TY - JOUR
T1 - Hospital length of stay following radical cystectomy for muscle-invasive bladder cancer
T2 - Development and validation of a population-based prediction model
AU - Ray-Zack, Mohamed D.
AU - Shan, Yong
AU - Mehta, Hemalkumar B.
AU - Yu, Xiaoying
AU - Kamat, Ashish M.
AU - Williams, Stephen B.
N1 - Funding Information:
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 funding sources were used for the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Dr. Stephen B. Williams had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This study used the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare linked databases. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the Applied Research Program, NCI; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the SEER program tumor registries in the creation of the SEER database.
Funding Information:
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 funding sources were used for the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Dr. Stephen B. Williams had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This study used the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare linked databases. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the efforts of the Applied Research Program, NCI; the Office of Research, Development and Information, CMS; Information Management Services (IMS), Inc.; and the SEER program tumor registries in the creation of the SEER database.
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/11
Y1 - 2019/11
N2 - Objective: Length of hospital stay for patients following radical cystectomy is an important determinant for improved quality of care. We sought to develop and validate a predictive model for length of hospital stay following radical cystectomy. Methods: Patients aged 66 to 90 years diagnosed with clinical stage T2-4a muscle-invasive bladder cancer who underwent radical cystectomy were included from January 1, 2002 through December 31, 2011 using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Linear regression analyses were used to develop and validate a predictive model for length of hospital stay. Results: A total of 2,448 patients met inclusion criteria. After random assignment, 1,224 patients were included in the discovery cohort and 1,224 patients included in the validation cohort. The cohorts were well balanced with no significant difference in any of the preoperative variables. A best model was developed using marital status, Surveillance, Epidemiology, and End Results (SEER) region, clinical stage, Charlson comorbidity index, logarithm of hospital cystectomy volume, and use of neoadjuvant chemotherapy in a backward selection to predict the length of stay. There was robust internal validation (sum square error (SSE): 258.1 vs. predicted sum of squares (PRESS): 264.0 at SLS = 0.10), consistent with the external validation (average square error (ASE): discovery (0.248) vs. validation (0.258)) cohort. The strength of the model in predicting length of stay for the entire cohort was (R2 = 0.048). Conclusion: In this large population-based study, we developed and validated a model to predict length of hospital stay following radical cystectomy. Identification of at-risk patients for prolonged hospital stay may aid in targeted interventions to reduce length of stay, improve quality of care, and decrease healthcare costs.
AB - Objective: Length of hospital stay for patients following radical cystectomy is an important determinant for improved quality of care. We sought to develop and validate a predictive model for length of hospital stay following radical cystectomy. Methods: Patients aged 66 to 90 years diagnosed with clinical stage T2-4a muscle-invasive bladder cancer who underwent radical cystectomy were included from January 1, 2002 through December 31, 2011 using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data. Linear regression analyses were used to develop and validate a predictive model for length of hospital stay. Results: A total of 2,448 patients met inclusion criteria. After random assignment, 1,224 patients were included in the discovery cohort and 1,224 patients included in the validation cohort. The cohorts were well balanced with no significant difference in any of the preoperative variables. A best model was developed using marital status, Surveillance, Epidemiology, and End Results (SEER) region, clinical stage, Charlson comorbidity index, logarithm of hospital cystectomy volume, and use of neoadjuvant chemotherapy in a backward selection to predict the length of stay. There was robust internal validation (sum square error (SSE): 258.1 vs. predicted sum of squares (PRESS): 264.0 at SLS = 0.10), consistent with the external validation (average square error (ASE): discovery (0.248) vs. validation (0.258)) cohort. The strength of the model in predicting length of stay for the entire cohort was (R2 = 0.048). Conclusion: In this large population-based study, we developed and validated a model to predict length of hospital stay following radical cystectomy. Identification of at-risk patients for prolonged hospital stay may aid in targeted interventions to reduce length of stay, improve quality of care, and decrease healthcare costs.
KW - Bladder cancer
KW - Hospital stay
KW - Model
KW - Prediction
KW - Radical cystectomy
KW - SEER
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U2 - 10.1016/j.urolonc.2018.10.024
DO - 10.1016/j.urolonc.2018.10.024
M3 - Review article
C2 - 30446462
AN - SCOPUS:85056330686
VL - 37
SP - 837
EP - 843
JO - Urologic Oncology
JF - Urologic Oncology
SN - 1078-1439
IS - 11
ER -