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Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer
PURPOSE: The risk of treatment-related toxicity is important for patients with localised prostate cancer to consider when deciding between treatment options. We developed a model to predict hospitalisation for radiation-induced genitourinary toxicity based on patient characteristics. METHODS: The pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712379/ https://www.ncbi.nlm.nih.gov/pubmed/36357601 http://dx.doi.org/10.1007/s00345-022-04212-y |
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author | David, Rowan Hiwase, Mrunal Kahokehr, Arman A. Lee, Jason Watson, David I. Leung, John O‘Callaghan, Michael E. |
author_facet | David, Rowan Hiwase, Mrunal Kahokehr, Arman A. Lee, Jason Watson, David I. Leung, John O‘Callaghan, Michael E. |
author_sort | David, Rowan |
collection | PubMed |
description | PURPOSE: The risk of treatment-related toxicity is important for patients with localised prostate cancer to consider when deciding between treatment options. We developed a model to predict hospitalisation for radiation-induced genitourinary toxicity based on patient characteristics. METHODS: The prospective South Australian Prostate Cancer Clinical Outcomes registry was used to identify men with localised prostate cancer who underwent curative intent external beam radiotherapy (EBRT) between 1998 and 2019. Multivariable Cox proportional regression was performed. Model discrimination, calibration, internal validation and utility were assessed using C-statistics and area under ROC, calibration plots, bootstrapping, and decision curve analysis, respectively. RESULTS: There were 3,243 patients treated with EBRT included, of which 644 (20%) patients had a treated-related admission. In multivariable analysis, diabetes (HR 1.35, 95% CI 1.13–1.60, p < 0.001), smoking (HR 1.78, 95% CI 1.40–2.12, p < 0.001), and bladder outlet obstruction (BOO) without transurethral resection of prostate (TURP) (HR 7.49, 95% CI 6.18–9.08 p < 0.001) followed by BOO with TURP (HR 4.96, 95% CI 4.10–5.99 p < 0.001) were strong independent predictors of hospitalisation (censor-adjusted c-statistic = 0.80). The model was well-calibrated (AUC = 0.76). The global proportional hazards were met. In internal validation through bootstrapping, the model was reasonably discriminate at five (AUC 0.75) years after radiotherapy. CONCLUSIONS: This is the first study to develop a predictive model for genitourinary toxicity requiring hospitalisation amongst men with prostate cancer treated with EBRT. Patients with localised prostate cancer and concurrent BOO may benefit from TURP before EBRT. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00345-022-04212-y. |
format | Online Article Text |
id | pubmed-9712379 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-97123792022-12-02 Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer David, Rowan Hiwase, Mrunal Kahokehr, Arman A. Lee, Jason Watson, David I. Leung, John O‘Callaghan, Michael E. World J Urol Original Article PURPOSE: The risk of treatment-related toxicity is important for patients with localised prostate cancer to consider when deciding between treatment options. We developed a model to predict hospitalisation for radiation-induced genitourinary toxicity based on patient characteristics. METHODS: The prospective South Australian Prostate Cancer Clinical Outcomes registry was used to identify men with localised prostate cancer who underwent curative intent external beam radiotherapy (EBRT) between 1998 and 2019. Multivariable Cox proportional regression was performed. Model discrimination, calibration, internal validation and utility were assessed using C-statistics and area under ROC, calibration plots, bootstrapping, and decision curve analysis, respectively. RESULTS: There were 3,243 patients treated with EBRT included, of which 644 (20%) patients had a treated-related admission. In multivariable analysis, diabetes (HR 1.35, 95% CI 1.13–1.60, p < 0.001), smoking (HR 1.78, 95% CI 1.40–2.12, p < 0.001), and bladder outlet obstruction (BOO) without transurethral resection of prostate (TURP) (HR 7.49, 95% CI 6.18–9.08 p < 0.001) followed by BOO with TURP (HR 4.96, 95% CI 4.10–5.99 p < 0.001) were strong independent predictors of hospitalisation (censor-adjusted c-statistic = 0.80). The model was well-calibrated (AUC = 0.76). The global proportional hazards were met. In internal validation through bootstrapping, the model was reasonably discriminate at five (AUC 0.75) years after radiotherapy. CONCLUSIONS: This is the first study to develop a predictive model for genitourinary toxicity requiring hospitalisation amongst men with prostate cancer treated with EBRT. Patients with localised prostate cancer and concurrent BOO may benefit from TURP before EBRT. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00345-022-04212-y. Springer Berlin Heidelberg 2022-11-10 2022 /pmc/articles/PMC9712379/ /pubmed/36357601 http://dx.doi.org/10.1007/s00345-022-04212-y Text en © Crown 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article David, Rowan Hiwase, Mrunal Kahokehr, Arman A. Lee, Jason Watson, David I. Leung, John O‘Callaghan, Michael E. Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title | Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title_full | Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title_fullStr | Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title_full_unstemmed | Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title_short | Predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
title_sort | predicting post-radiation genitourinary hospital admissions in patients with localised prostate cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712379/ https://www.ncbi.nlm.nih.gov/pubmed/36357601 http://dx.doi.org/10.1007/s00345-022-04212-y |
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