2024
Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome
Hicks E, Niarchou M, Goleva S, Kabir D, Johnson J, Johnston K, Ciarcia J, Pathak G, Smoller J, Davis L, Nievergelt C, Koenen K, Huckins L, Choi K, Group P. Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome. Biological Psychiatry Global Open Science 2024, 4: 100337. PMID: 39050781, PMCID: PMC11268109, DOI: 10.1016/j.bpsgos.2024.100337.Peer-Reviewed Original ResearchElectronic health recordsClinical knowledge gapsPhysical health problemsPhenome-wide associationPheWAS analysisBiobank participantsHealth recordsEpidemiological researchHealthcare systemTest associationsMedical phenomeAssociated with osteoporosisPulmonary heart diseasePhecodesHealth problemsLogistic regressionPosttraumatic stress disorderSex differencesPheWASComorbidity profilesHeart diseaseStress disorderInteraction termsAssociationPTSD
2022
Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study
Xu J, Johnson JS, Signer R, Consortium E, Birgegård A, Jordan J, Kennedy MA, Landén M, Maguire SL, Martin NG, Mortensen PB, Petersen LV, Thornton LM, Bulik CM, Huckins LM. Exploring the clinical and genetic associations of adult weight trajectories using electronic health records in a racially diverse biobank: a phenome-wide and polygenic risk study. The Lancet Digital Health 2022, 4: e604-e614. PMID: 35780037, PMCID: PMC9612590, DOI: 10.1016/s2589-7500(22)00099-1.Peer-Reviewed Original ResearchConceptsElectronic health recordsPolygenic risk scoresWeight trajectoriesDepression polygenic risk scoresObesity polygenic risk scoresHealth recordsWeight changeUK BiobankIndividual health statusLower disease riskGenetic associationPatient populationUS National InstitutesWeight managementStable weightRisk scoreHealthy populationHealth statusAnorexia nervosaBioMe BiobankDisease riskDisorder diagnosisMental healthWeight lossPhenome-wide association studySex-Stratified Relationships Between PTSD Phenotypes and the Medical Phenome Using Electronic Health Records
Choi K, Goleva S, Huckins L, Kozak E, Smoller J, Nievergelt C, Koenen K, Davis L. Sex-Stratified Relationships Between PTSD Phenotypes and the Medical Phenome Using Electronic Health Records. Biological Psychiatry 2022, 91: s3. DOI: 10.1016/j.biopsych.2022.02.026.Peer-Reviewed Original Research
2020
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City
Paranjpe I, Russak A, De Freitas J, Lala A, Miotto R, Vaid A, Johnson K, Danieletto M, Golden E, Meyer D, Singh M, Somani S, Kapoor A, O'Hagan R, Manna S, Nangia U, Jaladanki S, O’Reilly P, Huckins L, Glowe P, Kia A, Timsina P, Freeman R, Levin M, Jhang J, Firpo A, Kovatch P, Finkelstein J, Aberg J, Bagiella E, Horowitz C, Murphy B, Fayad Z, Narula J, Nestler E, Fuster V, Cordon-Cardo C, Charney D, Reich D, Just A, Bottinger E, Charney A, Glicksberg B, Nadkarni G, Center M. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City. BMJ Open 2020, 10: e040736. PMID: 33247020, PMCID: PMC7702220, DOI: 10.1136/bmjopen-2020-040736.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overC-Reactive ProteinComorbidityCOVID-19Critical CareFemaleFibrin Fibrinogen Degradation ProductsHospital MortalityHospitalizationHospitalsHumansLymphocytesMaleMiddle AgedNew York CityPandemicsProcalcitoninRetrospective StudiesRisk FactorsSARS-CoV-2Young AdultConceptsIn-hospital mortalityHospitalised patientsPre-existing conditionsInstitutional electronic health recordsElectronic health recordsHealth System hospitalsMount Sinai Health SystemUrban hospital systemMount Sinai Health System hospitalsSinai Health SystemStudy periodIntensive careHealth recordsInvestigate in-hospital mortalityCohort of hospitalised patientsPublic health crisisHealth systemRetrospective cohort studyHospital systemSystem hospitalsGlobal public health crisisClinical characteristicsCohort studyCOVID-19New York CityMachine 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 pandemicImplicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data
Dueñas H, Seah C, Johnson J, Huckins L. Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data. Human Molecular Genetics 2020, 29: r33-r41. PMID: 32879975, PMCID: PMC7530523, DOI: 10.1093/hmg/ddaa192.Peer-Reviewed Original ResearchConceptsElectronic health recordsUse of electronic health recordsElectronic health record dataElectronic health record analysisGenome-wide association studiesHealth recordsPhenotype definitionAssociation studiesMedical recordsClinical decisionsPhenotypic characterizationPhenotypic analysisClinically useful insightsPotential biasPresentation of diseaseDegree of biasHomogeneous cohortClinicPhenotypeScalable mannerRecordsCohortBias
2019
Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems
Zheutlin A, Dennis J, Karlsson Linnér R, Moscati A, Restrepo N, Straub P, Ruderfer D, Castro V, Chen C, Ge T, Huckins L, Charney A, Kirchner H, Stahl E, Chabris C, Davis L, Smoller J. Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. American Journal Of Psychiatry 2019, 176: 846-855. PMID: 31416338, PMCID: PMC6961974, DOI: 10.1176/appi.ajp.2019.18091085.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresHealth care systemCare systemGenetic riskAssociated with schizophreniaPolygenic risk score distributionPhenome-wide association studyMeasures of genetic riskRisk scoreHighest risk decileHealth care settingsElectronic health recordsOdds of schizophreniaAssociated with other phenotypesCare settingsRisk decileHealth recordsHigher oddsHealth consequencesResearch cohortAssociation studiesHealthEarly interventionMeta-analysisPersonality disorder