2025
The PRO‐AGE Tool and Its Association With Post Discharge Outcomes in Older Adults Admitted From the Emergency Department
Cohen I, Curiati P, Morinaga C, Han L, Gandhi T, Araujo K, Avelino‐Silva T, Bianco L, Brandt C, Capelli S, Carpenter C, Cruz D, Dresden S, Fishman I, Gipson K, Gray E, Hastings S, Hung W, Kang R, Lockhart M, Meeker D, Ohuabunwa U, Ottilie‐Kovelman S, Platts‐Mills T, Sandoval J, Sifnugel N, Taylor Z, Tomasino D, Vaughan C, Aliberti M, Hwang U. The PRO‐AGE Tool and Its Association With Post Discharge Outcomes in Older Adults Admitted From the Emergency Department. Journal Of The American Geriatrics Society 2025, 73: 1419-1428. PMID: 39843218, PMCID: PMC12101947, DOI: 10.1111/jgs.19374.Peer-Reviewed Original ResearchActivities of daily livingEmergency departmentFunctional declineActivities of daily living disabilityInstrumental activities of daily livingRisk scorePro-agingPatients admitted to hospitalPost-discharge outcomesUnited StatesInstrumental ADLRisk of deathProportional hazards modelDaily livingAssociated with mortalityCharlson comorbidity scoreOlder adultsDischarge outcomesDecline outcomesDiverse populationsMultiple hospitalsComorbidity scoreHazards modelOlder patientsMulticenter observational study
2023
A Multicenter Observational Study Comparing Virtual with In-Person Morning Reports during the COVID-19 Pandemic
Bradley J, Redinger J, Tuck M, Sweigart J, Smeraglio A, Mitchell C, Laudate J, Kwan B, Jagannath A, Heppe D, Guidry M, Ehlers E, Cyr J, Cornia P, Chun J, Caputo L, Arundel C, Albert T, Gunderson C. A Multicenter Observational Study Comparing Virtual with In-Person Morning Reports during the COVID-19 Pandemic. Southern Medical Journal 2023, 116: 745-749. PMID: 37657781, DOI: 10.14423/smj.0000000000001597.Peer-Reviewed Original ResearchConceptsProspective observational studyCoronavirus disease 2019 (COVID-19) pandemicMorning reportDisease 2019 pandemicCOVID-19 pandemicInternal medicine residency programsMedicine residency programsSingle hospitalObservational studyCase-based presentationsPatient diagnosisMultiple hospitalsMarked increaseNumber of slidesCOVID-19HospitalMorning report formatReport formatMore participantsResidency programsReportPandemic restrictionsPerson reportsAttendingsProgram directors
2020
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Vaid A, Somani S, Russak A, De Freitas J, Chaudhry F, Paranjpe I, Johnson K, Lee S, Miotto R, Richter F, Zhao S, Beckmann N, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly P, Huckins L, Kovatch P, Finkelstein J, Freeman R, Argulian E, Kasarskis A, Percha B, Aberg J, Bagiella E, Horowitz C, Murphy B, Nestler E, Schadt E, Cho J, Cordon-Cardo C, Fuster V, Charney D, Reich D, Bottinger E, Levin M, Narula J, Fayad Z, Just A, Charney A, Nadkarni G, Glicksberg B. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal Of Medical Internet Research 2020, 22: e24018. PMID: 33027032, PMCID: PMC7652593, DOI: 10.2196/24018.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAdolescentAdultAgedAged, 80 and overBetacoronavirusCohort StudiesCoronavirus InfectionsCOVID-19Electronic Health RecordsFemaleHospital MortalityHospitalizationHospitalsHumansMachine LearningMaleMiddle AgedNew York CityPandemicsPneumonia, ViralPrognosisRisk AssessmentROC CurveSARS-CoV-2Young AdultConceptsElectronic health recordsNew York CityYork CityMount Sinai Health SystemSinai Health SystemMortality predictionAdmitted to hospitalAt-risk patientsHealth recordsHealth systemEHR dataIn-Hospital MortalityEarly identification of high-risk patientsCOVID-19Identification of high-risk patientsMultiple hospitalsStudy populationPatient characteristicsSingle hospitalHospitalArea under the receiver operating characteristic curveEarly identificationPredicting MortalityCohort of patientsCOVID-19 pandemic
2011
Boosting enrolment in clinical trials: validation of a regional network model
Kernan W, Viscoli C, Brass L, Amatangelo M, Birch A, Clark W, Conwit R, Furie K, Gorman M, Pesapane B, Kleindorfer D, Lovejoy A, Osborne J, Silliman S, Zweifler R, Horwitz R. Boosting enrolment in clinical trials: validation of a regional network model. Clinical Trials 2011, 8: 645-653. PMID: 21824978, PMCID: PMC3852692, DOI: 10.1177/1740774511414925.Peer-Reviewed Original ResearchConceptsClinical trialsTransient ischemic attackNames of patientsEligible patientsIschemic attackStroke preventionDrug adherenceStroke therapyActive surveillanceAverage monthly rateHome visitsPatientsHospitalTrial researchParticipant costsOutreach NetworkMultiple hospitalsTrialsStudy intervalNational InstituteEnrollmentMonthly rateStrokeParticipantsReproducible method
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