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Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children
Objective: We aimed to establish useful models for the clinical differential diagnosis between vasovagal syncope (VVS) and psychogenic pseudosyncope (PPS). Methods: This bicentric study included 176 patients (150 VVS and 26 PPS cases) for model development. Based on the results of univariate and mul...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989585/ https://www.ncbi.nlm.nih.gov/pubmed/32038462 http://dx.doi.org/10.3389/fneur.2019.01392 |
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author | Zhang, Zhening Jiang, Xingyuan Han, Lu Chen, Selena Tao, Ling Tao, Chunyan Tian, Hong Du, Junbao |
author_facet | Zhang, Zhening Jiang, Xingyuan Han, Lu Chen, Selena Tao, Ling Tao, Chunyan Tian, Hong Du, Junbao |
author_sort | Zhang, Zhening |
collection | PubMed |
description | Objective: We aimed to establish useful models for the clinical differential diagnosis between vasovagal syncope (VVS) and psychogenic pseudosyncope (PPS). Methods: This bicentric study included 176 patients (150 VVS and 26 PPS cases) for model development. Based on the results of univariate and multivariate analyses, a logistic regression model and a scoring model were established and their abilities to differentiate VVS from PPS were tested. Another 78 patients (53 VVS and 25 PPS) were used for external validation. Results: In the logistic regression model, the outcome indicated that the QT-dispersion (QTd) (P < 0.001), syncope duration (P < 0.001), and upright posture (P < 0.001) acted as independent factors for the differentiation of VVS from PPS, which generated an area under the curve (AUC) of 0.892. A cutoff value of 0.234 yielded a sensitivity and specificity of 89.3 and 80.8%, respectively, for the differentiation between VVS and PPS in the logistic regression model. In the scoring model which consists of three variables, a cutoff score of three points yielded a sensitivity and specificity of 91.3 and 76.9%, respectively, with an AUC of 0.909. The external validation test indicated that the negative and positive predictive values of the scoring model were 78.8 and 91.7%, respectively, and the accuracy was 80.8%. Conclusion: The scoring model consisting of three variables is an easy-to-perform, inexpensive, and non-invasive measure for initial differential diagnosis between VVS and PPS. |
format | Online Article Text |
id | pubmed-6989585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69895852020-02-07 Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children Zhang, Zhening Jiang, Xingyuan Han, Lu Chen, Selena Tao, Ling Tao, Chunyan Tian, Hong Du, Junbao Front Neurol Neurology Objective: We aimed to establish useful models for the clinical differential diagnosis between vasovagal syncope (VVS) and psychogenic pseudosyncope (PPS). Methods: This bicentric study included 176 patients (150 VVS and 26 PPS cases) for model development. Based on the results of univariate and multivariate analyses, a logistic regression model and a scoring model were established and their abilities to differentiate VVS from PPS were tested. Another 78 patients (53 VVS and 25 PPS) were used for external validation. Results: In the logistic regression model, the outcome indicated that the QT-dispersion (QTd) (P < 0.001), syncope duration (P < 0.001), and upright posture (P < 0.001) acted as independent factors for the differentiation of VVS from PPS, which generated an area under the curve (AUC) of 0.892. A cutoff value of 0.234 yielded a sensitivity and specificity of 89.3 and 80.8%, respectively, for the differentiation between VVS and PPS in the logistic regression model. In the scoring model which consists of three variables, a cutoff score of three points yielded a sensitivity and specificity of 91.3 and 76.9%, respectively, with an AUC of 0.909. The external validation test indicated that the negative and positive predictive values of the scoring model were 78.8 and 91.7%, respectively, and the accuracy was 80.8%. Conclusion: The scoring model consisting of three variables is an easy-to-perform, inexpensive, and non-invasive measure for initial differential diagnosis between VVS and PPS. Frontiers Media S.A. 2020-01-23 /pmc/articles/PMC6989585/ /pubmed/32038462 http://dx.doi.org/10.3389/fneur.2019.01392 Text en Copyright © 2020 Zhang, Jiang, Han, Chen, Tao, Tao, Tian and Du. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Zhang, Zhening Jiang, Xingyuan Han, Lu Chen, Selena Tao, Ling Tao, Chunyan Tian, Hong Du, Junbao Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title | Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title_full | Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title_fullStr | Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title_full_unstemmed | Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title_short | Differential Diagnostic Models Between Vasovagal Syncope and Psychogenic Pseudosyncope in Children |
title_sort | differential diagnostic models between vasovagal syncope and psychogenic pseudosyncope in children |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6989585/ https://www.ncbi.nlm.nih.gov/pubmed/32038462 http://dx.doi.org/10.3389/fneur.2019.01392 |
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