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Risk factors for wound dehiscence following radical cystectomy: a prediction model
OBJECTIVES: Radical cystectomy (RC) is a complex urologic procedure performed for the treatment of bladder cancer and causes significant morbidity. Wound dehiscence (WD) is a major complication associated with RC and is associated with multiple risk factors. The objectives of this study are to ident...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842309/ https://www.ncbi.nlm.nih.gov/pubmed/35173813 http://dx.doi.org/10.1177/17562872211060570 |
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author | Nasrallah, Ali A. Mansour, Mazen Abou Heidar, Nassib F. Ayoub, Christian Najdi, Jad A. Tamim, Hani El Hajj, Albert |
author_facet | Nasrallah, Ali A. Mansour, Mazen Abou Heidar, Nassib F. Ayoub, Christian Najdi, Jad A. Tamim, Hani El Hajj, Albert |
author_sort | Nasrallah, Ali A. |
collection | PubMed |
description | OBJECTIVES: Radical cystectomy (RC) is a complex urologic procedure performed for the treatment of bladder cancer and causes significant morbidity. Wound dehiscence (WD) is a major complication associated with RC and is associated with multiple risk factors. The objectives of this study are to identify clinical risk factors for incidence of WD and develop a risk-prediction model to aid in patient risk-stratification and improvement of perioperative care. MATERIALS AND METHODS: The American College of Surgeons – National Surgical Quality Improvement Program (ACS-NSQIP) database was used to derive the study cohort. A univariate analysis provided nine variables eligible for multivariate model entry. A stepwise logistic regression analysis was conducted and refined considering clinical relevance of the variables, and then bootstrapped with 1000 samples, resulting in a five-factor model. Model performance and calibration were assessed by a receiver operated curve (ROC) analysis and the Hosmer–Lemeshow test for goodness of fit, respectively. RESULTS: A cohort of 11,703 patients was identified from years 2005 to 2017, with 342 (2.8%) incidences of WD within 30 days of operation. The final five-factor model included male gender [odds ratio (OR) = 2.5, p < 0.001], surgical site infection (OR = 6.3, p < 0.001), smoking (OR = 1.8, p < 0.001), chronic obstructive pulmonary disease (COPD) (OR = 1.9, p < 0.001), and weight class; morbidly obese patients had triple the odds of WD (OR = 2.9, p < 0.001). The ROC analysis provided a C-statistic of 0.76 and calibration R(2) was 0.99. CONCLUSION: The study yields a statistically robust and clinically beneficial five-factor model for estimation of WD incidence risk following RC, with good performance and excellent calibration. These factors may assist in identifying high-risk patients, providing preoperative counseling and thus leading to improvement in perioperative care. |
format | Online Article Text |
id | pubmed-8842309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-88423092022-02-15 Risk factors for wound dehiscence following radical cystectomy: a prediction model Nasrallah, Ali A. Mansour, Mazen Abou Heidar, Nassib F. Ayoub, Christian Najdi, Jad A. Tamim, Hani El Hajj, Albert Ther Adv Urol Original Research OBJECTIVES: Radical cystectomy (RC) is a complex urologic procedure performed for the treatment of bladder cancer and causes significant morbidity. Wound dehiscence (WD) is a major complication associated with RC and is associated with multiple risk factors. The objectives of this study are to identify clinical risk factors for incidence of WD and develop a risk-prediction model to aid in patient risk-stratification and improvement of perioperative care. MATERIALS AND METHODS: The American College of Surgeons – National Surgical Quality Improvement Program (ACS-NSQIP) database was used to derive the study cohort. A univariate analysis provided nine variables eligible for multivariate model entry. A stepwise logistic regression analysis was conducted and refined considering clinical relevance of the variables, and then bootstrapped with 1000 samples, resulting in a five-factor model. Model performance and calibration were assessed by a receiver operated curve (ROC) analysis and the Hosmer–Lemeshow test for goodness of fit, respectively. RESULTS: A cohort of 11,703 patients was identified from years 2005 to 2017, with 342 (2.8%) incidences of WD within 30 days of operation. The final five-factor model included male gender [odds ratio (OR) = 2.5, p < 0.001], surgical site infection (OR = 6.3, p < 0.001), smoking (OR = 1.8, p < 0.001), chronic obstructive pulmonary disease (COPD) (OR = 1.9, p < 0.001), and weight class; morbidly obese patients had triple the odds of WD (OR = 2.9, p < 0.001). The ROC analysis provided a C-statistic of 0.76 and calibration R(2) was 0.99. CONCLUSION: The study yields a statistically robust and clinically beneficial five-factor model for estimation of WD incidence risk following RC, with good performance and excellent calibration. These factors may assist in identifying high-risk patients, providing preoperative counseling and thus leading to improvement in perioperative care. SAGE Publications 2021-12-05 /pmc/articles/PMC8842309/ /pubmed/35173813 http://dx.doi.org/10.1177/17562872211060570 Text en © The Author(s), 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Nasrallah, Ali A. Mansour, Mazen Abou Heidar, Nassib F. Ayoub, Christian Najdi, Jad A. Tamim, Hani El Hajj, Albert Risk factors for wound dehiscence following radical cystectomy: a prediction model |
title | Risk factors for wound dehiscence following radical cystectomy: a
prediction model |
title_full | Risk factors for wound dehiscence following radical cystectomy: a
prediction model |
title_fullStr | Risk factors for wound dehiscence following radical cystectomy: a
prediction model |
title_full_unstemmed | Risk factors for wound dehiscence following radical cystectomy: a
prediction model |
title_short | Risk factors for wound dehiscence following radical cystectomy: a
prediction model |
title_sort | risk factors for wound dehiscence following radical cystectomy: a
prediction model |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842309/ https://www.ncbi.nlm.nih.gov/pubmed/35173813 http://dx.doi.org/10.1177/17562872211060570 |
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