, Butler J, Workman E, Agrawal R, Kupersmith J, Zeng-Treitler Q. Guideline concordant opioid therapy in Veterans receiving VA and community care. BMC Health Services Research 2024, 24: 1284. PMID: 39456008, PMCID: PMC11515256, DOI: 10.1186/s12913-024-11742-1.Peer-Reviewed Original ResearchConceptsDual-system usersGuideline concordant careConcordant careVA servicesCommunity careElectronic health record dataHealth record dataRates of guideline concordanceVA Medical CenterOpioid therapyAdherence to specific guidelinesBaltimore VA Medical CenterGuideline adherenceGuideline concordanceAdherence ratesGuideline recommendationsRecord dataCareDemographic factorsMedical CenterVeteransComorbid conditionsOpioid crisisUrine drug screensSpecific guidelinesFactors Associated With Influenza Vaccination in a National Veteran Cohort
Chen A, Farmer M, Han L, Runels T, Bade B, Crothers K, Bastian L, Bazan I, Bean-Mayberry B, Brandt C, Akgün K. Factors Associated With Influenza Vaccination in a National Veteran Cohort. AJPM Focus 2024, 4: 100290. PMID: 39611140, PMCID: PMC11602634, DOI: 10.1016/j.focus.2024.100290.Peer-Reviewed Original ResearchOdds of vaccinationInfluenza vaccineFemale veteransAmerican Indian/Alaskan Native raceFactors associated with influenza vaccinationReceipt of influenza vaccinationDocumenting influenza vaccinationReduced oddsRetrospective cohort studyElectronic health record dataMale veteransHealth record dataAmbulatory care utilizationInfluenza seasonRuralMale sexWhite male veteransWhite veteransAssociated with raceCohort studyBlack veteransBlack raceEthnic groupsRacial disparitiesInfluenzaCombining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study
Mao J, Matheny M, Smolderen K, Mena-Hurtado C, Sedrakyan A, Goodney P. Combining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study. BMJ Open 2024, 14: e085806. PMID: 39327057, PMCID: PMC11429269, DOI: 10.1136/bmjopen-2024-085806.Peer-Reviewed Original ResearchConceptsElectronic health recordsElectronic health record dataClinical Research NetworkAmputation-free survivalPeripheral vascular interventionsResearch NetworkINSIGHT Clinical Research NetworkNew York CityHealth record dataIncreased riskLong-term outcomes of interventionsSecondary outcome measuresOutcomes of interventionsIncreased risk of deathYork CityRisk of deathObservational cohort studyHealth recordsRegistry dataClinical registryVascular Quality Initiative registryCohort studyOutcome assessmentRecord dataLong-term outcomesBrain Health Outcomes in Sexual and Gender Minority Groups
Huo S, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Sunmonu N, de Havenon A, Sarpong D, Rosendale N, Sheth K, Falcone G. Brain Health Outcomes in Sexual and Gender Minority Groups. Neurology 2024, 103: e209863. PMID: 39321407, DOI: 10.1212/wnl.0000000000209863.Peer-Reviewed Original ResearchConceptsBrain health outcomesSexual minoritiesGender minoritiesSGM groupHealth outcomesSGM personsLate-life depressionGender identitySexual orientationHigher odds of dementiaUS population-based studyElectronic health record dataOdds of dementiaHealth record dataGender minority groupsPopulation-based studyOdds of strokeCross-sectional studyMultivariate logistic regressionHealth disparitiesBaseline questionnaireNon-SGMSubgroups of genderTransgender womenUS adultsInpatient Use of Guideline-Directed Medical Therapy During Heart Failure Hospitalizations Among Community-Based Health Systems
Zheng J, Sandhu A, Bhatt A, Collins S, Flint K, Fonarow G, Fudim M, Greene S, Heidenreich P, Lala A, Testani J, Varshney A, Wi R, Ambrosy A. Inpatient Use of Guideline-Directed Medical Therapy During Heart Failure Hospitalizations Among Community-Based Health Systems. JACC Heart Failure 2024, 13: 43-54. PMID: 39269395, DOI: 10.1016/j.jchf.2024.08.004.Peer-Reviewed Original ResearchCommunity-based health systemQuality improvement registryHealth systemGuideline-directed medical therapyGuideline-directed medical therapy useEligible hospitalsImprovement registryNational quality improvement registryElectronic health record dataHealth record dataEvidence-based medicationsHF hospitalizationHeart failurePostdischarge strategiesHFrEF patientsFailure hospitalizationMedical therapyCommunity-BasedAngiotensin receptor-neprilysin inhibitorSodium-glucose cotransporter 2 inhibitorsInpatient useRecord dataRenin-angiotensin system inhibitorsMineralocorticoid receptor antagonistsWorsening Renal Function
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