• Jeffrey A Jones, Muzaffar H Qazilbash, Ya-Chen T Shih, Scott B Cantor, Catherine D Cooksley, and EltingLinda SLS.
• Health Services Research Section, Department of Biostatistics, The University of Texas M D Anderson Cancer Center, Houston, TX 77030, USA. jeffrey.jones@osumc.edu
• Cancer. 2008 Mar 1; 112 (5): 1096-105.

BackgroundAutologous hematopoietic stem cell transplantation (auto HSCT) is standard of care therapy for multiple myeloma and Hodgkin and non-Hodgkin lymphomas in front-line and salvage settings, respectively. Complications remain common, but population-based estimates of their frequency and relative contribution to cost are not available.MethodsA retrospective cohort comprised of 8891 patients with multiple myeloma and lymphoma admitted to US hospitals for auto HSCT over a 2-year period (2000-2001) was extracted from the Nationwide Inpatient Sample (NIS). Patient characteristics, vital status, and total hospital charges were obtained directly from the NIS. Transplant characteristics and outcomes were identified by ICD-9-CM codes. Mean hospital charges were examined by outcome and transformed into cost by using Medicare cost-to-charge ratios. Factors associated with hospital cost, length of stay, and in-hospital mortality were explored by using multivariate regression.ResultsThe mean hospital cost for auto HSCT during this period was $51,312. Significant complications were documented for >50% of admissions. Infectious complications (~60%) and stomatitis (~40%) were the most frequent, and both were associated with increased hospital costs (range, $15,000 to $50,000). In-hospital mortality was rare (<5%) but was associated with markedly increased cost when it occurred. Pretransplant conditioning with total body irradiation was strongly associated with infectious complications, higher cost, and death.ConclusionsAdverse events are both common and costly after auto HSCT. Strategies to minimize complications could significantly reduce not only morbidity and mortality but also the cost of the procedure. Administrative data can be profitably exploited to investigate outcomes in this population.

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