2024
Natural 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 groupsComparative 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 discrimination
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 SurveyDetection 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) cohortUse 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 Research
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
2021
National Trends in Racial and Ethnic Disparities in Antihypertensive Medication Use and Blood Pressure Control Among Adults With Hypertension, 2011–2018
Lu Y, Liu Y, Dhingra LS, Massey D, Caraballo C, Mahajan S, Spatz ES, Onuma O, Herrin J, Krumholz HM. National Trends in Racial and Ethnic Disparities in Antihypertensive Medication Use and Blood Pressure Control Among Adults With Hypertension, 2011–2018. Hypertension 2021, 79: 207-217. PMID: 34775785, DOI: 10.1161/hypertensionaha.121.18381.Peer-Reviewed Original ResearchConceptsAntihypertensive medication usePoor hypertension controlOverall treatment rateLow control rateHypertension controlHypertension awarenessMedication useControl rateHispanic individualsEthnic differencesTreatment ratesBlack individualsGuideline-recommended medicationsBlood pressure controlWhite individualsNutrition Examination SurveyLow awareness rateAntihypertensive medicationsHypertensive peopleExamination SurveyIntensive medicationNational HealthAwareness rateEthnic disparitiesPressure control
2020
An Evaluation of the Vulnerable Physician Workforce in the USA During the Coronavirus Disease-19 Pandemic
Khera R, Dhingra LS, Jain S, Krumholz HM. An Evaluation of the Vulnerable Physician Workforce in the USA During the Coronavirus Disease-19 Pandemic. Journal Of General Internal Medicine 2020, 35: 3114-3116. PMID: 32495101, PMCID: PMC7269617, DOI: 10.1007/s11606-020-05854-7.Peer-Reviewed Original Research
2019
Predicting Hemodynamic Shock from Thermal Images using Machine Learning
Nagori A, Dhingra L, Bhatnagar A, Lodha R, Sethi T. Predicting Hemodynamic Shock from Thermal Images using Machine Learning. Scientific Reports 2019, 9: 91. PMID: 30643187, PMCID: PMC6331545, DOI: 10.1038/s41598-018-36586-8.Peer-Reviewed Original Research