2023
Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV
Torgersen J, Akers S, Huo Y, Terry J, Carr J, Ruutiainen A, Skanderson M, Levin W, Lim J, Taddei T, So‐Armah K, Bhattacharya D, Rentsch C, Shen L, Carr R, Shinohara R, McClain M, Freiberg M, Justice A, Re V. Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV. Pharmacoepidemiology And Drug Safety 2023, 32: 1121-1130. PMID: 37276449, PMCID: PMC10527049, DOI: 10.1002/pds.5648.Peer-Reviewed Original ResearchConceptsSevere hepatic steatosisHepatic steatosisHIV statusLiver attenuationHounsfield unitsPredictive valueRadiologist assessmentUS Veterans Health AdministrationNoncontrast abdominal CTVeterans Health AdministrationCross-sectional studySample of patientsNegative predictive valueReal-world studyPositive predictive valueAbdominal CTLiver fatTomography scanSteatosisCT imagesHealth AdministrationPharmacoepidemiologic studiesRadiologist reviewHIVPercent agreement
2020
COVID-19 in Great Britain: epidemiological and clinical characteristics of the first few hundred (FF100) cases: a descriptive case series and case control analysis
Boddington N, Charlett A, Elgohari S, Walker J, McDonald H, Byers C, Coughlan L, Vilaplana T, Whillock R, Sinnathamby M, Panagiotopoulos N, Letley L, MacDonald P, Vivancos R, Edeghere O, Shingleton J, Bennett E, Grint D, Strongman H, Mansfield K, Rentsch C, Minassian C, Douglas I, Mathur R, Peppa M, Cottrell S, McMenamin J, Zambon M, Ramsay M, Dabrera G, Saliba V, Bernal J. COVID-19 in Great Britain: epidemiological and clinical characteristics of the first few hundred (FF100) cases: a descriptive case series and case control analysis. Bulletin Of The World Health Organization 2020, 99: 178-189. PMID: 33716340, PMCID: PMC7941108, DOI: 10.2471/blt.20.265603.Peer-Reviewed Original ResearchConceptsProportion of COVID-19 casesPredictive value of symptomsTraining public health professionalsHealth-care seeking behaviorOccurrence of feverCase-control analysisCourse of diseaseSpecificity of symptomsShortness of breathPublic health professionalsProportion of childrenClinical presentationClinical characteristicsExposure to infectionRespiratory infectionsCases of coronavirus disease 2019Household contactsHealth professionalsBurden of COVID-19Predictive valueCase definitionCoronavirus disease 2019COVID-19 casesControl groupFever
2018
Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection
Rentsch CT, Cartwright EJ, Gandhi NR, Brown ST, Rodriguez-Barradas MC, Goetz MB, Marconi VC, Gibert CL, Re VL, Fiellin DA, Justice AC, Tate JP. Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection. Annals Of Epidemiology 2018, 28: 808-811. PMID: 30195616, PMCID: PMC6318448, DOI: 10.1016/j.annepidem.2018.08.007.Peer-Reviewed Original ResearchConceptsHepatitis C virus infectionCorporate Data WarehouseChronic HCV infectionC virus infectionPositive predictive valuePredictive valueHCV infectionHCV treatmentVirus infectionVeterans Health Administration Corporate Data WarehouseChronic hepatitis C virus (HCV) infectionStudy periodModern treatment eraRetrospective cohort studyElectronic health record dataPharmacy fill recordsHealth record dataNegative predictive valueElectronic health recordsAntiviral regimenHCV therapyTreatment eraChart reviewCohort studyAntiviral treatment