Clinical characteristics associated with treatment type for localized renal tumors: Implications for practice pattern assessment

摘要

Objective: To determine the associations between the pretreatment characteristics and treatment selection in patients presenting with clinical stage I renal masses. Materials and Methods: Using institutional data, patients presenting with clinical stage I (??7 cm) renal tumors that were managed with active surveillance (AS), tumor ablation (ABL), partial nephrectomy (PN), or radical nephrectomy (RN) from 2005 to 2011 were identified. The associations between the pretreatment characteristics and the selected treatment strategy were assessed using multinomial regression models, with RN as the reference group. Results: A total of 969 patients (mean age 61.9 ?? 12.8 years) with 1034 clinical stage I lesions (mean tumor size 3.3 ?? 1.5 cm) met the inclusion criteria. The patients were initially treated with RN (29.4%), PN (38.8%), ABL (6.1%), and AS (25.7%). Traditionally captured covariates, including older age (PN, odds ratio [OR] 0.96, 95% confidence interval [CI] 0.94-0.99]) and decreasing tumor size (PN, OR 0.2, 95% CI 0.1-0.4; ABL, OR 0.01, 95% CI 0.0-0.1; AS, OR 0.2, 95% CI 0.1-0.3) were associated with alternative treatment types compared with RN. However, the characteristics associated with treatment type that are not included in traditional registry or administrative data included the presence of a solitary kidney (PN, OR 11.9, 95% CI 2.9-48.9; ABL, OR 15.5, 95% CI 2.5-98.1; AS, OR 7.1, 95% CI 1.3-39.3) and high complexity nephrectomy score (PN, OR 0.1, 95% CI 0.1-0.3; ABL, OR 0.1, 95% CI 0.0-0.6; AS, OR 0.1, 95% CI 0.03-0.3). Conclusion: Pretreatment characteristics associated with treatment type in our series, including the presence of a solitary kidney and anatomic complexity, are poorly captured using administrative and registry data. Observational studies investigating the variations in practice patterns for stage I renal masses require improved integration of clinical and tumor characteristics to reduce selection biases. ? 2013 Elsevier Inc.