2023
Timing of Blood Draws Among Patients Hospitalized in a Large Academic Medical Center
Caraballo C, Mahajan S, Murugiah K, Mortazavi B, Lu Y, Khera R, Krumholz H. Timing of Blood Draws Among Patients Hospitalized in a Large Academic Medical Center. JAMA 2023, 329: 255-257. PMID: 36648476, PMCID: PMC9856620, DOI: 10.1001/jama.2022.21509.Peer-Reviewed Original Research
2021
Scope and Social Determinants of Food Insecurity Among Adults With Atherosclerotic Cardiovascular Disease in the United States
Mahajan S, Grandhi GR, Valero‐Elizondo J, Mszar R, Khera R, Acquah I, Yahya T, Virani SS, Blankstein R, Blaha MJ, Cainzos‐Achirica M, Nasir K. Scope and Social Determinants of Food Insecurity Among Adults With Atherosclerotic Cardiovascular Disease in the United States. Journal Of The American Heart Association 2021, 10: e020028. PMID: 34387089, PMCID: PMC8475063, DOI: 10.1161/jaha.120.020028.Peer-Reviewed Original ResearchConceptsHigh-risk characteristicsUS adultsNational Health Interview Survey dataHealth Interview Survey dataAtherosclerotic cardiovascular diseaseCoronary heart diseaseSelf-reported diagnosisNon-Hispanic blacksInterview Survey dataFood Security Survey ModuleCardiovascular disease resultsLow family incomeAdult Food Security Survey ModuleFood insecurityHeart diseaseASCVDCardiovascular diseasePocket healthcare expenditureHigher oddsSociodemographic determinantsDisease resultsStudy participantsSocial determinantsHealthcare expendituresSociodemographic subgroups
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
Enhancing 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