2022
Investigation of hypertension and type 2 diabetes as risk factors for dementia in the All of Us cohort
Nagar S, Pemu P, Qian J, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Ramirez A, Schully S, Able A, Green A, Zuchner S, Jordan I, Meller R. Investigation of hypertension and type 2 diabetes as risk factors for dementia in the All of Us cohort. Scientific Reports 2022, 12: 19797. PMID: 36396674, PMCID: PMC9672061, DOI: 10.1038/s41598-022-23353-z.Peer-Reviewed Original ResearchConceptsAssociation of hypertensionPrevalence of dementiaType 2 diabetesRisk factorsUS populationRace/ethnicityHigh prevalenceLarge observational cohort studyMultivariable logistic regression modelRisk factor modificationObservational cohort studyT2D risk factorsInvestigation of hypertensionAssociation of T2DOdds of dementiaRace/ethnicity groupsAssociation of sexCross-sectional analysisLogistic regression modelsWorld Health OrganizationElectronic health recordsConcurrent hypertensionModifiable comorbiditiesCohort studyFinal cohortSimplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease
Nguyen N, Patel S, Gabunilas J, Qian A, Cecil A, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease. Clinical And Translational Gastroenterology 2022, 13: e00507. PMID: 35905414, PMCID: PMC10476830, DOI: 10.14309/ctg.0000000000000507.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseUnplanned healthcare utilizationAdult patientsBowel diseaseHealthcare utilizationHealthcare costsLogistic regressionRetrospective cohort studyNationwide Readmissions DatabaseIdentification of patientsAdministrative claims dataHigh-cost patientsHNHC patientsCohort studyHospitalized patientsClaims dataHigh riskPatientsTraditional logistic regressionDerivation dataMean AUCIBDMean areaCharacteristic curveDiseaseInclusion of social determinants of health improves sepsis readmission prediction models
Amrollahi F, Shashikumar S, Meier A, Ohno-Machado L, Nemati S, Wardi G. Inclusion of social determinants of health improves sepsis readmission prediction models. Journal Of The American Medical Informatics Association 2022, 29: 1263-1270. PMID: 35511233, PMCID: PMC9196687, DOI: 10.1093/jamia/ocac060.Peer-Reviewed Original ResearchMeSH KeywordsHumansLogistic ModelsPatient ReadmissionRetrospective StudiesRisk FactorsSepsisSocial Determinants of HealthConceptsUnplanned readmissionSepsis patientsReadmission modelsClinical/laboratory featuresSocial determinantsUnplanned hospital readmissionHigh-risk patientsObjective clinical dataLow predictive valueReadmission prediction modelsSepsis readmissionsLaboratory featuresSepsis casesHospital readmissionPredictive factorsClinical dataReadmissionHigh riskPredictive valueSDH factorsMedical carePatientsDemographic featuresLarger studyProgram cohortEpidemiology of atrial fibrillation in the All of Us Research Program
Alonso A, Alam A, Kamel H, Subbian V, Qian J, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena-Cortes R, Mayo K, Mockrin S, Ohno-Machado L, Schully S, Ramirez A, Greenland P. Epidemiology of atrial fibrillation in the All of Us Research Program. PLOS ONE 2022, 17: e0265498. PMID: 35294480, PMCID: PMC8926244, DOI: 10.1371/journal.pone.0265498.Peer-Reviewed Original ResearchConceptsAtrial fibrillationNon-Hispanic whitesUs Research ProgramRisk factorsEpidemiology of AFIncidence of AFPresence of AFHigher body mass indexStudy of AFClinical risk factorsIncident atrial fibrillationCoronary heart diseaseBody mass indexElectronic health record dataMedical history dataMedical history surveyNon-Hispanic blacksHealth record dataAvailable EHR dataNon-Hispanic AsiansHeart failureStudy enrollmentMass indexEligible participantsMean age
2020
Active Surveillance of the Implantable Cardioverter-Defibrillator Registry for Defibrillator Lead Failures
Resnic F, Majithia A, Dhruva S, Ssemaganda H, Robbins S, Marinac-Dabic D, Hewitt K, Ohno-Machado L, Reynolds M, Matheny M. Active Surveillance of the Implantable Cardioverter-Defibrillator Registry for Defibrillator Lead Failures. Circulation Cardiovascular Quality And Outcomes 2020, 13: e006105. PMID: 32283971, PMCID: PMC7360169, DOI: 10.1161/circoutcomes.119.006105.Peer-Reviewed Original ResearchConceptsICD RegistryLead failureActive surveillanceNational Cardiovascular Data Registry ICD RegistryImplantable Cardioverter-Defibrillator RegistryPrimary safety end pointPropensity-matched survival analysisRate of freedomSafety end pointLead failure rateLong-term safetySignificant patient harmDefibrillator lead failureEarly lead failureMonitoring of safetyComparator patientsContemporary ICDLead survivalMeaningful differencesOutcome ascertainmentFailure rateNew ICDPatient harmPatientsSurvival analysis
2018
Infections and Cardiovascular Complications are Common Causes for Hospitalization in Older Patients with Inflammatory Bowel Diseases
Nguyen N, Ohno-Machado L, Sandborn W, Singh S. Infections and Cardiovascular Complications are Common Causes for Hospitalization in Older Patients with Inflammatory Bowel Diseases. Inflammatory Bowel Diseases 2018, 24: 916-923. PMID: 29562273, DOI: 10.1093/ibd/izx089.Peer-Reviewed Original ResearchConceptsInflammatory bowel diseaseOlder patientsYounger patientsAnnual burdenNationwide Readmissions Database 2013Treatment-related complicationsDisease-related complicationsCause of hospitalizationRepresentative cohort studyCohort studyBowel diseaseHigh riskPatientsMore daysHospitalizationComplicationsBurdenCauseRehospitalizationHospitalTherapyCohortPrevalenceDisease
2017
A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries
Gabriel R, Waterman R, Kim J, Ohno-Machado L. A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries. Anesthesia & Analgesia 2017, 124: 1529-1536. PMID: 28079580, DOI: 10.1213/ane.0000000000001827.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge FactorsAgedAged, 80 and overAmbulatory Surgical ProceduresAnesthesiaChildChild, PreschoolCritical CareFemaleForecastingHumansHypertensionInfantInfant, NewbornIntensive Care UnitsLength of StayLogistic ModelsMaleMiddle AgedModels, StatisticalObesity, MorbidPostoperative CareRisk FactorsROC CurveYoung AdultConceptsPACU lengthPostanesthesia care unit lengthPrimary anesthesia typePostanesthesia care unitHosmer-Lemeshow testLogistic regression modelsAnesthesia typeMorbid obesityCare unitHL testOutpatient surgeryOutpatient procedureSingle institutionHigher oddsNonsignificant P valuesStayPatientsSurgical specialtiesROC curveGood calibrationCharacteristic curveExcellent discriminationAUC valuesP-valueBackward eliminationA risk prediction score for acute kidney injury in the intensive care unit
Malhotra R, Kashani K, Macedo E, Kim J, Bouchard J, Wynn S, Li G, Ohno-Machado L, Mehta R. A risk prediction score for acute kidney injury in the intensive care unit. Nephrology Dialysis Transplantation 2017, 32: 814-822. PMID: 28402551, DOI: 10.1093/ndt/gfx026.Peer-Reviewed Original ResearchConceptsAcute kidney injuryIntensive care unitAcute risk factorsRisk score modelICU admissionKidney injuryCare unitValidation cohortKidney diseaseRisk factorsTest cohortTreatment of AKIAtherosclerotic coronary vascular diseaseMulticenter prospective cohort studyGlobal Outcomes criteriaChronic kidney diseaseHigh-risk patientsProspective cohort studyChronic liver diseaseCongestive heart failureTime of screeningCoronary vascular diseaseRisk prediction scoreEarly therapeutic interventionExternal validation cohort
2015
The differential associations of preexisting conditions with trauma-related outcomes in the presence of competing risks
Calvo R, Lindsay S, Edland S, Macera C, Wingard D, Ohno-Machado L, Sise M. The differential associations of preexisting conditions with trauma-related outcomes in the presence of competing risks. Injury 2015, 47: 677-684. PMID: 26684173, DOI: 10.1016/j.injury.2015.10.055.Peer-Reviewed Original ResearchConceptsPre-existing chronic conditionsOlder trauma patientsHospital mortalityTrauma patientsMortality riskHigh-risk groupLow-risk subgroupsLower mortality riskLonger HLOSHospital lengthMost patientsRisk patientsClinical outcomesLiver diseaseRetrospective studyTrauma populationTrauma-related outcomesRisk regressionChronic conditionsExcess mortalityParkinson's diseaseLevel ICare facilitiesPatientsClinical decision
2014
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014, 33: 1123-1131. PMID: 25006137, DOI: 10.1377/hlthaff.2014.0041.Commentaries, Editorials and LettersConceptsBig dataClinical analyticsPrivacy concernsUse casesElectronic health recordsAnalyticsTypes of dataHealth recordsTypes of insightsNecessary analysisSupport of researchHigh-cost patientsUnprecedented opportunityMonitoring devicesCostHealth careAlgorithmMultiple organ systemsRapid progressInfrastructureUS health care systemHealth care systemSystemAdverse eventsClinical data
2012
An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors
Sarin P, Urman R, Ohno-Machado L. An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors. Journal Of The American Medical Informatics Association 2012, 19: 995-1002. PMID: 22582204, PMCID: PMC3534465, DOI: 10.1136/amiajnl-2012-000872.Peer-Reviewed Original ResearchConceptsAmbulatory surgery dataExperimental modelNon-modifiable patient characteristicsApfel risk scoreAmbulatory surgery patientsGood calibrationLogistic regression modelsAmbulatory surgery casesPONV prophylaxisPostoperative nauseaFrequent complicationPatient characteristicsSurgery patientsAnesthetic techniqueAmbulatory surgeryPatient riskRisk factorsSurgery casesAnaesthetic practiceRisk scoreAcademic centersSurgery dataPractice improvementNauseaVomiting
2010
Automating pressure ulcer risk assessment using documented patient data
Kim H, Choi J, Thompson S, Meeker L, Dykes P, Goldsmith D, Ohno-Machado L. Automating pressure ulcer risk assessment using documented patient data. International Journal Of Medical Informatics 2010, 79: 840-848. PMID: 20869303, DOI: 10.1016/j.ijmedinf.2010.08.005.Peer-Reviewed Original ResearchBuilding an ontology for pressure ulcer risk assessment to allow data sharing and comparisons across hospitals.
Kim H, Choi J, Secalag L, Dibsie L, Boxwala A, Ohno-Machado L. Building an ontology for pressure ulcer risk assessment to allow data sharing and comparisons across hospitals. AMIA Annual Symposium Proceedings 2010, 2010: 382-6. PMID: 21347005, PMCID: PMC3041354.Peer-Reviewed Original Research
2007
Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality
Matheny M, Resnic F, Arora N, Ohno-Machado L. Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality. Journal Of Biomedical Informatics 2007, 40: 688-697. PMID: 17600771, PMCID: PMC2170520, DOI: 10.1016/j.jbi.2007.05.008.Peer-Reviewed Original ResearchConceptsSupport vector machineRadial Basis Kernel Support Vector MachineKernel support vector machineCross-entropy errorSVM parameter optimizationUnseen test dataSVM kernel typesTraining dataVector machineEvolutionary algorithmGrid searchMean squared errorKernel typeMachineOptimization methodPrediction modelNumber of methodsParameter optimizationTest dataMedical applicationsOptimization parametersMortality prediction modelAlgorithmBest modelApplications
2006
PROGNOSIS IN CRITICAL CARE
Ohno-Machado L, Resnic F, Matheny M. PROGNOSIS IN CRITICAL CARE. Annual Review Of Biomedical Engineering 2006, 8: 567-599. PMID: 16834567, DOI: 10.1146/annurev.bioeng.8.061505.095842.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2005
The use of receiver operating characteristic curves in biomedical informatics
Lasko T, Bhagwat J, Zou K, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. Journal Of Biomedical Informatics 2005, 38: 404-415. PMID: 16198999, DOI: 10.1016/j.jbi.2005.02.008.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsDiscrimination and calibration of mortality risk prediction models in interventional cardiology
Matheny M, Ohno-Machado L, Resnic F. Discrimination and calibration of mortality risk prediction models in interventional cardiology. Journal Of Biomedical Informatics 2005, 38: 367-375. PMID: 16198996, DOI: 10.1016/j.jbi.2005.02.007.Peer-Reviewed Original ResearchMeSH KeywordsAngioplasty, Balloon, CoronaryCalibrationCardiologyComorbidityDecision Support Systems, ClinicalDiagnosis, Computer-AssistedDiscriminant AnalysisExpert SystemsHumansIncidenceOutcome Assessment, Health CarePostoperative ComplicationsPrognosisRetrospective StudiesRisk AssessmentRisk FactorsROC CurveSurvival AnalysisSurvival RateUnited StatesConceptsLocal risk modelAcute myocardial infarctionHosmer-Lemeshow goodnessRisk prediction modelRisk factorsCardiology-National Cardiovascular Data RegistryConsecutive percutaneous coronary interventionsMortality risk prediction modelPercutaneous coronary interventionMultivariate risk factorsCertain risk factorsROC curveAccurate risk predictionIndividual casesGood discriminationCardiogenic shockHospital mortalityCoronary interventionUnstable anginaArtery interventionPatient populationMyocardial infarctionRisk modelElective proceduresWomen's HospitalA global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method
Zou K, Resnic F, Talos I, Goldberg-Zimring D, Bhagwat J, Haker S, Kikinis R, Jolesz F, Ohno-Machado L. A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method. Journal Of Biomedical Informatics 2005, 38: 395-403. PMID: 16198998, DOI: 10.1016/j.jbi.2005.02.004.Peer-Reviewed Original ResearchAdolescentAdultAlgorithmsAngioplasty, Balloon, CoronaryBrain NeoplasmsCalibrationData Interpretation, StatisticalDecision Support Systems, ClinicalDiagnosis, Computer-AssistedDiscriminant AnalysisExpert SystemsFemaleHumansIncidenceMaleMiddle AgedOutcome Assessment, Health CarePrognosisRisk AssessmentRisk FactorsROC CurveSurvival AnalysisSurvival RateUnited States
2003
No-reflow is an independent predictor of death and myocardial infarction after percutaneous coronary intervention
Resnic F, Wainstein M, Lee M, Behrendt D, Wainstein R, Ohno-Machado L, Kirshenbaum J, Rogers C, Popma J, Piana R. No-reflow is an independent predictor of death and myocardial infarction after percutaneous coronary intervention. American Heart Journal 2003, 145: 42-46. PMID: 12514653, DOI: 10.1067/mhj.2003.36.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionPostprocedural myocardial infarctionStrong independent predictorMyocardial infarctionIndependent predictorsSodium nitroprussideInhospital outcomesCoronary interventionClinical outcomesSaphenous vein graft interventionIntracoronary vasodilator therapyVein graft interventionAdministration of verapamilAcute myocardial infarctionRate of deathInhospital mortalityVasodilator therapyCardiogenic shockBaseline demographicsGraft interventionUnstable anginaAdverse eventsConsecutive patientsIntracoronary verapamilInfarction
2001
Vascular closure devices and the risk of vascular complications after percutaneous coronary intervention in patients receiving glycoprotein IIb-IIIa inhibitors
Resnic F, Blake G, Ohno-Machado L, Selwyn A, Popma J, Rogers C. Vascular closure devices and the risk of vascular complications after percutaneous coronary intervention in patients receiving glycoprotein IIb-IIIa inhibitors. The American Journal Of Cardiology 2001, 88: 493-496. PMID: 11524056, DOI: 10.1016/s0002-9149(01)01725-8.Peer-Reviewed Original ResearchMeSH KeywordsAge DistributionAgedAnalysis of VarianceAneurysm, FalseAngioplasty, Balloon, CoronaryAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedArteriovenous FistulaChi-Square DistributionCoronary DiseaseCoronary Vessel AnomaliesCoronary VesselsEquipment SafetyFemaleHematomaHumansIncidenceMaleMiddle AgedMultivariate AnalysisMyocardial InfarctionProbabilityRetrospective StudiesRisk AssessmentRisk FactorsSex DistributionConceptsPercutaneous coronary interventionVascular closure deviceGlycoprotein IIb-IIIa antagonistsVascular complication ratesVascular complicationsClosure deviceCoronary interventionComplication rateLower vascular complication ratesOverall vascular complication rateGlycoprotein IIb-IIIa inhibitorsIIb-IIIa inhibitorsSubgroup of patientsLength of stayHospital stayConsecutive patientsLarge hematomaSurgical repairArteriovenous fistulaPatient populationUnivariate analysisRetrospective analysisComplicationsPatient comfortPatients