ArticleFrontiers in cardiovascular medicine2022
Differential Diagnosis Between Psychogenic Pseudosyncope and Vasovagal Syncope in Children: A Quantitative Scoring Model Based on Clinical Manifestations.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for the diagnosis and treatment of neurally mediated syncope in children and adolescents (revised 2024).World journal of pediatrics : WJP · 2024Guideline
- Difference in clinical characteristics between functional syncope and vasovagal syncope in children.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Predictive value of Tpeak-Tend interval of supine and orthostatic electrocardiogram in the diagnosis between psychogenic pseudosyncope and vasovagal syncope in children.Scientific reports · 2024Article
- Value of catecholamine levels in the differential diagnosis of vasovagal syncope and psychogenic pseudosyncope in children.Frontiers in pediatrics · 2024Article
- Pediatric syncope: pearls and pitfalls in history taking.Clinical and experimental pediatrics · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
The study was designed to explore a clinical manifestation-based quantitative scoring model to assist the differentiation between psychogenic pseudosyncope (PPS) and vasovagal syncope (VVS) in children. In this retrospective case-control study, the training set included 233 pediatric patients aged 5-17 years (183 children with VVS and 50 with PPS) and the validation set consisted of another 138 patients aged 5-15 years (100 children with VVS and 38 with PPS). In the training set study, the demographic characteristics and clinical presentation of patients were compared between PPS and VVS. The independent variables were analyzed by binary logistic regression, and the score for each variable was given according to the approximate values of odds ratio (OR) to develop a scoring model for distinguishing PPS and VVS. The cut-off scores and area under the curve (AUC) for differentiating PPS and VVS cases were calculated using receiver operating characteristic (ROC) curve. Then, the ability of the scoring model to differentiate PPS from VVS was validated by the true clinical diagnosis of PPS and VVS in the validation set. In the training set, there were 7 variables with significant differences between the PPS and VVS groups, including duration of loss of consciousness (DLOC) (
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