title: Automated screening for myelodysplastic syndromes through analysis of complete blood count and cell population data parameters.
abstract: The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.
authors.affiliation: Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Philipp W
authors.initials: PW
authors.lastname: Raess
authors.forename: Gert-Jan M
authors.initials: GJ
authors.lastname: van de Geijn
authors.forename: Tjin L
authors.initials: TL
authors.lastname: Njo
authors.forename: Boudewijn
authors.initials: B
authors.lastname: Klop
authors.forename: Dmitry
authors.initials: D
authors.lastname: Sukhachev
authors.forename: Gerald
authors.initials: G
authors.lastname: Wertheim
authors.forename: Tom
authors.initials: T
authors.lastname: McAleer
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Master
authors.forename: Adam
authors.initials: A
authors.lastname: Bagg
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001772:Blood Cell Count / Q000295:instrumentation / Q000706:statistics & numerical data; D001792:Blood Platelets / Q000648:ultrastructure; D001853:Bone Marrow / Q000473:pathology; D001856:Bone Marrow Examination; D048429:Cell Size; D015331:Cohort Studies; D003663:Decision Trees; D017097:Electric Impedance; D004909:Erythrocyte Indices; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D009190:Myelodysplastic Syndromes / Q000097:blood* / Q000175:diagnosis / Q000473:pathology; D010045:Outpatients; D012372:ROC Curve; D011897:Random Allocation; D012189:Retrospective Studies