Health Care Resource Utilization and Cost of Chronic Lymphocytic Leukemia in Finland

OBJECTIVES: Describe the health care resource utilisation (HCRU) and associated costs of chronic lymphocytic leukaemia by genetic subgrouping in Finland.

METHODS: An observational cohort study was performed using electronic health records documented during routine clinical practice at the Helsinki University Hospital (HUS). All CLL (ICD-10: C91.1; C83.0) patients starting first-line (1L) systemic treatment between 2012 and 2023 were included and grouped into non-mutually exclusive genetic subgroups (unmutated IGHV, mutated IGHV, wild-type TP53/negative del(17p), and TP53 mutated/del(17p)). Adverse events (AEs) were measured over 12 months from 1L treatment initiation or until next treatment initiation. All-cause specialised HCRU and their costs were assessed in 1L based on in- and out-patient contacts at HUS, and priced utilising Finnish healthcare unit costs. HCRU was analysed by AEs as haematological, cardiovascular, renal impairment, bleeding, infectious, and Richter’s Transformation.

RESULTS: 543 patients had received 1L systemic treatment for CLL during the study period, with a median follow-up of 39.4 months. Genetic subgroup patient numbers were: uIGHV (n=85), mIGHV (n=66), wild-type TP53/negative del(17p) (n=385), TP53mut/del(17p) (n=78). The most common AEs during 1L in the total cohort (n=543) were: neutropenia (n=253), infections (n=121), anaemia (n=105), and thrombocytopenia (n=97). Patients with uIGHV (n=85) had the highest average number of healthcare contacts per patient-year (PPY) (25.5 PPY) and of them the patients with cardiovascular, haematological, or infectious AEs had even higher resource use (28.1, 27.6 and 33.6 contacts PPY, respectively). Wild-type TP53/negative del(17p) (n=385) was the only subgroup to have above 4 events of bleeding (n=8) and Richter’s Transformation (n=8), costing an average of €18K and €10K PPY, respectively.

CONCLUSIONS: HCRU and costs vary between genetic subgroups of CLL patients likely reflecting the heterogenous risk profiles of the genetic markers. It is important to identify the patient’s genetic prognosis to prevent excessive HCRU to the greatest extent possible.