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Establishing a prediction model for recurrence of condyloma acuminatum

We collected the clinical data of 156 patients diagnosed with condyloma acuminatum (CA), including age, gender, marriage, education level, stay up late, smoking, drinking, number of sexual partners, HPV infection status of sexual partners, genitourinary and anal diseases, condom use, other diseases...

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Autores principales: Zhan, Mengyun, Tong, Zhenzhen, Chen, Song, Miao, Yu, Yang, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494849/
https://www.ncbi.nlm.nih.gov/pubmed/36138469
http://dx.doi.org/10.1186/s40001-022-00816-7
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author Zhan, Mengyun
Tong, Zhenzhen
Chen, Song
Miao, Yu
Yang, Yun
author_facet Zhan, Mengyun
Tong, Zhenzhen
Chen, Song
Miao, Yu
Yang, Yun
author_sort Zhan, Mengyun
collection PubMed
description We collected the clinical data of 156 patients diagnosed with condyloma acuminatum (CA), including age, gender, marriage, education level, stay up late, smoking, drinking, number of sexual partners, HPV infection status of sexual partners, genitourinary and anal diseases, condom use, other diseases of HPV infection, location and number of warts, HPV typing, etc. Analyze the risk factors affecting the recurrence of CA, explore the influencing factors and independent influencing factors of CA recurrence, establish the prediction model of CA recurrence, and evaluate its prediction value. Univariate analysis showed that stay up late, HPV infection status of sexual partners, urogenital diseases, condom use, other diseases of HPV infection and the number of CA were the influencing factors of CA recurrence. Multivariate analysis showed that condom use (OR = 0.166), HPV infection status of sexual partners (OR = 4.848), number of warts (OR = 1.212) and urogenital diseases (OR = 3.179) were independent factors affecting the recurrence of CA (P < 0.05). Therefore, the prediction model of CA recurrence can be established, and the area under the curve AUC of the prediction model was calculated to be 0.867 (95% CI 0.812–0.923). The model established in this study has certain prediction value for the recurrence of CA and can be used to preliminarily predict the recurrence of CA.
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spelling pubmed-94948492022-09-23 Establishing a prediction model for recurrence of condyloma acuminatum Zhan, Mengyun Tong, Zhenzhen Chen, Song Miao, Yu Yang, Yun Eur J Med Res Research We collected the clinical data of 156 patients diagnosed with condyloma acuminatum (CA), including age, gender, marriage, education level, stay up late, smoking, drinking, number of sexual partners, HPV infection status of sexual partners, genitourinary and anal diseases, condom use, other diseases of HPV infection, location and number of warts, HPV typing, etc. Analyze the risk factors affecting the recurrence of CA, explore the influencing factors and independent influencing factors of CA recurrence, establish the prediction model of CA recurrence, and evaluate its prediction value. Univariate analysis showed that stay up late, HPV infection status of sexual partners, urogenital diseases, condom use, other diseases of HPV infection and the number of CA were the influencing factors of CA recurrence. Multivariate analysis showed that condom use (OR = 0.166), HPV infection status of sexual partners (OR = 4.848), number of warts (OR = 1.212) and urogenital diseases (OR = 3.179) were independent factors affecting the recurrence of CA (P < 0.05). Therefore, the prediction model of CA recurrence can be established, and the area under the curve AUC of the prediction model was calculated to be 0.867 (95% CI 0.812–0.923). The model established in this study has certain prediction value for the recurrence of CA and can be used to preliminarily predict the recurrence of CA. BioMed Central 2022-09-22 /pmc/articles/PMC9494849/ /pubmed/36138469 http://dx.doi.org/10.1186/s40001-022-00816-7 Text en © The Author(s) 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhan, Mengyun
Tong, Zhenzhen
Chen, Song
Miao, Yu
Yang, Yun
Establishing a prediction model for recurrence of condyloma acuminatum
title Establishing a prediction model for recurrence of condyloma acuminatum
title_full Establishing a prediction model for recurrence of condyloma acuminatum
title_fullStr Establishing a prediction model for recurrence of condyloma acuminatum
title_full_unstemmed Establishing a prediction model for recurrence of condyloma acuminatum
title_short Establishing a prediction model for recurrence of condyloma acuminatum
title_sort establishing a prediction model for recurrence of condyloma acuminatum
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494849/
https://www.ncbi.nlm.nih.gov/pubmed/36138469
http://dx.doi.org/10.1186/s40001-022-00816-7
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