Urologic Oncology: Seminars and Original Investigations
Seminars ArticleHospital length of stay following radical cystectomy for muscle-invasive bladder cancer: Development and validation of a population-based prediction model
Introduction
Nearly 81,190 bladder cancer cases and 17,240 associated deaths are estimated in the United States in 2018 [1]. Radical cystectomy with extended pelvic lymphadenectomy is recommended for patients with muscle-invasive bladder cancer [2]. Despite these longstanding guidelines, radical cystectomy is markedly underused as historically only 21% of patients with muscle-invasive disease are offered this potentially curative surgery [3]. Given concerns regarding the non-negligible morbidity and mortality associated with this complex surgery, along with patients more often being elderly with increased comorbidities, identifying determinants to improve outcomes are needed. One of the potential surrogates for improved quality of care defined by the Center for Medicare and Medicaid Services (CMS) includes length of stay. Prolonged hospital stays were historically associated with increased adverse events, subsequent utilization of healthcare resources, as well as associated cost of care, particularly following major cancer surgeries [4]. While some factors, such as patient age and racial background, were observed as independent predictors of prolonged hospital stays, there is no comprehensive model to accurately predict length of stay for patients following radical cystectomy [5], [6]. The purpose of the present study was to develop and validate a predictive model which may identify at-risk patients to develop targeted interventions to reduce length of stay.
Section snippets
2.1. Data source
We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data from the National Cancer Institute [7]. SEER database contains information on patient demographics, cancer characteristics (stage, grade, histology), and patient follow-up information. The Medicare database contains information on inpatient and outpatient claims. The study was deemed exempt by the Institutional Review Board at The University of Texas Medical Branch at Galveston.
2.2. Ascertainment of study cohort
Using SEER-Medicare, we included
Results
Patient demographics in the discovery and validation cohorts are summarized in Table 1. Overall, 1,224 (50.0%) and 1,224 (50.0%) of the 2,448 patients who underwent radical cystectomy for muscle-invasive bladder cancer were included in the discovery and validation cohorts, respectively. Patients were similar across all demographic and clinical predictors except for census region. Patients in the validation cohort were more often from the West (41.8% vs. 40.6%) and South (25.5% vs. 23.9%) than
Discussion
We developed and validated a predictive model for length of hospital stay following radical cystectomy. The model performed well as shown by the robust internal and external validation enhancing generalizability and applicability of the model. With the need to improve quality and outcomes of care at decreased costs, models such as the one developed and validated in the present may identify potential at-risk patients and measures to decrease length of stay following surgery.
Our study has several
Conclusions
In this large population-based study, we developed and validated a model to predict length of hospital stay following radical cystectomy. Patients who are unmarried, increased comorbidities, advanced clinical stage, received neoadjuvant chemotherapy, and underwent radical cystectomy at lower-volume hospitals in the West census region of the United States were at greater risk for prolonged hospital stay following surgery. Refinement of the model with inclusion of additional confounders may
Conflicts of interest
None.
Acknowledgments
This study was conducted with the support of a Department of Defense Peer Reviewed Cancer Research Program (PRCRP), Career Development Award (W81XWH1710576), and the Herzog Foundation (SBW). This study was conducted with the support of the Institute for Translational Sciences at the University of Texas Medical Branch, supported in part by a Clinical and Translational Science Award (UL1 cTR001439 and 1TL1TR00144003) from the National Center for Advancing Translational Sciences (MDR). These
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This is part of the Seminar series guest edited by Dr. Schmitz-Drager under the supervision of Dr. Droller.