Healthcare Costs of Treating Privately Insured Patients with Acute Myeloid Leukemia in the United States from 2004 to 2014: A Generalized Additive Modeling Approach

Objectives: The primary objective of this study was to predict healthcare cost trajectories for patients with newly diagnosed acute myeloid leukemia (AML) receiving allogeneic hematopoietic cell transplantation (alloHCT), as a function of days since chemotherapy initiation, days relative to alloHCT, and days before death or last date of insurance eligibility (LDE). An exploratory objective examined patients with AML receiving chemotherapy only. Methods: We used Optum’s de-identified Clinformatics^® Data Mart Database to construct cumulative cost trajectories from chemotherapy initiation to death or LDE (through 31 December 2014) for US patients aged 20–74 years diagnosed between 1 March 2004 and 31 December 2013 (n = 187 alloHCT; n = 253 chemotherapy only). We used generalized additive modeling (GAM) to predict expected trajectories and bootstrapped confidence intervals (CIs) at user-specified intervals conditional on dates of alloHCT and death or LDE relative to chemotherapy initiation. Results: Expected costs (in 2017 values) for a hypothetical patient receiving alloHCT 60 days after chemotherapy initiation and followed for 5 years were $US572,000 (95% CI 517,000–633,000); $US119,000 (95% CI 51,000–192,000); $US102,000 (95% CI 0–285,000); $US79,000 (95% CI 0–233,000), for years 1–4, respectively, and either $US494,000 (95% CI 212,000–799,000) or $US108,000 (95% CI 0–230,000) in year 5, whether the patient died or was lost to follow-up on day 1825, respectively. Conclusions: Rates of cost accrual varied over time since chemotherapy initiation, with accelerations around the time of alloHCT and death. GAM is a potentially useful approach for imputing longitudinal costs relative to treatment initiation and one or more intercurrent, clinical, or terminal events in randomized controlled trials or registries with unrecorded costs or for dynamic decision–analytic models.