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The overall objective is to develop a clinical data registry that can be used to facilitate research with the ultimate goal of reducing the morbidity and/or mortality and improving the quality of life of patients diagnosed or living with hairy cell leukemia. With approximately 600 new cases of this rare disease identified in the US each year, HCL represents 2% of all cases of leukemia in adults. Considering the rarity of this chronic leukemia, the investigators have organized Hairy Cell Leukemia Foundation (HCLF) consisting of medical centers across the globe with recognized expertise in the management of these patients.
This clinical registry is being established to collect de-identified information on this rare disease. The registry created by assimilation of de- identified coded patient data will centralize information that can be used to improve the management of the many complications of this disease and its treatment. This study is focused on collection of clinically and biologically meaningful endpoints across multiple institutions; as such, it is not focused on a specific set of hypotheses but will collect data that will facilitate such analyses. The investigators will collect information related to the symptoms and the clinical course of the disease, to the complications from the disease and its treatment, presence of minimal residual disease, frequency of relapse and subsequent management, data on novel molecular markers associated with the prognosis.
In conjunction with Dr. Philip Payne, Chairman of the Department of Bioinformatics at The Ohio State University, the investigators have created a system for safeguarding the confidentiality and the identity of all patients who agree to participate in this research registry. Each participating institution will be responsible for de-identification of the data, using software developed by The Ohio State University, Department of Biomedical Informatics, before it is used in the registry. Each institution will confidentially maintain a code for linking this information to an individual patient.
Each participating institution will have direct control over the data contained in the registry that is associated with their respective patient population, which will be housed locally (at the participating institution) and not replicated in a central location. As a result of these measures, should a patient wish to withdraw from the registry, the responsible institution will be able to immediately remove all records related to that patient from the registry
Observational Model: Cohort, Time Perspective: Prospective
Ohio State University Comprehensive Cancer Center
Ohio State University Comprehensive Cancer Center
Published on BioPortfolio: 2015-09-29T07:58:46-0400
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