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Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women
Trichomonas vaginitis (TV) is the most common non-viral sexually transmitted infection (STI) worldwide. The high prevalence of TV combined with mild or asymptomatic early symptoms leads to clinical vulnerability from delayed diagnosis. Latent infection can increase the incidence of pelvic infections...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691695/ https://www.ncbi.nlm.nih.gov/pubmed/36424393 http://dx.doi.org/10.1038/s41598-022-24396-y |
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author | Li, Qi Li, Yaqin Bai, Ying Zhang, Honglei Zhao, Weihong |
author_facet | Li, Qi Li, Yaqin Bai, Ying Zhang, Honglei Zhao, Weihong |
author_sort | Li, Qi |
collection | PubMed |
description | Trichomonas vaginitis (TV) is the most common non-viral sexually transmitted infection (STI) worldwide. The high prevalence of TV combined with mild or asymptomatic early symptoms leads to clinical vulnerability from delayed diagnosis. Latent infection can increase the incidence of pelvic infections, infertility, and adverse pregnancy complications. Data from 898 women who underwent vaginal flora testing from June 2014 to December 2014 were used to create a nomogram to assess the risk of TV in women in order to guide TV prevention and clinical intervention. The prediction model was evaluated in terms of identification, calibration, and clinical utility using the C-index, calibration plots, decision curve analysis, and internal validation. Predictors in the TV nomogram included age, occupation, yearly income, tea drinking, bathing frequency, menopause, spontaneous abortion, use of contraceptives, history of gynecological surgery, and HPV infection. The C-index of the TV risk prediction model was 0.732 (95% confidence interval: 0.695–0.768). It showed good discriminatory and predictive power. Decision curve analysis indicated that the nomogram had a good net benefit when the threshold probability of TV in women was 2–80%. The established TV prediction model easily, accurately, and quickly predicts the risk of TV onset. |
format | Online Article Text |
id | pubmed-9691695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96916952022-11-26 Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women Li, Qi Li, Yaqin Bai, Ying Zhang, Honglei Zhao, Weihong Sci Rep Article Trichomonas vaginitis (TV) is the most common non-viral sexually transmitted infection (STI) worldwide. The high prevalence of TV combined with mild or asymptomatic early symptoms leads to clinical vulnerability from delayed diagnosis. Latent infection can increase the incidence of pelvic infections, infertility, and adverse pregnancy complications. Data from 898 women who underwent vaginal flora testing from June 2014 to December 2014 were used to create a nomogram to assess the risk of TV in women in order to guide TV prevention and clinical intervention. The prediction model was evaluated in terms of identification, calibration, and clinical utility using the C-index, calibration plots, decision curve analysis, and internal validation. Predictors in the TV nomogram included age, occupation, yearly income, tea drinking, bathing frequency, menopause, spontaneous abortion, use of contraceptives, history of gynecological surgery, and HPV infection. The C-index of the TV risk prediction model was 0.732 (95% confidence interval: 0.695–0.768). It showed good discriminatory and predictive power. Decision curve analysis indicated that the nomogram had a good net benefit when the threshold probability of TV in women was 2–80%. The established TV prediction model easily, accurately, and quickly predicts the risk of TV onset. Nature Publishing Group UK 2022-11-23 /pmc/articles/PMC9691695/ /pubmed/36424393 http://dx.doi.org/10.1038/s41598-022-24396-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Article Li, Qi Li, Yaqin Bai, Ying Zhang, Honglei Zhao, Weihong Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title | Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title_full | Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title_fullStr | Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title_full_unstemmed | Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title_short | Development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
title_sort | development and validation of a predictive model for the risk of developing trichomonas vaginitis in women |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691695/ https://www.ncbi.nlm.nih.gov/pubmed/36424393 http://dx.doi.org/10.1038/s41598-022-24396-y |
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