In this light, Dr. Sarah P. Psutka utilized a contemporary multi-institutional cohort to develop treatment-specific prediction models for cancer specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). More specifically, the prediction models used competing risks regressions for CSM and OCM and logistic regressions for 90-day Clavien ≥3 complication rate, adjusting for tumor size as well as patient age, sex, body mass index, glomerular filtration rate, ECOG performance status (PS), and Charlson comorbidity index (CCI). The cohort included 4,995 patients treated with radical nephrectomy (RN, n=1270), partial nephrectomy (PN, n=2842), TA (n=479), or AS (n=404). Median follow-up was 5.1 years (IQR 2.5-8.5).
Using the prediction models, Dr. Psutka et al. developed a user-friendly prediction calculator that may be found online: https://rgulati.shinyapps.io/rcc-risk-calculator. Patient and tumor-specific parameters may be inputted into the calculator to obtain predictions for CSM, OCM, and 90-day Clavien ≥3 complications stratified by treatment modality. For example, a 70-year-old female with a 5.5 cm RCM, body mass index of 25.0 kg/m2, glomerular filtration rate of 100 mL/min, PS of 2, and CCI of 3 may expect a 5-year CSM of 4-7%, a 5-year OCM of 25-42%, and 90-day Clavien ≥3 complication rate of 2-7% across all treatment modalities. The user interface based on the example patient is shown:
Dr. Psutka concluded that selecting the appropriate treatment option for a specific patient should be personalized, and account for competing risks related to tumor and patient characteristics. However, selecting the optimal treatment option for a specific patient may be challenging. The risk calculator developed by Dr. Psutka et al. allows for quantification of competing causes of mortality and treatment-associated complications based on specific patient and tumor characteristics. Pending validation, the risk calculator may be utilized in clinical practice to facilitate shared decision-making in selecting a personalized treatment for patients with cT1c RCMs.
Using the prediction models, Dr. Psutka et al. developed a user-friendly prediction calculator that may be found online: https://rgulati.shinyapps.io/rcc-risk-calculator. Patient and tumor-specific parameters may be inputted into the calculator to obtain predictions for CSM, OCM, and 90-day Clavien ≥3 complications stratified by treatment modality. For example, a 70-year-old female with a 5.5 cm RCM, body mass index of 25.0 kg/m2, glomerular filtration rate of 100 mL/min, PS of 2, and CCI of 3 may expect a 5-year CSM of 4-7%, a 5-year OCM of 25-42%, and 90-day Clavien ≥3 complication rate of 2-7% across all treatment modalities. The user interface based on the example patient is shown:
Dr. Psutka concluded that selecting the appropriate treatment option for a specific patient should be personalized, and account for competing risks related to tumor and patient characteristics. However, selecting the optimal treatment option for a specific patient may be challenging. The risk calculator developed by Dr. Psutka et al. allows for quantification of competing causes of mortality and treatment-associated complications based on specific patient and tumor characteristics. Pending validation, the risk calculator may be utilized in clinical practice to facilitate shared decision-making in selecting a personalized treatment for patients with cT1c RCMs.
Presented by: Sarah P. Psutka, MD, MSc, Assistant Professor, Department of Urology, University of Washington Medical Center, Seattle, Washington
Written by: Ziho Lee, MD, Fellow in Advanced Robotic Oncology and Reconstruction, Temple University, Philadelphia, PA, Twitter: @ZLeeGU at the 2020 Genitourinary Cancers Symposium, ASCO GU #GU20, February 13-15, 2020, San Francisco, California