Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound

We want to describe a model that allows the use of transperineal ultrasound to define the probability of experiencing uterine prolapse (UP). This was a prospective observational study involving 107 patients with UP or cervical elongation (CE) without UP. The ultrasound study was performed using tran...

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Autores: García Mejido, José Antonio, Ramos Vega, Zenaida, Fernández Palacín, Ana, Borrero González, Carlota, Valdivia, Maribel, Pelayo Delgado, Irene, Sáinz Bueno, José Antonio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/145720
Acceso en línea:https://hdl.handle.net/11441/145720
https://doi.org/10.3390/tomography8040144
Access Level:acceso abierto
Palabra clave:3D Transperineal ultrasound
Pelvic organ prolapse
Uterine prolapse (UP)
Cervical elongation
Pelvic floor
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spelling Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal UltrasoundGarcía Mejido, José AntonioRamos Vega, ZenaidaFernández Palacín, AnaBorrero González, CarlotaValdivia, MaribelPelayo Delgado, IreneSáinz Bueno, José Antonio3D Transperineal ultrasoundPelvic organ prolapseUterine prolapse (UP)Cervical elongationPelvic floorWe want to describe a model that allows the use of transperineal ultrasound to define the probability of experiencing uterine prolapse (UP). This was a prospective observational study involving 107 patients with UP or cervical elongation (CE) without UP. The ultrasound study was performed using transperineal ultrasound and evaluated the differences in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. We generated different multivariate binary logistic regression models using nonautomated methods to predict UP, including the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. The parameters were added progressively according to their simplicity of use and their predictive capacity for identifying UP. We used two binary logistic regression models to predict UP. Model 1 was based on the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient [AUC: 0.967 (95% CI, 0.939–0.995; p < 0.0005)]. Model 2 used the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver, age, avulsion and ballooning [AUC: 0.971 (95% CI, 0.945–0.997; p < 0.0005)]. In conclusion, the model based on the difference in the pubis– uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient could predict 96.7% of patients with UP.MDPICirugíaMedicina Preventiva y Salud Pública2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/145720https://doi.org/10.3390/tomography8040144reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésTomography, 8 (4), 1716-1725.https://www.mdpi.com/2379-139X/8/4/144info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1457202026-06-17T12:51:07Z
dc.title.none.fl_str_mv Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
title Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
spellingShingle Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
García Mejido, José Antonio
3D Transperineal ultrasound
Pelvic organ prolapse
Uterine prolapse (UP)
Cervical elongation
Pelvic floor
title_short Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
title_full Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
title_fullStr Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
title_full_unstemmed Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
title_sort Predictive Model for the Diagnosis of Uterine Prolapse Based on Transperineal Ultrasound
dc.creator.none.fl_str_mv García Mejido, José Antonio
Ramos Vega, Zenaida
Fernández Palacín, Ana
Borrero González, Carlota
Valdivia, Maribel
Pelayo Delgado, Irene
Sáinz Bueno, José Antonio
author García Mejido, José Antonio
author_facet García Mejido, José Antonio
Ramos Vega, Zenaida
Fernández Palacín, Ana
Borrero González, Carlota
Valdivia, Maribel
Pelayo Delgado, Irene
Sáinz Bueno, José Antonio
author_role author
author2 Ramos Vega, Zenaida
Fernández Palacín, Ana
Borrero González, Carlota
Valdivia, Maribel
Pelayo Delgado, Irene
Sáinz Bueno, José Antonio
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Cirugía
Medicina Preventiva y Salud Pública
dc.subject.none.fl_str_mv 3D Transperineal ultrasound
Pelvic organ prolapse
Uterine prolapse (UP)
Cervical elongation
Pelvic floor
topic 3D Transperineal ultrasound
Pelvic organ prolapse
Uterine prolapse (UP)
Cervical elongation
Pelvic floor
description We want to describe a model that allows the use of transperineal ultrasound to define the probability of experiencing uterine prolapse (UP). This was a prospective observational study involving 107 patients with UP or cervical elongation (CE) without UP. The ultrasound study was performed using transperineal ultrasound and evaluated the differences in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. We generated different multivariate binary logistic regression models using nonautomated methods to predict UP, including the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver. The parameters were added progressively according to their simplicity of use and their predictive capacity for identifying UP. We used two binary logistic regression models to predict UP. Model 1 was based on the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient [AUC: 0.967 (95% CI, 0.939–0.995; p < 0.0005)]. Model 2 used the difference in the pubis–uterine fundus distance at rest and with the Valsalva maneuver, age, avulsion and ballooning [AUC: 0.971 (95% CI, 0.945–0.997; p < 0.0005)]. In conclusion, the model based on the difference in the pubis– uterine fundus distance at rest and with the Valsalva maneuver and the age of the patient could predict 96.7% of patients with UP.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/145720
https://doi.org/10.3390/tomography8040144
url https://hdl.handle.net/11441/145720
https://doi.org/10.3390/tomography8040144
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Tomography, 8 (4), 1716-1725.
https://www.mdpi.com/2379-139X/8/4/144
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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