2024
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers
Dhingra L, Sangha V, Aminorroaya A, Bryde R, Gaballa A, Ali A, Mehra N, Krumholz H, Sen S, Kramer C, Martinez M, Desai M, Oikonomou E, Khera R. A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers. The American Journal Of Cardiology 2024 PMID: 39581517, DOI: 10.1016/j.amjcard.2024.11.028.Peer-Reviewed Original ResearchCleveland Clinic FoundationHypertrophic cardiomyopathyMedian follow-up periodHypertrophic cardiomyopathy therapyMonitoring treatment responseFollow-up periodImpact of therapyAtlantic Health SystemLack of improvementOral alternativePost-SRTMedical therapyTreatment responseMulticenter evaluationInterventricular septumPercutaneous reductionMavacamtenTherapyPatientsClinic FoundationPoint-of-care monitoringECGECG imagesScoresHealth systemNatural 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 groupsAutomated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing
Nargesi A, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni G, Lin Z, Ahmad F, Krumholz H, Khera R. Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing. JACC Heart Failure 2024 PMID: 39453355, DOI: 10.1016/j.jchf.2024.08.012.Peer-Reviewed Original ResearchReduced ejection fractionEjection fractionHeart failureLeft ventricular ejection fractionVentricular ejection fractionYale-New Haven HospitalIdentification of patientsCommunity hospitalIdentification of heart failureLanguage modelNorthwestern MedicineMeasure care qualityQuality of careNew Haven HospitalDeep learning-based natural language processingHFrEFGuideline-directed careDeep learning language modelsMIMIC-IIIDetect HFrEFNatural language processingReclassification improvementHospital dischargePatientsCare qualityArtificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Oikonomou E, Sangha V, Dhingra L, Aminorroaya A, Coppi A, Krumholz H, Baldassarre L, Khera R. Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images. Circulation Cardiovascular Quality And Outcomes 2024 PMID: 39221857, DOI: 10.1161/circoutcomes.124.011504.Peer-Reviewed Original ResearchCancer therapeutics-related cardiac dysfunctionGlobal longitudinal strainLeft ventricular systolic dysfunctionCardiac dysfunctionBreast cancerNon-Hodgkin lymphoma therapyNon-Hodgkin's lymphomaVentricular systolic dysfunctionAssociated with worse global longitudinal strainRisk stratification strategiesHigh-risk groupMonths post-treatmentPost hoc analysisElectrocardiographic (ECGTrastuzumab exposureLymphoma therapySystolic dysfunctionAI-ECGBefore treatmentRisk biomarkersLongitudinal strainLow riskStratification strategiesHigher incidencePositive screenComparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM
Khera R, Aminorroaya A, Dhingra L, Thangaraj P, Pedroso Camargos A, Bu F, Ding X, Nishimura A, Anand T, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Kaur G, Lau W, Li J, Li K, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLeggon J, McLemore M, Minty E, Morales D, Nagy P, Ostropolets A, Pistillo A, Phan T, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager S, Simon K, Viernes B, Yang J, Yin C, You S, Zhou J, Ryan P, Schuemie M, Krumholz H, Hripcsak G, Suchard M. Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes A Multinational, Federated Analysis of LEGEND-T2DM. Journal Of The American College Of Cardiology 2024, 84: 904-917. PMID: 39197980, DOI: 10.1016/j.jacc.2024.05.069.Peer-Reviewed Original ResearchConceptsGLP-1 RAsSecond-line agentsGLP-1Antihyperglycemic agentsCardiovascular diseaseMACE riskGlucagon-like peptide-1 receptor agonistsSodium-glucose cotransporter 2 inhibitorsPeptide-1 receptor agonistsDipeptidyl peptidase-4 inhibitorsEffects of SGLT2isType 2 diabetes mellitusPeptidase-4 inhibitorsAdverse cardiovascular eventsCox proportional hazards modelsRandom-effects meta-analysisCardiovascular risk reductionTarget trial emulationProportional hazards modelPerformance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study
Camargos A, Barreto S, Brant L, Ribeiro A, Dhingra L, Aminorroaya A, Bittencourt M, Figueiredo R, Khera R. Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study. Open Heart 2024, 11: e002762. PMID: 38862252, PMCID: PMC11168182, DOI: 10.1136/openhrt-2024-002762.Peer-Reviewed Original ResearchConceptsPooled Cohort EquationsELSA-BrasilRisk scoreCardiovascular diseaseCVD eventsCommunity-based cohort studyArea under the receiver operating characteristic curveCVD risk scoreELSA-Brasil studyIncident CVD eventsMiddle-income countriesAdjudicated CVD eventsCardiovascular disease riskCVD scoreCohort EquationsNational guidelinesRisk stratification scoresWhite womenAge/sex groupsCohort studyProspective cohortLMICsSex/race groupsHigher incomeRisk discriminationDevelopment and multinational validation of an algorithmic strategy for high Lp(a) screening
Aminorroaya A, Dhingra L, Oikonomou E, Saadatagah S, Thangaraj P, Vasisht Shankar S, Spatz E, Khera R. Development and multinational validation of an algorithmic strategy for high Lp(a) screening. Nature Cardiovascular Research 2024, 3: 558-566. PMID: 39195936, DOI: 10.1038/s44161-024-00469-1.Peer-Reviewed Original ResearchElectronic health recordsAssociated with premature atherosclerotic cardiovascular diseaseElevated Lp(aHealth recordsUK BiobankPremature atherosclerotic cardiovascular diseaseMachine learning modelsAtherosclerotic cardiovascular diseaseCohort studyReal-world settingsTargeted screeningCardiovascular diseaseLearning modelsNovel targeted therapeuticsAlgorithmic strategiesCohortProbability thresholdScreeningClinical featuresValidation cohortElevated lipoproteinRisk inspectionARICLp(aReal-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
Oikonomou E, Aminorroaya A, Dhingra L, Partridge C, Velazquez E, Desai N, Krumholz H, Miller E, Khera R. Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study. European Heart Journal - Digital Health 2024, 5: 303-313. PMID: 38774380, PMCID: PMC11104476, DOI: 10.1093/ehjdh/ztae023.Peer-Reviewed Original ResearchRisk of acute myocardial infarctionAssociated with lower oddsHospital health systemCoronary artery diseaseCardiac testingRisk of adverse outcomesUK BiobankHealth systemProvider-drivenLower oddsAssociated with better outcomesAcute myocardial infarctionBlack raceStable chest painFemale sexReal world evaluationDiabetes historyMulticohort studyFunction testsSuspected coronary artery diseaseYounger ageRisk profileAdverse outcomesMultinational cohortPost hoc analysis
2023
National Trends in Racial and Ethnic Disparities in Use of Recommended Therapies in Adults with Atherosclerotic Cardiovascular Disease, 1999-2020
Lu Y, Liu Y, Dhingra L, Caraballo C, Mahajan S, Massey D, Spatz E, Sharma R, Rodriguez F, Watson K, Masoudi F, Krumholz H. National Trends in Racial and Ethnic Disparities in Use of Recommended Therapies in Adults with Atherosclerotic Cardiovascular Disease, 1999-2020. JAMA Network Open 2023, 6: e2345964. PMID: 38039001, PMCID: PMC10692850, DOI: 10.1001/jamanetworkopen.2023.45964.Peer-Reviewed Original ResearchConceptsAtherosclerotic cardiovascular diseaseHistory of ASCVDCross-sectional studyLifestyle modificationPharmacological medicationsOptimal careCurrent careUS adultsEthnic differencesWhite individualsGuideline-recommended therapiesTotal cholesterol controlNon-Hispanic white individualsNutrition Examination SurveyLatino individualsQuality of careSelf-reported raceStatin useRecommended TherapiesSecondary preventionCholesterol controlOptimal regimensSmoking cessationEligible participantsExamination SurveyMultinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM
Khera R, Dhingra L, Aminorroaya A, Li K, Zhou J, Arshad F, Blacketer C, Bowring M, Bu F, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Horban S, Lau W, Li J, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLemore M, Minty E, Morales D, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada J, Pratt N, Reyes C, Ross J, Seager S, Shah N, Simon K, Wan E, Yang J, Yin C, You S, Schuemie M, Ryan P, Hripcsak G, Krumholz H, Suchard M. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Medicine 2023, 2: e000651. PMID: 37829182, PMCID: PMC10565313, DOI: 10.1136/bmjmed-2023-000651.Peer-Reviewed Original ResearchType 2 diabetes mellitusSecond-line treatmentCardiovascular risk groupsDiabetes mellitusCardiovascular diseaseAntihyperglycaemic drugsLine treatmentRisk groupsObservational Health Data SciencesGlucagon-like peptide-1 receptor agonistsElectronic health recordsSodium-glucose cotransporter 2 inhibitorsCalendar year trendsPeptide-1 receptor agonistsUS databaseOutcomes of patientsCotransporter 2 inhibitorsAdministrative claims databaseSecond-line drugsHealth recordsSodium-glucose cotransporter-2 inhibitorsMedication useMetformin monotherapyGuideline recommendationsOutcome measuresUse of Smart Devices to Track Cardiovascular Health Goals in the United States
Aminorroaya A, Dhingra L, Nargesi A, Oikonomou E, Krumholz H, Khera R. Use of Smart Devices to Track Cardiovascular Health Goals in the United States. JACC Advances 2023, 2: 100544. PMID: 38094515, PMCID: PMC10718569, DOI: 10.1016/j.jacadv.2023.100544.Peer-Reviewed Original ResearchHealth goalsRisk of cardiovascular diseaseCardiovascular risk factorsNationally representative Health Information National Trends SurveyHealth Information National Trends SurveyU.S. adultsCardiovascular diseaseNational Trends SurveyRisk factors of hypertensionDigital health interventionsCardiovascular health goalsHealth-related goalsRisk of CVDFactors of hypertensionU.S. adult populationCardiovascular risk managementHigher educational attainmentLow-income individualsSmart devicesTrends SurveyImprove careHealth interventionsNational estimatesRisk factorsSurvey participantsCardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra L, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. The American Journal Of Cardiology 2023, 203: 136-148. PMID: 37499593, PMCID: PMC10865722, DOI: 10.1016/j.amjcard.2023.06.104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsElectronic health recordsData elementsUnstructured data streamsUnstructured data elementsNatural language processingCommon data modelHealth recordsStructured data elementsComputer visionUnstructured dataData streamsHeterogeneity challengesSeamless deliveryData modelLanguage processingData storageFree textClinical narrativesComputational phenotypesOngoing workPatient informationRapid innovationSpecific expertiseConfidentialityOngoing innovationDetection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images
Sangha V, Nargesi A, Dhingra L, Khunte A, Mortazavi B, Ribeiro A, Banina E, Adeola O, Garg N, Brandt C, Miller E, Ribeiro A, Velazquez E, Giatti L, Barreto S, Foppa M, Yuan N, Ouyang D, Krumholz H, Khera R. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images. Circulation 2023, 148: 765-777. PMID: 37489538, PMCID: PMC10982757, DOI: 10.1161/circulationaha.122.062646.Peer-Reviewed Original ResearchConceptsLV systolic dysfunctionYale-New Haven HospitalVentricular systolic dysfunctionSystolic dysfunctionLV ejection fractionBrazilian Longitudinal StudyNew Haven HospitalEjection fractionCardiology clinicRegional hospitalLeft ventricular systolic dysfunctionCedars-Sinai Medical CenterAdult Health (ELSA-Brasil) cohortDetection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingUse of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020
Dhingra L, Aminorroaya A, Oikonomou E, Nargesi A, Wilson F, Krumholz H, Khera R. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA Network Open 2023, 6: e2316634. PMID: 37285157, PMCID: PMC10248745, DOI: 10.1001/jamanetworkopen.2023.16634.Peer-Reviewed Original ResearchConceptsHealth Information National Trends SurveyUS adultsExacerbate disparitiesWearable device usersCardiovascular diseaseCardiovascular healthPopulation-based cross-sectional studySelf-reported cardiovascular diseaseCardiovascular disease risk factorsNational Trends SurveyOverall US adult populationCardiovascular risk factor profileSelf-reported accessAssociated with lower useUse of wearable devicesImprove cardiovascular healthLower household incomeLower educational attainmentUS adult populationRisk factor profileNationally representative sampleCross-sectional studyProportion of adultsTrends SurveyWearable device dataNonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.
Liu Y, Herrin J, Huang C, Khera R, Dhingra L, Dong W, Mortazavi B, Krumholz H, Lu Y. Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys. Journal Of The American Medical Informatics Association 2023, 30: 943-952. PMID: 36905605, PMCID: PMC10114129, DOI: 10.1093/jamia/ocad035.Peer-Reviewed Original ResearchOcular complications of plasma cell dyscrasias
Singh R, Singhal S, Sinha S, Cho J, Nguyen A, Dhingra L, Kaur S, Sharma V, Agarwal A. Ocular complications of plasma cell dyscrasias. European Journal Of Ophthalmology 2023, 33: 1786-1800. PMID: 36760117, PMCID: PMC10472748, DOI: 10.1177/11206721231155974.Peer-Reviewed Original ResearchA Cross-Sectional Questionnaire-Based Study to Assess Anxiety among Healthcare Professionals in India during COVID-19 Pandemic
Arushi, Agarwal M, Patel L, Agrawal S, Kaur S, Dhingra L, Boparai G, Sarkar S, Ranjan P, Khakha D, Sharan P. A Cross-Sectional Questionnaire-Based Study to Assess Anxiety among Healthcare Professionals in India during COVID-19 Pandemic. Indian Journal Of Social Psychiatry 2023, 39: 90-94. DOI: 10.4103/ijsp.ijsp_28_21.Peer-Reviewed Original ResearchHealthcare professionalsModerate/severe anxietyQuestionnaire-based studyMental health of healthcare professionalsHealth of healthcare professionalsAssess anxietyProportion of healthcare professionalsCross-sectional questionnaire-based studyGeneralized Anxiety Disorder 7 scaleMental health concernsFamily membersIndian healthcare workersLong working hoursNursing personnelFear of beingConvenience sampleMental healthEmotional distressResident doctorsHealthcare workersShortage of personal protective equipmentHealthcareInadequate sleepPersonal protective equipmentProfessionals
2022
Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens
Awasthi R, Rakholia V, Agrawal S, Dhingra L, Nagori A, Kaur H, Sethi T. Estimating the impact of health systems factors on antimicrobial resistance in priority pathogens. Journal Of Global Antimicrobial Resistance 2022, 30: 133-142. PMID: 35533985, DOI: 10.1016/j.jgar.2022.04.021.Peer-Reviewed Original ResearchConceptsGlobal Burden of DiseaseHealth system factorsAntimicrobial resistanceReducing antimicrobial resistanceSystemic factorsBurden of diseaseHigh-income countriesResistance to ceftarolineDeterminants of antimicrobial resistanceDisease burden variablesObstetric careAntimicrobial resistance dataCausal machine learningQuality of governanceGlobal antimicrobial resistanceBurden variablesHealth approachBayesian decision networkGlobal burdenDesign interventionsAntibiotic susceptibilityKnowledge discovery approachGovernment effectivenessPriority pathogensGeopolitical factorsA machine learning application for raising WASH awareness in the times of COVID-19 pandemic
Pandey R, Gautam V, Pal R, Bandhey H, Dhingra L, Misra V, Sharma H, Jain C, Bhagat K, Arushi, Patel L, Agarwal M, Agrawal S, Jalan R, Wadhwa A, Garg A, Agrawal Y, Rana B, Kumaraguru P, Sethi T. A machine learning application for raising WASH awareness in the times of COVID-19 pandemic. Scientific Reports 2022, 12: 810. PMID: 35039533, PMCID: PMC8764038, DOI: 10.1038/s41598-021-03869-6.Peer-Reviewed Original ResearchConceptsMachine learning applicationsMachine learningArtificial intelligenceLearning applicationsContinuous machine learningConversational artificial intelligenceHealth misinformationLeverage machine learningConversational AIMachine translationNeural modelAI chatbotsDelivery of informationRight informationCombat misinformationMachineHealth appsMHealth platformAppsLearningNews contentVernacular languageInformationLocal languageCOVID-19 information