A Clinical Decision Aid to Support Personalized Treatment Selection for Patients with Clinical T1 Renal Masses: Results from a Multi-institutional Competing-risks Analysis.

Abstract

BACKGROUND:

Personalized treatment for clinical T1 renal cortical masses (RCMs) should take into account competing risks related to tumor and patient characteristics.

OBJECTIVE:

To develop treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-d Clavien grade ≥3 complications across radical nephrectomy (RN), partial nephrectomy (PN), thermal ablation (TA), and active surveillance (AS).

DESIGN, SETTING, AND PARTICIPANTS:

Pretreatment clinical and radiological features were collected for consecutive adult patients treated with initial RN, PN, TA, or AS for RCMs at four high-volume referral centers (2000-2019).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:

Prediction models used competing-risks regression for CSM and OCM and logistic regression for 90-d Clavien grade ≥3 complications. Performance was assessed using bootstrap validation.

RESULTS AND LIMITATIONS:

The cohort comprised 5300 patients treated with RN (n = 1277), PN (n = 2967), TA (n = 476), or AS (n = 580). Over median follow-up of 5.2 yr (interquartile range 2.5-8.7), there were 117 CSM, 607 OCM, and 198 complication events. The C index for the predictive models was 0.80 for CSM, 0.77 for OCM, and 0.64 for complications. Predictions from the fitted models are provided in an online calculator (https://small-renal-mass-risk-calculator.fredhutch.org). To illustrate, a hypothetical 74-yr-old male with a 4.5-cm RCM, body mass index of 32 kg/m2, estimated glomerular filtration rate of 50 ml/min, Eastern Cooperative Oncology Group performance status of 3, and Charlson comorbidity index of 3 has predicted 5-yr CSM of 2.9-5.6% across treatments, but 5-yr OCM of 29% and risk of 90-d Clavien grade 3-5 complications of 1.9% for RN, 5.8% for PN, and 3.6% for TA. Limitations include selection bias, heterogeneity in practice across treatment sites and the study time period, and lack of control for surgeon/hospital volume.

CONCLUSIONS:

We present a risk calculator incorporating pretreatment features to estimate treatment-specific competing risks of mortality and complications for use during shared decision-making and personalized treatment selection for RCMs.

PATIENT SUMMARY:

We present a risk calculator that generates personalized estimates of the risks of death from cancer or other causes and of complications for surgical, ablation, and surveillance treatment options for patients with stage 1 kidney tumors.

• Authors: Sarah P Psutka, Roman Gulati, Michael A S Jewett, Kamel Fadaak, Antonio Finelli, Laura Legere, Todd M Morgan, Phillip M Pierorazio, Mohamad E Allaf, Jeph Herrin, Christine M Lohse, R Houston Thompson, Stephen A Boorjian, Thomas D Atwell, Grant D Schmit, Brian A Costello, Nilay D Shah, Bradley C Leibovich
• Journal: European urology (Eur Urol) Vol. 81 Issue 6 Pg. 576-585 (06 2022) ISSN: 1873-7560 [Electronic] Switzerland
• PMID: 34862099 (Publication Type: Journal Article, Multicenter Study, Research Support, N.I.H., Extramural)
• Copyright: Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Topics

• Adult
• Carcinoma, Renal Cell (surgery)
• Decision Support Techniques
• Humans
• Kidney Neoplasms (surgery)
• Male
• Nephrectomy (methods)
• Precision Medicine
• Retrospective Studies
• Treatment Outcome