2024
A Validated Algorithm to Identify Hepatic Decompensation in the Veterans Health Administration Electronic Health Record System
Haque L, Tate J, Chew M, Caniglia E, Taddei T, Re V. A Validated Algorithm to Identify Hepatic Decompensation in the Veterans Health Administration Electronic Health Record System. Pharmacoepidemiology And Drug Safety 2024, 33: e70024. PMID: 39477692, DOI: 10.1002/pds.70024.Peer-Reviewed Original ResearchConceptsVeterans Health Administration dataHealth administrative dataAdministrative dataElectronic health record systemsHealth record systemsInternational Classification of DiseasesCoding algorithmOutpatient International Classification of DiseasesPositive predictive valueClassification of DiseasesHepatic decompensationDiagnosis codesPharmacoepidemiologic researchMedical recordsVeteransRecording systemValidation algorithmAlgorithmChronic liver diseaseDecompensationLiver diseasePredictive valueRecordsDiagnosisIdentification of hepatic steatosis among persons with and without HIV using natural language processing
Torgersen J, Skanderson M, Kidwai-Khan F, Carbonari D, Tate J, Park L, Bhattacharya D, Lim J, Taddei T, Justice A, Re V. Identification of hepatic steatosis among persons with and without HIV using natural language processing. Hepatology Communications 2024, 8: e0468. PMID: 38896066, PMCID: PMC11186806, DOI: 10.1097/hc9.0000000000000468.Peer-Reviewed Original ResearchConceptsImaging ReportingSteatotic liver diseaseHIV statusHepatic steatosisPrevalence of metabolic comorbiditiesImaging studiesVeterans Aging Cohort StudyCompare patient characteristicsPositive predictive valueAlcohol use disorderAging Cohort StudyIdentification of hepatic steatosisHIV infectionHepatitis BMetabolic comorbiditiesNatural language processing algorithmsRadiological studiesCohort studyPatient characteristicsClinical reviewLiver diseaseHIVPredictive valueUse disorderClinical image reports
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
Brief Report: Accuracy of FIB-4 for Cirrhosis in People Living With HIV and Hepatocellular Carcinoma.
Torgersen J, Kallan MJ, Carbonari DM, Park LS, Mehta RL, D'Addeo K, Tate JP, Lim JK, Goetz MB, Rodriguez-Barradas MC, Bräu N, Brown ST, Taddei TH, Justice AC, Lo Re V. Brief Report: Accuracy of FIB-4 for Cirrhosis in People Living With HIV and Hepatocellular Carcinoma. JAIDS Journal Of Acquired Immune Deficiency Syndromes 2020, 85: 530-534. PMID: 33185999, PMCID: PMC8353543, DOI: 10.1097/qai.0000000000002510.Peer-Reviewed Original ResearchConceptsSetting of HCCFIB-4Receiver-operating characteristic curveHepatocellular carcinomaHCC diagnosisPredictive valueVeterans Aging Cohort StudyEvidence of cirrhosisSetting of HIVAbsence of cirrhosisFIB-4 scoreFibrosis-4 indexIncident hepatocellular carcinomaMedical record reviewPrevalence of cirrhosisAging Cohort StudyCross-sectional studyCharacteristic curveNegative predictive valuePositive predictive valueCirrhosis statusCohort studyMechanisms of carcinogenesisRecord reviewPlatelet count
2018
Hepatocellular Carcinoma Outcome is Predicted by Expression of Neuronal Calcium Sensor 1
Schuette D, Moore LM, Robert ME, Taddei TH, Ehrlich BE. Hepatocellular Carcinoma Outcome is Predicted by Expression of Neuronal Calcium Sensor 1. Cancer Epidemiology Biomarkers & Prevention 2018, 27: cebp.0167.2018. PMID: 29789326, PMCID: PMC8465775, DOI: 10.1158/1055-9965.epi-18-0167.Peer-Reviewed Original ResearchConceptsNeuronal calcium sensor-1Hepatocellular carcinomaDisease outcomePrognostic biomarkerIncidence of HCCWorse disease outcomesCancer-related deathLiver cancer cohortExpression levelsFurther functional assessmentEarly tumor detectionProspective cohortAsian patientsPatient survivalVariety of Ca2Tumor microarrayHCC patientsMetastatic cancerBreast cancerCancer cohortAggressive phenotypeNovel biomarkersFunctional assessmentPredictive valueTumor progression