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Updated: Nov 3, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Volume-outcome relationships for kidney cancer may be driven by disparities and patient risk
Julia J Wainger1, Joseph G Cheaib1, Hiten D Patel1
1The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Purpose:
Provider and hospital factors influence healthcare quality, but data are lacking to assess their impact on renal cancer surgery. We aimed to assess factors related to surgeon and hospital volume and study their impact on 30-day outcomes after radical nephrectomy.
Materials And Methods:
Renal surgery data were abstracted from Maryland's Health Service Cost Review Commission from 2000 to 2018. Patients ≤18 years old, without a diagnosis of renal cancer, and concurrently receiving another major surgery were excluded. Volume categories were derived from the mean annual cases distribution. Multivariable logistic and linear regression models assessed the association of volume on length of stay, intensive care days, cost, 30-day mortality, readmission, and complications.
Results:
7,950 surgeries, completed by 573 surgeons at 48 hospitals, were included. Demographic, surgical, and admission characteristics differed between groups. Radical nephrectomies performed by low volume surgeons demonstrated increased post-operative complication frequency, mortality frequency, length of stay, and days spent in intensive care relative to other groups. However, after logistic regression adjusting for clinical risk and socioeconomic factors, only increased length of stay and ICU days remained associated with lower surgeon volume. Similarly, after adjusted logistic regression, hospital volume was not associated with the studied outcomes.
Conclusions:
Surgeons and hospitals differ in regards to patient demographic and clinical factors. Barriers exist regarding access to high-volume care, and thus some volume-outcome trends may be driven predominantly by disparities and case mix.
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