Pd59-07 Using Mathematical Modeling to Define the Role of Active Surveillance in the Management of Patients With Clinical T1 Renal Masses
Year: 2017
Type: article
Abstract: You have accessJournal of UrologyKidney Cancer: Localized: Active Surveillance1 Apr 2017PD59-07 USING MATHEMATICAL MODELING TO DEFINE THE ROLE OF ACTIVE SURVEILLANCE IN THE MANAGEMENT OF PATIENTS WITH CLINICAL T1 RENAL MASSES Devang Sharma, Jennifer Lobo, Noah Schenkman, and Tracey Krupski Devang SharmaDevang Sharma More articles by this author , Jennifer LoboJennifer Lobo More articles by this author , Noah SchenkmanNoah Schenkman More articles by this author , and Tracey KrupskiTracey Krupski More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2635AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES 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. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1135 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Devang Sharma More articles by this author Jennifer Lobo More articles by this author Noah Schenkman More articles by this author Tracey Krupski More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Source: The Journal of Urology
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2604508562