2023
An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model
Charkoftaki G, Aalizadeh R, Santos-Neto A, Tan W, Davidson E, Nikolopoulou V, Wang Y, Thompson B, Furnary T, Chen Y, Wunder E, Coppi A, Schulz W, Iwasaki A, Pierce R, Cruz C, Desir G, Kaminski N, Farhadian S, Veselkov K, Datta R, Campbell M, Thomaidis N, Ko A, Thompson D, Vasiliou V. An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model. Human Genomics 2023, 17: 80. PMID: 37641126, PMCID: PMC10463861, DOI: 10.1186/s40246-023-00521-4.Peer-Reviewed Original ResearchConceptsCOVID-19 patientsDisease severityViral outbreaksFuture viral outbreaksLength of hospitalizationIntensive care unitWorse disease prognosisLife-threatening illnessEffective medical interventionsCOVID-19Clinical decision treeGlucuronic acid metabolitesNew potential biomarkersHospitalization lengthCare unitComorbidity dataSerotonin levelsDisease progressionHealthy controlsPatient outcomesDisease prognosisPatient transferPatientsHealthcare resourcesPotential biomarkers
2021
Temporal relationship of computed and structured diagnoses in electronic health record data
Schulz WL, Young HP, Coppi A, Mortazavi BJ, Lin Z, Jean RA, Krumholz HM. Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics And Decision Making 2021, 21: 61. PMID: 33596898, PMCID: PMC7890604, DOI: 10.1186/s12911-021-01416-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsStructured diagnosisOutpatient blood pressureElectronic health record dataAcademic health systemLow-density lipoproteinHealth record dataBlood pressureStructured data elementsAdministrative claimsHypertensionClinical informationHyperlipidemiaClinical phenotypeEquivalent diagnosisVital signsHealth systemDiagnosisProblem listAdditional studiesHealth recordsRecord dataTimely accessEHR dataPatients
2018
Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators
Bates J, Parzynski CS, Dhruva SS, Coppi A, Kuntz R, Li S, Marinac‐Dabic D, Masoudi FA, Shaw RE, Warner F, Krumholz HM, Ross JS. Quantifying the utilization of medical devices necessary to detect postmarket safety differences: A case study of implantable cardioverter defibrillators. Pharmacoepidemiology And Drug Safety 2018, 27: 848-856. PMID: 29896873, PMCID: PMC6436550, DOI: 10.1002/pds.4565.Peer-Reviewed Original ResearchConceptsAdverse event ratesSafety differencesEvent ratesMedical device utilizationICD utilizationRate ratioNational Cardiovascular Data RegistryICD modelsImplantable cardioverter defibrillatorEvent rate ratioMost patientsCardioverter defibrillatorProportion of individualsAmerican CollegeData registryRoutine surveillanceSample size estimatesAverage event rateDevice utilizationSignificance levelDifferencesPatientsRegistryDefibrillatorICD
2017
Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures
Mortazavi B, Desai N, Zhang J, Coppi A, Warner F, Krumholz H, Negahban S. Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE Journal Of Biomedical And Health Informatics 2017, 21: 1719-1729. PMID: 28287993, DOI: 10.1109/jbhi.2017.2675340.Peer-Reviewed Original ResearchConceptsMajor cardiovascular proceduresElectronic health recordsRespiratory failureAdverse eventsCardiovascular proceduresYale-New Haven HospitalPostoperative respiratory failurePatient cohortHospital costsPatient outcomesSpecific patientPatientsHealth recordsCohort-specific modelsCharacteristic curveInfectionFailureHospitalCohortClinicians