Abstract: PD59-07
Sources of Funding: American Cancer Society Institutional Research Grant, 81-001-26, Lobo (PI), 12/01/2015-11/30/2016, "A Model to Optimize use of Biopsies and Treatment for Small Renal Masses"

Introduction

A considerable proportion of small renal masses (SRMs) are either benign or demonstrate indolent behavior, yet guidelines stop short of recommending active surveillance (AS) over definitive treatment for any subset of patients with SRMs. Because coding does not easily capture AS, outcomes data is limited and secondary data analysis is difficult. Given these limitations, we developed a mathematical model to determine when AS can be recommended over definitive treatment.

Methods

We developed a Markov Decision Process (MDP) model to maximize life years and quality-adjusted life years (QALYs) for patients with SRMs over a ten year horizon, comparing AS, ablation, and surgical treatments. Patient demographics, pre-existing comorbidities, mass characteristics, degree of renal impairment, and treatment-associated morbidity were incorporated. A Markov model was used to simulate the size progression of SRMs. All model inputs were extrapolated from current literature.

Results

Table 1 shows results for one patient subset: 65 year old patients with no comorbidities. To maximize life years, the model recommended AS over definitive treatment for patients with SRMs up to 3 cm in diameter. Partial nephrectomy (PN) was recommended for patients with masses 3 cm and larger. Ablation was recommended as a secondary option to PN due to the assumption of a higher recurrence rate with ablation compared to PN. For patients with a central mass where nephron sparring treatment was less feasible, radical nephrectomy (RN) was only recommended for patients with 4 cm masses and larger without advanced chronic kidney disease (CKD Stage 4 and 5). To maximize QALYs, the model recommended AS for more subsets of patients, including older patients.

Conclusions

Clinicians are increasingly advising patients with SRMs to undergo active surveillance over definitive treatment. In the absence of large prospective trials, mathematical modeling can help frame the decision making process for patients and inform future guidelines on the management of patients with SRMs. Our model can give personalized recommendations for patients based on demographics, comorbidities, and mass characteristics._x000D_

Funding

American Cancer Society Institutional Research Grant, 81-001-26, Lobo (PI), 12/01/2015-11/30/2016, "A Model to Optimize use of Biopsies and Treatment for Small Renal Masses"