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Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer sci...

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Autores principales: Huynh, Quynh Thi Vu, Le, Nguyen Quoc Khanh, Huang, Shih-Yi, Ho, Ban Tran, Vu, Tru Huy, Pham, Hong Thi Minh, Pham, An Le, Hou, Jia-Woei, Nguyen, Ngan Thi Kim, Chen, Yang Ching
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782515/
https://www.ncbi.nlm.nih.gov/pubmed/35061754
http://dx.doi.org/10.1371/journal.pone.0261965
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author Huynh, Quynh Thi Vu
Le, Nguyen Quoc Khanh
Huang, Shih-Yi
Ho, Ban Tran
Vu, Tru Huy
Pham, Hong Thi Minh
Pham, An Le
Hou, Jia-Woei
Nguyen, Ngan Thi Kim
Chen, Yang Ching
author_facet Huynh, Quynh Thi Vu
Le, Nguyen Quoc Khanh
Huang, Shih-Yi
Ho, Ban Tran
Vu, Tru Huy
Pham, Hong Thi Minh
Pham, An Le
Hou, Jia-Woei
Nguyen, Ngan Thi Kim
Chen, Yang Ching
author_sort Huynh, Quynh Thi Vu
collection PubMed
description BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis. METHODS: We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation). RESULTS: Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test. CONCLUSIONS: We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings.
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spelling pubmed-87825152022-01-22 Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches Huynh, Quynh Thi Vu Le, Nguyen Quoc Khanh Huang, Shih-Yi Ho, Ban Tran Vu, Tru Huy Pham, Hong Thi Minh Pham, An Le Hou, Jia-Woei Nguyen, Ngan Thi Kim Chen, Yang Ching PLoS One Research Article BACKGROUND: A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis. METHODS: We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation). RESULTS: Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test. CONCLUSIONS: We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings. Public Library of Science 2022-01-21 /pmc/articles/PMC8782515/ /pubmed/35061754 http://dx.doi.org/10.1371/journal.pone.0261965 Text en © 2022 Huynh et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Huynh, Quynh Thi Vu
Le, Nguyen Quoc Khanh
Huang, Shih-Yi
Ho, Ban Tran
Vu, Tru Huy
Pham, Hong Thi Minh
Pham, An Le
Hou, Jia-Woei
Nguyen, Ngan Thi Kim
Chen, Yang Ching
Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title_full Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title_fullStr Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title_full_unstemmed Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title_short Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches
title_sort development and validation of clinical diagnostic model for girls with central precocious puberty: machine-learning approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782515/
https://www.ncbi.nlm.nih.gov/pubmed/35061754
http://dx.doi.org/10.1371/journal.pone.0261965
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