2024
Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurementsBarriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure
Lu Y, Arowojolu O, Qiu X, Liu Y, Curry L, Krumholz H. Barriers to Optimal Clinician Guideline Adherence in Management of Markedly Elevated Blood Pressure. JAMA Network Open 2024, 7: e2426135. PMID: 39106065, PMCID: PMC11304113, DOI: 10.1001/jamanetworkopen.2024.26135.Peer-Reviewed Original ResearchConceptsBarriers to guideline adherenceElectronic health recordsGuideline adherenceClinician adherenceEHR dataElevated blood pressureHypertension managementAnalysis of EHR dataYale New Haven Health SystemSevere hypertensionClinical practice guidelinesAdherence scenariosQualitative content analysisPublic health challengeThematic saturationHealth recordsHealth systemBlood pressureThematic analysisTargeted interventionsManagement of severe hypertensionQualitative studyHealth challengesPractice guidelinesPatient outcomes
2023
Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography
Oikonomou E, Holste G, Mcnamara R, Velazquez E, Nadkarni G, Ouyang D, Krumholz H, Wang Z, Khera R. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography. European Heart Journal 2023, 44: ehad655.040. DOI: 10.1093/eurheartj/ehad655.040.Peer-Reviewed Original ResearchLeft ventricular ejection fractionSevere aortic stenosisAortic stenosisAS progressionAV VmaxTransthoracic echocardiographyYale New Haven Health SystemBaseline left ventricular ejection fractionAortic stenosis progressionModerate aortic stenosisRetrospective cohort studyVentricular ejection fractionTwo-dimensional echocardiographyMean rateModerate ASAS severityCohort studyEjection fractionPatient sexStenosis progressionTTE studiesEligible participantsSerial monitoringSpecialized centersTimely diagnosisDeveloping Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithmQuantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study
Lu Y, Linderman G, Mahajan S, Liu Y, Huang C, Khera R, Mortazavi B, Spatz E, Krumholz H. Quantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009258. PMID: 36883456, DOI: 10.1161/circoutcomes.122.009258.Peer-Reviewed Original ResearchConceptsRetrospective cohort studyBlood pressure valuesPatient characteristicsReal-world settingCohort studyPatient subgroupsYale New Haven Health SystemMean body mass indexSystolic blood pressure valuesBlood pressure visitHistory of hypertensionCoronary artery diseaseManagement of patientsMultivariable linear regression modelsBlood pressure readingsBody mass indexPatient-level measuresBlood pressure variationAbsolute standardized differencesNon-Hispanic whitesAntihypertensive medicationsReal-world practiceVisit variabilityArtery diseaseRegression modelsDeveloping an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records
Lu Y, Du C, Khidir H, Caraballo C, Mahajan S, Spatz E, Curry L, Krumholz H. Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009453. PMID: 36727515, DOI: 10.1161/circoutcomes.122.009453.Peer-Reviewed Original ResearchConceptsPersistent hypertensionElectronic health recordsBlood pressureHealth recordsPharmacologic agentsPrescribed treatmentYale New Haven Health SystemTreatment planAdditional pharmacologic agentsAntihypertensive treatment intensificationConsecutive outpatient visitsElevated blood pressurePersistence of hypertensionElectronic health record dataHealth record dataEligible patientsTreatment intensificationChart reviewHispanic patientsOutpatient visitsMean agePharmacological treatmentConventional content analysisHypertensionClinician notes
2022
A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H, Ross JS, Shah ND, Theel ES, Jenkinson WG, Knepper C, Wang K, Peaper D, Martinello RA, Brandt CA, Lin Z, Ko AI, Krumholz HM, Pollock BD, Schulz WL. A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. Npj Digital Medicine 2022, 5: 27. PMID: 35260762, PMCID: PMC8904579, DOI: 10.1038/s41746-022-00570-4.Peer-Reviewed Original ResearchCOVID-19 hospitalizationMayo ClinicDiagnosis codesCOVID-19 diagnosisPositive SARS-CoV-2 PCRYale New Haven Health SystemPositive SARS-CoV-2 testSARS-CoV-2 infectionSARS-CoV-2 PCRSARS-CoV-2 testCOVID-19Higher inhospital mortalitySARS-CoV2 infectionElectronic health record dataICD-10 diagnosisPositive laboratory testsHealth record dataInhospital mortalityAdditional patientsAntigen testSecondary diagnosisPrincipal diagnosisMulticenter evaluationPositive testComputable phenotype definitions
2020
Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure
Lu Y, Huang C, Mahajan S, Schulz WL, Nasir K, Spatz ES, Krumholz HM. Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure. Journal Of The American Heart Association 2020, 9: e015033. PMID: 32200730, PMCID: PMC7428633, DOI: 10.1161/jaha.119.015033.Peer-Reviewed Original ResearchConceptsDiastolic blood pressureSystolic blood pressureElevated blood pressureBlood pressureElectronic health recordsPopulation health surveillanceHealth recordsYale New Haven Health SystemHealth surveillanceHealth systemPatterns of patientsLarge health systemUsual careOutpatient encountersControl ratePatientsCare patternsPopulation healthMonthsHgSurveillancePrevalenceRecordsVisitsCare