Pre-diagnostic trajectories of lymphocytosis predict time to treatment and death in patients with chronic lymphocytic leukemia

Abstract

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.

The Copenhagen Primary Care Laboratory (CopLab) Database

“… 19 We have evaluated the prevalence and clinical significance of neutropenia discovered in routine complete blood cell counts – alone or in conjunction with other cytopenias 23 , 31 and have shown that anaemia is present years before diagnosis of myelodysplastic syndromes among primary care patients. 24 Lately, our data have been used to show that abnormal eosinophil count in chronic lymphatic leukaemia (CLL) diagnosis is associated with shorter treatment-free survival 32 and that machine learning has the potential to recognize recently diagnosed CLL patients who are highly susceptible to infections. 33 For solid tumours, we have shown that the presence of cancer heightened the risk of developing new-onset type 2 diabetes, which, in turn, was linked to elevated overall mortality rates.…”

Agents contributing to secondary immunodeficiency development in patients with multiple myeloma, chronic lymphocytic leukemia and non-Hodgkin lymphoma: A systematic literature review

“…However, different institutions may use slightly different reference ranges to determine grading. Lymphocytopenia might not be an ideal marker of a dysfunctional immune system in patients with CLL due to lymphocytosis; however, it could be useful in patients with NHL and MM to define the risk of infections (44)(45)(46).…”

Blood sampling patterns in primary care change several years before a cancer diagnosis

“…Patients with slowly progressing malignancies may debut with insidious symptoms and seek medical attention with increasing frequency, which indeed has been demonstrated through different markers of primary care activity in the last 1-2 years prior to a cancer diagnosis [1,2]. However, within hematological malignancies in particular, early signs of slowly progressing or pre-malignant conditions may in fact be detectable several years before the malignant disease is diagnosed [3][4][5]. Identifying early signs of malignancy or pre-malignancy is important in order to increase chances of successful treatment and lower morbidity and mortality [6][7][8].…”