2024
Validating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism
Bikdeli B, Khairani C, Bejjani A, Lo Y, Mahajan S, Caraballo C, Jimenez J, Krishnathasan D, Zarghami M, Rashedi S, Jimenez D, Barco S, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Mojibian H, Aneja S, Khera R, Konstantinides S, Goldhaber S, Wang L, Zhou L, Monreal M, Piazza G, Krumholz H, Investigators P. Validating International Classification of Diseases Code (ICD) 10th Revision Algorithms for Accurate Identification of Pulmonary Embolism. Journal Of Thrombosis And Haemostasis 2024 PMID: 39505153, DOI: 10.1016/j.jtha.2024.10.013.Peer-Reviewed Original ResearchDischarge codesInternational ClassificationICD-10Yale New Haven Health SystemPositive predictive valueMass General Brigham hospitalsAccuracy of ICD-10ICD-10 codesPulmonary embolismHealth systemImage codingElectronic databasesF1 scorePre-specified protocolExcellent positive predictive valueIndependent physiciansHighest F1 scoreIdentification of pulmonary embolismAcute pulmonary embolismSecondary codePE codesScoresIdentified PERevised algorithmLocal large language models for privacy-preserving accelerated review of historic echocardiogram reports
Vaid A, Duong S, Lampert J, Kovatch P, Freeman R, Argulian E, Croft L, Lerakis S, Goldman M, Khera R, Nadkarni G. Local large language models for privacy-preserving accelerated review of historic echocardiogram reports. Journal Of The American Medical Informatics Association 2024, 31: 2097-2102. PMID: 38687616, PMCID: PMC11339495, DOI: 10.1093/jamia/ocae085.Peer-Reviewed Original ResearchLanguage modelEchocardiogram reportsGround-truth answersText similarity measuresMount Sinai Health SystemQuestion-answer pairsEnhancing clinical decision-makingSinai Health SystemIntervention identificationClinical decision-makingHealth systemPatient careComplex patient dataRelevant snippetsSimilarity measureComplex cardiac diseaseGround truthReal-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
Developing 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 reportsAlgorithm
2021
Financial burden, distress, and toxicity in cardiovascular disease
Slavin SD, Khera R, Zafar SY, Nasir K, Warraich HJ. Financial burden, distress, and toxicity in cardiovascular disease. American Heart Journal 2021, 238: 75-84. PMID: 33961830, DOI: 10.1016/j.ahj.2021.04.011.Peer-Reviewed Original ResearchConceptsCardiovascular diseaseFinancial burdenCommunity Health Worker IntegrationHigh-risk individualsComparative effectiveness studiesNon-medical needsHigh-cost interventionsHigh-cost treatmentsCVD managementEffectiveness studiesHealth systemPsychological distressInsurance coverageHealthcare policyBurdenDistressDiseaseSystem navigationInterventionCommunity-based initiativesPatientsPhysicians
2020
The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers.
Mori M, Khera R, Lin Z, Ross JS, Schulz W, Krumholz HM. The Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers. Methodist DeBakey Cardiovascular Journal 2020, 16: 212-219. PMID: 33133357, PMCID: PMC7587314, DOI: 10.14797/mdcj-16-3-212.Commentaries, Editorials and LettersConceptsLearning health systemLearning systemCommon data modelDynamic learning systemAdvanced analyticsBig dataData assetsData modelDigital solutionsCustomer interactionContinuous learningKnowledge generationEffective useConceptual modelAnalyticsSystemGoogleHealth systemLearningComparable scaleModelDataCompanies