Hospital length of stay following radical cystectomy for muscle-invasive bladder cancer: Development and validation of a population-based prediction model.
Aged
Aged, 80 and over
Carcinoma, Transitional Cell
/ surgery
Cystectomy
/ adverse effects
Female
Humans
Length of Stay
/ economics
Male
Models, Statistical
Neoadjuvant Therapy
/ statistics & numerical data
Quality Improvement
Quality Indicators, Health Care
/ economics
Retrospective Studies
Risk Assessment
/ methods
Risk Factors
SEER Program
/ statistics & numerical data
United States
Urinary Bladder
/ surgery
Urinary Bladder Neoplasms
/ therapy
Bladder cancer
Hospital stay
Model
Prediction
Radical cystectomy
SEER
Journal
Urologic oncology
ISSN: 1873-2496
Titre abrégé: Urol Oncol
Pays: United States
ID NLM: 9805460
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
19
09
2018
revised:
15
10
2018
accepted:
23
10
2018
pubmed:
18
11
2018
medline:
24
7
2020
entrez:
18
11
2018
Statut:
ppublish
Résumé
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. 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. 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 (R 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.
Identifiants
pubmed: 30446462
pii: S1078-1439(18)30431-9
doi: 10.1016/j.urolonc.2018.10.024
pmc: PMC7682754
mid: NIHMS1644203
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
837-843Subventions
Organisme : NCATS NIH HHS
ID : TL1 TR001440
Pays : United States
Informations de copyright
Copyright © 2018 Elsevier Inc. All rights reserved.
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