Pre-diagnostic trajectories of lymphocytosis predict time to treatment and death in patients with chronic lymphocytic leukemia
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- Fulltext
Final published version, 1.6 MB, PDF document
BACKGROUND: The dynamics of pre-diagnostic lymphocytosis in patients with ensuing chronic lymphocytic leukemia (CLL) need to be explored as a better understanding of disease progression may improve treatment options and even lead to disease avoidance approaches. Our aim was to investigate the development of lymphocytosis prior to diagnosis in a population-based cohort of patients with CLL and to assess the prognostic information in these pre-diagnostic measurements.
METHODS: All patients diagnosed with CLL in the Greater Copenhagen area between 2008 and 2016 were included in the study. Pre-diagnostic blood test results were obtained from the Copenhagen Primary Care Laboratory Database encompassing all blood tests requested by Copenhagen general practitioners. Using pre-diagnostic measurements, we developed a model to assess the prognosis following diagnosis. Our model accounts for known prognostic factors and corresponds to lymphocyte dynamics after diagnosis.
RESULTS: We explore trajectories of lymphocytosis, associated with known recurrent mutations. We show that the pre-diagnostic trajectories are an independent predictor of time to treatment. The implementation of pre-diagnostic lymphocytosis slope groups improved the model predictions (compared to CLL-IPI alone) for treatment throughout the period. The model can manage the heterogeneous data that are to be expected from the real-world setting and adds further prognostic information.
CONCLUSIONS: Our findings further knowledge of the development of CLL and may eventually make prophylactic measures possible.
Original language | English |
---|---|
Article number | 50 |
Journal | Communications Medicine |
Volume | 2 |
Number of pages | 8 |
ISSN | 2730-664X |
DOIs | |
Publication status | Published - 2022 |
Bibliographical note
© The Author(s) 2022.
ID: 332824154