2024
SOFA score performs worse than age for predicting mortality in patients with COVID-19
Sherak R, Sajjadi H, Khimani N, Tolchin B, Jubanyik K, Taylor R, Schulz W, Mortazavi B, Haimovich A. SOFA score performs worse than age for predicting mortality in patients with COVID-19. PLOS ONE 2024, 19: e0301013. PMID: 38758942, PMCID: PMC11101117, DOI: 10.1371/journal.pone.0301013.Peer-Reviewed Original ResearchConceptsCrisis standards of careIn-hospital mortalityIntensive care unitAcademic health systemSequential Organ Failure Assessment scoreCohort of intensive care unitSequential Organ Failure AssessmentStandard of careLogistic regression modelsMortality predictionPredicting in-hospital mortalityHealth systemUnivariate logistic regression modelCrisis standardsDisease morbidityCOVID-19Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients
Haimovich A, Burke R, Nathanson L, Rubins D, Taylor R, Kross E, Ouchi K, Shapiro N, Schonberg M. Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients. JAMA Network Open 2024, 7: e2414213. PMID: 38819823, PMCID: PMC11143461, DOI: 10.1001/jamanetworkopen.2024.14213.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overBostonEmergency Service, HospitalFemaleGeriatric AssessmentHumansMaleMortalityPrognosisTerminal CareConceptsElectronic health recordsEmergency departmentObserved mortality rateED encountersEnd-of-Life Screening ToolOlder adultsEnd-of-life preferencesMortality riskIllness criteriaLife-limiting illnessOptimal screening criteriaDays of ED arrivalEHR-based algorithmTertiary care EDLow risk of mortalityHigher mortality riskMortality rateRisk of mortalityHealth recordsReceiver operating characteristic curveIllness diagnosisMain OutcomesED arrivalSerious illnessDemographic subgroupsThe Scope of Multimorbidity in Family Medicine: Identifying Age Patterns Across the Lifespan
Chartash D, Gilson A, Taylor R, Hart L. The Scope of Multimorbidity in Family Medicine: Identifying Age Patterns Across the Lifespan. The Journal Of The American Board Of Family Medicine 2024, 37: 251-260. PMID: 38740476, DOI: 10.3122/jabfm.2023.230221r1.Peer-Reviewed Original ResearchConceptsRates of multimorbidityICD-10 diagnostic codesFamily medicine clinicPresence of multimorbidityHealth care systemCardiometabolic disordersMedical historyStudy periodMultimorbidity rateMultimorbidity indexGroup of diagnosesPatient transitionsFamily medicineGeriatric careRetrospective cohort studyCare systemMental healthMultimorbidityMedicine clinicDiagnostic codesPractical resourcesAlcohol use disorderCohort studyAged 0Age groups
2016
Impact of point-of-care ultrasonography on ED time to disposition for patients with nontraumatic shock
Hall MK, Taylor RA, Luty S, Allen IE, Moore CL. Impact of point-of-care ultrasonography on ED time to disposition for patients with nontraumatic shock. The American Journal Of Emergency Medicine 2016, 34: 1022-1030. PMID: 26988105, DOI: 10.1016/j.ajem.2016.02.059.Peer-Reviewed Original ResearchConceptsPOC ultrasonographyEmergency departmentNontraumatic shockCare ultrasonographyPropensity scorePropensity score matchElectronic health recordsHospital mortalityShock patientsPrompt diagnosisED arrivalED patientsED physiciansPoint of careRetrospective studyUnique patientsImpact of pointMean reductionPropensity score modelPatientsUltrasonographyED timeDiagnostic ultrasonographyCovariates of timeEvidence of reductionPrediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER, Fleischman W, Hall MK. Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach. Academic Emergency Medicine 2016, 23: 269-278. PMID: 26679719, PMCID: PMC5884101, DOI: 10.1111/acem.12876.Peer-Reviewed Original ResearchConceptsMachine learning approachesElectronic health recordsLearning approachPredictive analyticsMachine learning techniquesRandom forest modelClinical decision support systemBig Data DrivenDecision support systemForest modelLearning techniquesUse casesData-DrivenFacilitate automationTraditional analytic techniquesAnalyticsSupport systemSimple heuristicsNew analyticsHealth recordsSmall setTree modelQuestion of generalizabilityPrediction modelDecision rules
2013
Point-of-Care Focused Cardiac Ultrasound for Prediction of Pulmonary Embolism Adverse Outcomes
Taylor RA, Davis J, Liu R, Gupta V, Dziura J, Moore CL. Point-of-Care Focused Cardiac Ultrasound for Prediction of Pulmonary Embolism Adverse Outcomes. Journal Of Emergency Medicine 2013, 45: 392-399. PMID: 23827166, DOI: 10.1016/j.jemermed.2013.04.014.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overEchocardiographyEmergency Service, HospitalFemaleHemorrhageHospital MortalityHumansMaleMiddle AgedPoint-of-Care SystemsPredictive Value of TestsPrognosisPulmonary EmbolismRecurrenceRespiratory InsufficiencyRetrospective StudiesRisk FactorsShockVenous ThromboembolismVentricular Dysfunction, RightConceptsRight ventricular strainHospital adverse outcomesRetrospective chart reviewPulmonary embolismAdverse outcomesEmergency departmentChart reviewCardiac ultrasoundHighest positive likelihood ratioRecurrent venous thromboembolismLow negative likelihood ratioSignificant predictorsEmergency care practitionersFocused cardiac ultrasoundFOCUS examinationPositive likelihood ratioNegative likelihood ratioMajor bleedingLikelihood ratioRespiratory failureVenous thromboembolismTransthoracic echocardiographyHospital admissionIndependent predictorsVentricular strain