2023
Quantifying 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 ResearchMeSH KeywordsAdolescentAdultBlood PressureBlood Pressure DeterminationFemaleHumansHypertensionMaleMiddle AgedRetrospective StudiesRisk FactorsConceptsRetrospective 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 models
2022
Physical Activity Among Patients With Intracardiac Remote Monitoring Devices Before, During, and After COVID-19–Related Restrictions
Lu Y, Jones PW, Murugiah K, Caraballo C, Massey DS, Mahajan S, Ahmed R, Bader EM, Krumholz HM. Physical Activity Among Patients With Intracardiac Remote Monitoring Devices Before, During, and After COVID-19–Related Restrictions. Journal Of The American College Of Cardiology 2022, 79: 309-310. PMID: 35057917, PMCID: PMC8763290, DOI: 10.1016/j.jacc.2021.11.010.Peer-Reviewed Original Research
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
2018
Gender Differences in Patient‐Reported Outcomes Among Adults With Atherosclerotic Cardiovascular Disease
Okunrintemi V, Valero‐Elizondo J, Patrick B, Salami J, Tibuakuu M, Ahmad S, Ogunmoroti O, Mahajan S, Khan SU, Gulati M, Nasir K, Michos ED. Gender Differences in Patient‐Reported Outcomes Among Adults With Atherosclerotic Cardiovascular Disease. Journal Of The American Heart Association 2018, 7: e010498. PMID: 30561253, PMCID: PMC6405598, DOI: 10.1161/jaha.118.010498.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAtherosclerosisCross-Sectional StudiesFemaleHealth CommunicationHealth Knowledge, Attitudes, PracticeHealth Status DisparitiesHealthcare DisparitiesHumansHydroxymethylglutaryl-CoA Reductase InhibitorsMaleMiddle AgedPatient Reported Outcome MeasuresPatient SatisfactionPhysician-Patient RelationsPlatelet Aggregation InhibitorsQuality of LifeRetrospective StudiesRisk FactorsSex FactorsUnited StatesYoung AdultConceptsHealth-related qualityBackground Atherosclerotic cardiovascular diseaseLower health-related qualityAtherosclerotic cardiovascular diseasePatient experienceCardiovascular diseaseHealth outcomesPoor patient-provider communicationImportant public health implicationsSelf-reported patient experienceNinth Revision codesPatient-centered outcomesPatient-reported outcomesPatient-provider communicationThird of deathsPoor perceptionPoor patient experiencePositive patient experiencePublic health implicationsMedical Expenditure PanelPerception of healthGender-specific differencesRepresentative US sampleGender differencesASCVD patientsEnhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort