2023
Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US
Fritsche L, Jin W, Admon A, Mukherjee B. Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US. Journal Of Clinical Medicine 2023, 12: 1328. PMID: 36835863, PMCID: PMC9967320, DOI: 10.3390/jcm12041328.Peer-Reviewed Original ResearchPost-Acute Sequelae of SARS CoV-2 infectionElectronic health record dataPhenotype risk scoreHealth record dataCase-control study designPhenome-wide scanAcademic medical centerRisk prediction modelPost-COVID-19Risk stratification approachStudy designRecord dataRisk scoreHistory of COVID-19Medical CenterCOVID-19Increased riskPre-COVID-19Post-acute sequelaePre-COVID-19 periodRiskPost-COVID-19 periodCohortStratification approachSARS CoV-2 infectionCOVID-19 outcomes by cancer status, site, treatment, and vaccination
Salvatore M, Hu M, Beesley L, Mondul A, Pearce C, Friese C, Fritsche L, Mukherjee B. COVID-19 outcomes by cancer status, site, treatment, and vaccination. Cancer Epidemiology Biomarkers & Prevention 2023, 32: 748-759. PMID: 36626383, DOI: 10.1158/1055-9965.epi-22-0607.Peer-Reviewed Original ResearchConceptsCOVID-19 outcomesCancer statusCancer diagnosisAssociated with higher ratesElectronic health record dataHealth record dataColorectal cancerIncreased riskAcademic medical centerKidney cancerCancer-free patientsIntensive care unit admissionCancer sitesAssociated with lower ratesChemotherapy receiptHigher ratesCOVID-19 precautionsRecord dataCOVID-19Logistic regressionMedical CenterUnit admissionRetrospective cohortVaccination statusLung cancer
2022
Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
Beesley L, Mukherjee B. Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification. Statistics In Medicine 2022, 41: 5501-5516. PMID: 36131394, PMCID: PMC9826451, DOI: 10.1002/sim.9579.Peer-Reviewed Original ResearchConceptsElectronic health recordsElectronic health record data analysisElectronic health record settingsLeverages external data sourcesElectronic health record dataPopulation-based data sourcesEHR-based researchLongitudinal health informationUniversity of Michigan Health SystemHealth record dataSelection biasPopulation-based researchMichigan Health SystemMultiple sources of biasFactors related to selectionPatient-level dataHealth recordsHealth systemHealth informationPhenotype misclassificationSummary estimatesPhenotyping errorsCancer diagnosisSources of biasRecord dataEstimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan
Roberts E, Gu T, Wagner A, Mukherjee B, Fritsche L. Estimating COVID-19 Vaccination and Booster Effectiveness Using Electronic Health Records From an Academic Medical Center in Michigan. AJPM Focus 2022, 1: 100015. PMID: 36942016, PMCID: PMC9323299, DOI: 10.1016/j.focus.2022.100015.Peer-Reviewed Original ResearchIntensive care unit admissionElectronic health record dataHealth record dataElectronic health recordsMedical CenterUnit admissionAcademic medical centerOdds of vaccinationHealth recordsSevere COVID-19 outcomesAffluent areasHealthcare workersStudy designRecord dataCalendar quarterCOVID-19COVID-19 outcomesDisease overallUniversity of Michigan Medical CenterObservational studySevere COVID-19SARS-CoV-2 infectionVaccine effectivenessBooster statusOngoing surveillance
2020
Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks
Salvatore M, Beesley L, Fritsche L, Hanauer D, Shi X, Mondul A, Pearce C, Mukherjee B. Phenotype risk scores (PheRS) for pancreatic cancer using time-stamped electronic health record data: Discovery and validation in two large biobanks. Journal Of Biomedical Informatics 2020, 113: 103652. PMID: 33279681, PMCID: PMC7855433, DOI: 10.1016/j.jbi.2020.103652.Peer-Reviewed Original ResearchConceptsElectronic health recordsPolygenic risk scoresElectronic health record dataMichigan Genomics InitiativePhenotype risk scoreHigh-risk individualsPancreatic cancer diagnosisBody mass indexRisk scoreCancer diagnosisMedical phenomeUK Biobank (UKBHealth record dataSource of patient informationRisk predictionHypothesis-generating associationsDisease risk predictionHealth recordsUnadjusted associationsDrinking statusSmoking statusEpidemiological covariatesUKBPatient informationMultivariate associationsAn analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records
Beesley L, Fritsche L, Mukherjee B. An analytic framework for exploring sampling and observation process biases in genome and phenome‐wide association studies using electronic health records. Statistics In Medicine 2020, 39: 1965-1979. PMID: 32198773, DOI: 10.1002/sim.8524.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsAssociation studiesObservational health care databasesElectronic health record dataLongitudinal biorepository effortPhenome-wide association studyMichigan Genomics InitiativeHealth record dataHealth care databasesDisease-gene association studiesMichigan Health SystemCare databaseHealth systemPhenotype misclassificationStudy biasRecord dataNonprobability samplingAssociation analysisData sourcesGenome InitiativeMisclassificationAnalysis approachRecordsSensitivity analysis
2019
Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb
Fritsche L, Beesley L, VandeHaar P, Peng R, Salvatore M, Zawistowski M, Taliun S, Das S, LeFaive J, Kaleba E, Klumpner T, Moser S, Blanc V, Brummett C, Kheterpal S, Abecasis G, Gruber S, Mukherjee B. Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan genomics initiative and the UK Biobank with a visual catalog: PRSWeb. PLOS Genetics 2019, 15: e1008202. PMID: 31194742, PMCID: PMC6592565, DOI: 10.1371/journal.pgen.1008202.Peer-Reviewed Original ResearchConceptsMichigan Genomics InitiativeElectronic health recordsPolygenic risk scoresSkin cancer subtypesPheWAS resultsUK BiobankElectronic health record dataLongitudinal biorepository effortPhenome-wide association studyRisk scoreHealth record dataUK Biobank dataPrediction of disease riskPublicly-available sourcesHealth recordsGenetic architectureBiobank dataMichigan MedicineRecord dataSecondary phenotypesDisease riskVisual catalogAssociation studiesGenome InitiativePheWAS