12 research outputs found
Increase in circulating CD4CD25Foxp3 T cells in patients with Philadelphia-negative chronic myeloproliferative neoplasms during treatment with IFN-α
Gene expression profiling with principal component analysis depicts the biological continuum from essential thrombocythemia over polycythemia vera to myelofibrosis
Molecular profiling of peripheral blood cells from patients with polycythemia vera and related neoplasms:Identification of deregulated genes of significance for inflammation and immune surveillance
Eosinophilia in routine blood samples as a biomarker for solid tumor development:A study based on The Copenhagen Primary Care Differential Count (CopDiff) Database
The Copenhagen primary care differential count (CopDiff) database
BACKGROUND: The differential blood cell count provides valuable information about a person’s state of health. Together with a variety of biochemical variables, these analyses describe important physiological and pathophysiological relations. There is a need for research databases to explore associations between these parameters, concurrent comorbidities, and future disease outcomes. METHODS AND RESULTS: The Copenhagen General Practitioners’ Laboratory is the only laboratory serving general practitioners in the Copenhagen area, covering approximately 1.2 million inhabitants. The Copenhagen General Practitioners’ Laboratory has registered all analytical results since July 1, 2000. The Copenhagen Primary Care Differential Count database contains all differential blood cell count results (n=1,308,022) from July 1, 2000 to January 25, 2010 requested by general practitioners, along with results from analysis of various other blood components. This data set is merged with detailed data at a person level from The Danish Cancer Registry, The Danish National Patient Register, The Danish Civil Registration System, and The Danish Register of Causes of Death. CONCLUSION: This paper reviews methodological issues behind the construction of the Copenhagen Primary Care Differential Count database as well as the distribution of characteristics of the population it covers and the variables that are recorded. Finally, it gives examples of its use as an inspiration to peers for collaboration