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Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis
Objective. Esophageal dysmotility is a common and neglected complication of systemic sclerosis (SSc) associated with poor prognosis, while the assessment remains a challenge. We aimed to develop a diagnostic model for esophageal dysmotility in SSc patients that provides individualized risk estimates...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776849/ https://www.ncbi.nlm.nih.gov/pubmed/36553149 http://dx.doi.org/10.3390/diagnostics12123142 |
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author | Liu, Peiling Chai, Jing Dai, Liyi Chen, Beidi Zhao, Jinxia Lu, Ming Zeng, Lin Xia, Zhiwei Mu, Rong |
author_facet | Liu, Peiling Chai, Jing Dai, Liyi Chen, Beidi Zhao, Jinxia Lu, Ming Zeng, Lin Xia, Zhiwei Mu, Rong |
author_sort | Liu, Peiling |
collection | PubMed |
description | Objective. Esophageal dysmotility is a common and neglected complication of systemic sclerosis (SSc) associated with poor prognosis, while the assessment remains a challenge. We aimed to develop a diagnostic model for esophageal dysmotility in SSc patients that provides individualized risk estimates. Methods. Seventy-five SSc patients who underwent high-resolution manometry (HRM) were included in the study. Esophageal widest diameter (WED) was measured on a chest CT scan. Esophageal parameters between patients with and without esophageal dysmotility were compared. Multivariate logistic regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to fit the model. The diagnostic model was evaluated by discrimination and calibration. Internal validation was estimated using the enhanced bootstrap method with 1000 repetitions. Results. Sixty-one systemic sclerosis patients (81.3%) were diagnosed with esophageal dysmotility according to the Chicago Classification v 3.0. The diagnostic model for evaluating the probability of esophageal dysmotility integrated clinical and imaging features, including disease duration, ILD, and WED. The model displayed good discrimination with an area under the curve (AUC) of 0.923 (95% CI: 0.837–1.000), a Brier score of 0.083, and good calibration. A high AUC value of 0.911 could still be achieved in the internal validation. Conclusion. The diagnostic model, which combines the disease duration, ILD, and imaging feature (WED), is an effective and noninvasive method for predicting esophageal dysmotility in SSc patients. |
format | Online Article Text |
id | pubmed-9776849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97768492022-12-23 Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis Liu, Peiling Chai, Jing Dai, Liyi Chen, Beidi Zhao, Jinxia Lu, Ming Zeng, Lin Xia, Zhiwei Mu, Rong Diagnostics (Basel) Article Objective. Esophageal dysmotility is a common and neglected complication of systemic sclerosis (SSc) associated with poor prognosis, while the assessment remains a challenge. We aimed to develop a diagnostic model for esophageal dysmotility in SSc patients that provides individualized risk estimates. Methods. Seventy-five SSc patients who underwent high-resolution manometry (HRM) were included in the study. Esophageal widest diameter (WED) was measured on a chest CT scan. Esophageal parameters between patients with and without esophageal dysmotility were compared. Multivariate logistic regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to fit the model. The diagnostic model was evaluated by discrimination and calibration. Internal validation was estimated using the enhanced bootstrap method with 1000 repetitions. Results. Sixty-one systemic sclerosis patients (81.3%) were diagnosed with esophageal dysmotility according to the Chicago Classification v 3.0. The diagnostic model for evaluating the probability of esophageal dysmotility integrated clinical and imaging features, including disease duration, ILD, and WED. The model displayed good discrimination with an area under the curve (AUC) of 0.923 (95% CI: 0.837–1.000), a Brier score of 0.083, and good calibration. A high AUC value of 0.911 could still be achieved in the internal validation. Conclusion. The diagnostic model, which combines the disease duration, ILD, and imaging feature (WED), is an effective and noninvasive method for predicting esophageal dysmotility in SSc patients. MDPI 2022-12-13 /pmc/articles/PMC9776849/ /pubmed/36553149 http://dx.doi.org/10.3390/diagnostics12123142 Text en © 2022 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 Liu, Peiling Chai, Jing Dai, Liyi Chen, Beidi Zhao, Jinxia Lu, Ming Zeng, Lin Xia, Zhiwei Mu, Rong Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title | Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title_full | Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title_fullStr | Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title_full_unstemmed | Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title_short | Development of a Diagnostic Model Focusing on Esophageal Dysmotility in Patients with Systemic Sclerosis |
title_sort | development of a diagnostic model focusing on esophageal dysmotility in patients with systemic sclerosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776849/ https://www.ncbi.nlm.nih.gov/pubmed/36553149 http://dx.doi.org/10.3390/diagnostics12123142 |
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