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Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review

INTRODUCTION: This study aimed to systematically review research on cinacalcet and secondary hyperparathyroidism (SHPT) using machine learning-based statistical analyses. METHODS: Publications indexed in the Web of Science Core Collection database on Cinacalcet and SHPT published between 2000 and 20...

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Detalles Bibliográficos
Autores principales: Li, Xiaosong, Ding, Wei, Zhang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395090/
https://www.ncbi.nlm.nih.gov/pubmed/37538795
http://dx.doi.org/10.3389/fendo.2023.1146955
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author Li, Xiaosong
Ding, Wei
Zhang, Hong
author_facet Li, Xiaosong
Ding, Wei
Zhang, Hong
author_sort Li, Xiaosong
collection PubMed
description INTRODUCTION: This study aimed to systematically review research on cinacalcet and secondary hyperparathyroidism (SHPT) using machine learning-based statistical analyses. METHODS: Publications indexed in the Web of Science Core Collection database on Cinacalcet and SHPT published between 2000 and 2022 were retrieved. The R package “Bibliometrix,” VOSviewer, CiteSpace, meta, and latent Dirichlet allocation (LDA) in Python were used to generate bibliometric and meta-analytical results. RESULTS: A total of 959 articles were included in our bibliometric analysis. In total, 3753 scholars from 54 countries contributed to this field of research. The United States, Japan, and China were found to be among the three most productive countries worldwide. Three Japanese institutions (Showa University, Tokai University, and Kobe University) published the most articles on Cinacalcet and SHPT. Fukagawa, M.; Chertow, G.M.; Goodman W.G. were the three authors who published the most articles in this field. Most articles were published in Nephrology Dialysis Transplantation, Kidney International, and Therapeutic Apheresis and Dialysis. Research on Cinacalcet and SHPT has mainly included three topics: 1) comparative effects of various treatments, 2) the safety and efficacy of cinacalcet, and 3) fibroblast growth factor-23 (FGF-23). Integrated treatments, cinacalcet use in pediatric chronic kidney disease, and new therapeutic targets are emerging research hotspots. Through a meta-analysis, we confirmed the effects of Cinacalcet on reducing serum PTH (SMD = -0.56, 95% CI = -0.76 to -0.37, p = 0.001) and calcium (SMD = -0.93, 95% CI = -1.21to -0.64, p = 0.001) and improving phosphate (SMD = 0.17, 95% CI = -0.33 to -0.01, p = 0.033) and calcium-phosphate product levels (SMD = -0.49, 95% CI = -0.71 to -0.28, p = 0.001); we found no difference in all-cause mortality (RR = 0.97, 95% CI = 0.90 to 1.05, p = 0.47), cardiovascular mortality (RR = 0.69, 95% CI = 0.36 to 1.31, p = 0.25), and parathyroidectomy (RR = 0.36, 95% CI = 0.09 to 1.35, p = 0.13) between the Cinacalcet and non-Cinacalcet users. Moreover, Cinacalcet was associated with an increased risk of nausea (RR = 2.29, 95% CI = 1.73 to 3.05, p = 0.001), hypocalcemia (RR = 4.05, 95% CI = 2.33 to 7.04, p = 0.001), and vomiting (RR = 1.90, 95% CI = 1.70 to 2.11, p = 0.001). DISCUSSION: The number of publications indexed to Cinacalcet and SHPT has increased rapidly over the past 22 years. Literature distribution, research topics, and emerging trends in publications on Cinacalcet and SHPT were analyzed using a machine learning-based bibliometric review. The findings of this meta-analysis provide valuable insights into the efficacy and safety of cinacalcet for the treatment of SHPT, which will be of interest to both clinical and researchers.
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spelling pubmed-103950902023-08-03 Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review Li, Xiaosong Ding, Wei Zhang, Hong Front Endocrinol (Lausanne) Endocrinology INTRODUCTION: This study aimed to systematically review research on cinacalcet and secondary hyperparathyroidism (SHPT) using machine learning-based statistical analyses. METHODS: Publications indexed in the Web of Science Core Collection database on Cinacalcet and SHPT published between 2000 and 2022 were retrieved. The R package “Bibliometrix,” VOSviewer, CiteSpace, meta, and latent Dirichlet allocation (LDA) in Python were used to generate bibliometric and meta-analytical results. RESULTS: A total of 959 articles were included in our bibliometric analysis. In total, 3753 scholars from 54 countries contributed to this field of research. The United States, Japan, and China were found to be among the three most productive countries worldwide. Three Japanese institutions (Showa University, Tokai University, and Kobe University) published the most articles on Cinacalcet and SHPT. Fukagawa, M.; Chertow, G.M.; Goodman W.G. were the three authors who published the most articles in this field. Most articles were published in Nephrology Dialysis Transplantation, Kidney International, and Therapeutic Apheresis and Dialysis. Research on Cinacalcet and SHPT has mainly included three topics: 1) comparative effects of various treatments, 2) the safety and efficacy of cinacalcet, and 3) fibroblast growth factor-23 (FGF-23). Integrated treatments, cinacalcet use in pediatric chronic kidney disease, and new therapeutic targets are emerging research hotspots. Through a meta-analysis, we confirmed the effects of Cinacalcet on reducing serum PTH (SMD = -0.56, 95% CI = -0.76 to -0.37, p = 0.001) and calcium (SMD = -0.93, 95% CI = -1.21to -0.64, p = 0.001) and improving phosphate (SMD = 0.17, 95% CI = -0.33 to -0.01, p = 0.033) and calcium-phosphate product levels (SMD = -0.49, 95% CI = -0.71 to -0.28, p = 0.001); we found no difference in all-cause mortality (RR = 0.97, 95% CI = 0.90 to 1.05, p = 0.47), cardiovascular mortality (RR = 0.69, 95% CI = 0.36 to 1.31, p = 0.25), and parathyroidectomy (RR = 0.36, 95% CI = 0.09 to 1.35, p = 0.13) between the Cinacalcet and non-Cinacalcet users. Moreover, Cinacalcet was associated with an increased risk of nausea (RR = 2.29, 95% CI = 1.73 to 3.05, p = 0.001), hypocalcemia (RR = 4.05, 95% CI = 2.33 to 7.04, p = 0.001), and vomiting (RR = 1.90, 95% CI = 1.70 to 2.11, p = 0.001). DISCUSSION: The number of publications indexed to Cinacalcet and SHPT has increased rapidly over the past 22 years. Literature distribution, research topics, and emerging trends in publications on Cinacalcet and SHPT were analyzed using a machine learning-based bibliometric review. The findings of this meta-analysis provide valuable insights into the efficacy and safety of cinacalcet for the treatment of SHPT, which will be of interest to both clinical and researchers. Frontiers Media S.A. 2023-07-19 /pmc/articles/PMC10395090/ /pubmed/37538795 http://dx.doi.org/10.3389/fendo.2023.1146955 Text en Copyright © 2023 Li, Ding and Zhang https://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 Endocrinology
Li, Xiaosong
Ding, Wei
Zhang, Hong
Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title_full Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title_fullStr Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title_full_unstemmed Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title_short Cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
title_sort cinacalcet use in secondary hyperparathyroidism: a machine learning-based systematic review
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395090/
https://www.ncbi.nlm.nih.gov/pubmed/37538795
http://dx.doi.org/10.3389/fendo.2023.1146955
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