2021
A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data
Hauser RG, Esserman D, Beste LA, Ong SY, Colomb DG, Bhargava A, Wadia R, Rose MG. A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data. American Journal Of Clinical Pathology 2021, 156: 1142-1148. PMID: 34184028, DOI: 10.1093/ajcp/aqab086.Peer-Reviewed Original ResearchConceptsBlood cell countChronic myelogenous leukemiaCell countMyelogenous leukemiaRetrospective electronic health record dataDiagnosis of CMLLarge integrated health care systemDifferential blood cell countsIntegrated health care systemUsual medical careTime of diagnosisElectronic health record dataClonal stem cell disorderHealth record dataStem cell disordersHealth care systemDisease courseDiagnostic workupAdult leukemiaCell disordersMedical careDiagnostic testingDiagnostic testsBlood cellsCare systemRisk of Disseminated Gonococcal Infections With Terminal Complement Blockade
Graciaa SH, Graciaa DS, Yildirim I, Chonat S. Risk of Disseminated Gonococcal Infections With Terminal Complement Blockade. Journal Of Pediatric Hematology/Oncology 2021, 44: e493-e495. PMID: 33560079, PMCID: PMC8556643, DOI: 10.1097/mph.0000000000002075.Peer-Reviewed Original ResearchConceptsTerminal complement blockadeComplement blockadeGonococcal infectionClonal hematopoietic stem cell disordersDisseminated gonococcal infectionHematopoietic stem cell disordersQuality of lifeStem cell disordersParoxysmal nocturnal hemoglobinuriaComplement protein C5Complement-mediated hemolysisProphylactic antibioticsIntravascular hemolysisAppropriate immunizationsAzithromycin resistanceCell disordersNocturnal hemoglobinuriaNeisseria gonorrhoeaeProtein C5Infection riskPatientsMonoclonal antibodiesNeisseria meningitidisBlockadeInfection
2020
Emerging treatment options for patients with high-risk myelodysplastic syndrome
Bewersdorf JP, Carraway H, Prebet T. Emerging treatment options for patients with high-risk myelodysplastic syndrome. Therapeutic Advances In Hematology 2020, 11: 2040620720955006. PMID: 33240476, PMCID: PMC7675905, DOI: 10.1177/2040620720955006.Peer-Reviewed Original ResearchAcute myeloid leukemiaMyelodysplastic syndromeHigh-risk myelodysplastic syndromeClonal hematopoietic stem cell disordersDriver mutationsCombinations of HMAsImmune checkpoint inhibitorsMinority of patientsModest survival benefitPeripheral blood cytopeniasTargetable driver mutationsHematopoietic stem cell disordersStem cell disordersDysplastic cell morphologyUnited States FoodAgent azacitidineCheckpoint inhibitorsIntensive chemotherapyOral agentsBlood cytopeniasSurvival benefitMDS patientsCombination therapyMDS treatmentTreatment options
2018
Immunosuppressive therapy in myelodysplastic syndromes: a borrowed therapy in search of the right place
Shallis RM, Chokr N, Stahl M, Pine AB, Zeidan AM. Immunosuppressive therapy in myelodysplastic syndromes: a borrowed therapy in search of the right place. Expert Review Of Hematology 2018, 11: 715-726. PMID: 30024293, DOI: 10.1080/17474086.2018.1503049.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsImmunosuppressive therapyMyelodysplastic syndromeImmune pathwaysManagement of MDSTreatment of MDSClonal hematopoietic stem cell disordersMeaningful clinical activityAdaptive immune pathwaysHematopoietic stem cell disordersStem cell disordersImmune dysregulationDisease coursePeripheral cytopeniasClinical benefitImmune activationTherapeutic optionsAplastic anemiaClinical activityHematologic diseasesCell disordersClinical experienceContinued clarificationLeukemic progressionTherapyPatients
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply