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Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters

Objectives: Are other pain symptoms in addition to dysmenorrhea, dyspareunia, dyschezia, dysuria, and chronic pelvic pain correlated to endometriosis and suitable for a clinical prediction model? Methods: We conducted a prospective study from 2016 to 2022, including a total of 269 women with numerou...

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Autores principales: Konrad, Lutz, Fruhmann Berger, Lea M., Maier, Veronica, Horné, Fabian, Neuheisel, Laura M., Laucks, Elisa V., Riaz, Muhammad A., Oehmke, Frank, Meinhold-Heerlein, Ivo, Zeppernick, Felix
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10342998/
https://www.ncbi.nlm.nih.gov/pubmed/37445265
http://dx.doi.org/10.3390/jcm12134231
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author Konrad, Lutz
Fruhmann Berger, Lea M.
Maier, Veronica
Horné, Fabian
Neuheisel, Laura M.
Laucks, Elisa V.
Riaz, Muhammad A.
Oehmke, Frank
Meinhold-Heerlein, Ivo
Zeppernick, Felix
author_facet Konrad, Lutz
Fruhmann Berger, Lea M.
Maier, Veronica
Horné, Fabian
Neuheisel, Laura M.
Laucks, Elisa V.
Riaz, Muhammad A.
Oehmke, Frank
Meinhold-Heerlein, Ivo
Zeppernick, Felix
author_sort Konrad, Lutz
collection PubMed
description Objectives: Are other pain symptoms in addition to dysmenorrhea, dyspareunia, dyschezia, dysuria, and chronic pelvic pain correlated to endometriosis and suitable for a clinical prediction model? Methods: We conducted a prospective study from 2016 to 2022, including a total of 269 women with numerous pain symptoms and other parameters. All women filled out two questionnaires and were examined by palpation and transvaginal ultrasound (TVUS). In cases of suspected deep endometriosis, magnetic resonance imaging (MRI) was performed. After the operation, endometriosis was diagnosed by histological examination. Results: All in all, 30 significant parameters and 6 significant numeric rating scale (NRS) scores associated with endometriosis could be identified: 7 pain adjectives, 8 endometriosis-associated pain symptoms, 5 pain localizations, 6 parameters from the PainDETECT, consumption of analgesics, and allergies. Furthermore, longer pain duration (before, during, and after menstruation) was observed in women with endometriosis compared to women without endometriosis (34.0% vs. 12.3%, respectively). Although no specific pain for endometriosis could be identified for all women, a subgroup with endometriosis reported radiating pain to the thighs/legs in contrast to a lower number of women without endometriosis (33.9% vs. 15.2%, respectively). Furthermore, a subgroup of women with endometriosis suffered from dysuria compared to patients without endometriosis (32.2% vs. 4.3%, respectively). Remarkably, the numbers of significant parameters were significantly higher in women with endometriosis compared to women without endometriosis (14.10 ± 4.2 vs. 7.75 ± 5.8, respectively). A decision tree was developed, resulting in 0.904 sensitivity, 0.750 specificity, 0.874 positive predictive values (PPV), 0.802 negative predictive values (NPV), 28.235 odds ratio (OR), and 4.423 relative risks (RR). The PPV of 0.874 is comparable to the positive prediction of endometriosis by the clinicians of 0.86 (177/205). Conclusions: The presented predictive model will enable a non-invasive diagnosis of endometriosis and can also be used by both patients and clinicians for surveillance of the disease before and after surgery. In cases of positivety, as evaluated by the questionnaire, patients can then seek advice again. Similarly, patients without an operation but with medical therapy can be monitored with the questionnaire.
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spelling pubmed-103429982023-07-14 Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters Konrad, Lutz Fruhmann Berger, Lea M. Maier, Veronica Horné, Fabian Neuheisel, Laura M. Laucks, Elisa V. Riaz, Muhammad A. Oehmke, Frank Meinhold-Heerlein, Ivo Zeppernick, Felix J Clin Med Article Objectives: Are other pain symptoms in addition to dysmenorrhea, dyspareunia, dyschezia, dysuria, and chronic pelvic pain correlated to endometriosis and suitable for a clinical prediction model? Methods: We conducted a prospective study from 2016 to 2022, including a total of 269 women with numerous pain symptoms and other parameters. All women filled out two questionnaires and were examined by palpation and transvaginal ultrasound (TVUS). In cases of suspected deep endometriosis, magnetic resonance imaging (MRI) was performed. After the operation, endometriosis was diagnosed by histological examination. Results: All in all, 30 significant parameters and 6 significant numeric rating scale (NRS) scores associated with endometriosis could be identified: 7 pain adjectives, 8 endometriosis-associated pain symptoms, 5 pain localizations, 6 parameters from the PainDETECT, consumption of analgesics, and allergies. Furthermore, longer pain duration (before, during, and after menstruation) was observed in women with endometriosis compared to women without endometriosis (34.0% vs. 12.3%, respectively). Although no specific pain for endometriosis could be identified for all women, a subgroup with endometriosis reported radiating pain to the thighs/legs in contrast to a lower number of women without endometriosis (33.9% vs. 15.2%, respectively). Furthermore, a subgroup of women with endometriosis suffered from dysuria compared to patients without endometriosis (32.2% vs. 4.3%, respectively). Remarkably, the numbers of significant parameters were significantly higher in women with endometriosis compared to women without endometriosis (14.10 ± 4.2 vs. 7.75 ± 5.8, respectively). A decision tree was developed, resulting in 0.904 sensitivity, 0.750 specificity, 0.874 positive predictive values (PPV), 0.802 negative predictive values (NPV), 28.235 odds ratio (OR), and 4.423 relative risks (RR). The PPV of 0.874 is comparable to the positive prediction of endometriosis by the clinicians of 0.86 (177/205). Conclusions: The presented predictive model will enable a non-invasive diagnosis of endometriosis and can also be used by both patients and clinicians for surveillance of the disease before and after surgery. In cases of positivety, as evaluated by the questionnaire, patients can then seek advice again. Similarly, patients without an operation but with medical therapy can be monitored with the questionnaire. MDPI 2023-06-23 /pmc/articles/PMC10342998/ /pubmed/37445265 http://dx.doi.org/10.3390/jcm12134231 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Konrad, Lutz
Fruhmann Berger, Lea M.
Maier, Veronica
Horné, Fabian
Neuheisel, Laura M.
Laucks, Elisa V.
Riaz, Muhammad A.
Oehmke, Frank
Meinhold-Heerlein, Ivo
Zeppernick, Felix
Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title_full Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title_fullStr Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title_full_unstemmed Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title_short Predictive Model for the Non-Invasive Diagnosis of Endometriosis Based on Clinical Parameters
title_sort predictive model for the non-invasive diagnosis of endometriosis based on clinical parameters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10342998/
https://www.ncbi.nlm.nih.gov/pubmed/37445265
http://dx.doi.org/10.3390/jcm12134231
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