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The retrospective data analysis on the pedigree of nervous system diseases in children
Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as pred...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322957/ https://www.ncbi.nlm.nih.gov/pubmed/37407586 http://dx.doi.org/10.1038/s41598-023-35571-0 |
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author | Liu, Xiaohui Chen, Huanxi Ma, Xiansi Yu, Hongjia Yang, Haiyan Ai, Liang Liu, Qing Wu, Liwen |
author_facet | Liu, Xiaohui Chen, Huanxi Ma, Xiansi Yu, Hongjia Yang, Haiyan Ai, Liang Liu, Qing Wu, Liwen |
author_sort | Liu, Xiaohui |
collection | PubMed |
description | Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as predict changes in later disease pedigrees and the direction of diagnosis and treatment. The inpatients of the neurology department from January 2012 to December 2020 in Hunan Children's Hospital were retrospectively analyzed. There were 36,777 patients enrolled in this study. The next analysis was based on factors like age, gender, length of stay (LoS), number of patients per month and per year (MNoP and ANoP, respectively), and average daily hospital cost (ADHE). To evaluate the characteristics of neurological diseases, we applied a series of statistical tools such as numerical characteristics, boxplots, density charts, one-way ANOVA, Kruskal–Wallis tests, time-series plots, and seasonally adjusted indices. The statistical analysis of neurological diseases led to the following conclusions: First, children having neurological illnesses are most likely to develop them between the ages of 4 and 8 years. Benign intracranial hypertension was the youngest mean age of onset among the various neurologic diseases, and most patients with bacterial intracranial infection were young children. Some diseases have a similar mean age of onset, such as seizures (gastroenteritis/diarrhea) and febrile convulsions. Second, women made up most patients with autoimmune diseases of the central nervous system. Treatment options for inherited metabolic encephalopathy and epilepsy are similar, but they differ significantly for viral intracranial infection. Some neurologic diseases were found to have seasonal variations; for example, infectious diseases of the central nervous system were shown to occur more commonly in the warm season, whereas, autoimmune diseases primarily appeared in the autumn and winter months. Additionally, the number of patients admitted to hospitals with intracranial infections and encephalopathy has dramatically dropped recently, but the number of patients with autoimmune diseases of the central nervous system and hereditary metabolic encephalopathy has been rising year over year. Finally, we discovered apparent polycentric distributions in various illnesses’ density distributions. The study offered an epidemiological basis for common nervous system diseases, including evidence from age of onset, number of cases, and so on. The pedigree of nervous system diseases has significantly changed. The proportion of patients with neuroimmune diseases and genetic metabolic diseases is rising while the number of patients with infection-related diseases and uncertain diagnoses is decreasing. The existence of a disease multimodal model suggests that there is still a lack of understanding of many diseases' diagnosis and treatment, which needs to be improved further because accurate diagnosis aids in the formulation of individualized treatment plans and the allocation of medical resources; additionally, there is still a lack of effective treatment for most genetic diseases. The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions, and awareness of daily health care. |
format | Online Article Text |
id | pubmed-10322957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103229572023-07-07 The retrospective data analysis on the pedigree of nervous system diseases in children Liu, Xiaohui Chen, Huanxi Ma, Xiansi Yu, Hongjia Yang, Haiyan Ai, Liang Liu, Qing Wu, Liwen Sci Rep Article Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as predict changes in later disease pedigrees and the direction of diagnosis and treatment. The inpatients of the neurology department from January 2012 to December 2020 in Hunan Children's Hospital were retrospectively analyzed. There were 36,777 patients enrolled in this study. The next analysis was based on factors like age, gender, length of stay (LoS), number of patients per month and per year (MNoP and ANoP, respectively), and average daily hospital cost (ADHE). To evaluate the characteristics of neurological diseases, we applied a series of statistical tools such as numerical characteristics, boxplots, density charts, one-way ANOVA, Kruskal–Wallis tests, time-series plots, and seasonally adjusted indices. The statistical analysis of neurological diseases led to the following conclusions: First, children having neurological illnesses are most likely to develop them between the ages of 4 and 8 years. Benign intracranial hypertension was the youngest mean age of onset among the various neurologic diseases, and most patients with bacterial intracranial infection were young children. Some diseases have a similar mean age of onset, such as seizures (gastroenteritis/diarrhea) and febrile convulsions. Second, women made up most patients with autoimmune diseases of the central nervous system. Treatment options for inherited metabolic encephalopathy and epilepsy are similar, but they differ significantly for viral intracranial infection. Some neurologic diseases were found to have seasonal variations; for example, infectious diseases of the central nervous system were shown to occur more commonly in the warm season, whereas, autoimmune diseases primarily appeared in the autumn and winter months. Additionally, the number of patients admitted to hospitals with intracranial infections and encephalopathy has dramatically dropped recently, but the number of patients with autoimmune diseases of the central nervous system and hereditary metabolic encephalopathy has been rising year over year. Finally, we discovered apparent polycentric distributions in various illnesses’ density distributions. The study offered an epidemiological basis for common nervous system diseases, including evidence from age of onset, number of cases, and so on. The pedigree of nervous system diseases has significantly changed. The proportion of patients with neuroimmune diseases and genetic metabolic diseases is rising while the number of patients with infection-related diseases and uncertain diagnoses is decreasing. The existence of a disease multimodal model suggests that there is still a lack of understanding of many diseases' diagnosis and treatment, which needs to be improved further because accurate diagnosis aids in the formulation of individualized treatment plans and the allocation of medical resources; additionally, there is still a lack of effective treatment for most genetic diseases. The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions, and awareness of daily health care. Nature Publishing Group UK 2023-07-05 /pmc/articles/PMC10322957/ /pubmed/37407586 http://dx.doi.org/10.1038/s41598-023-35571-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Xiaohui Chen, Huanxi Ma, Xiansi Yu, Hongjia Yang, Haiyan Ai, Liang Liu, Qing Wu, Liwen The retrospective data analysis on the pedigree of nervous system diseases in children |
title | The retrospective data analysis on the pedigree of nervous system diseases in children |
title_full | The retrospective data analysis on the pedigree of nervous system diseases in children |
title_fullStr | The retrospective data analysis on the pedigree of nervous system diseases in children |
title_full_unstemmed | The retrospective data analysis on the pedigree of nervous system diseases in children |
title_short | The retrospective data analysis on the pedigree of nervous system diseases in children |
title_sort | retrospective data analysis on the pedigree of nervous system diseases in children |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322957/ https://www.ncbi.nlm.nih.gov/pubmed/37407586 http://dx.doi.org/10.1038/s41598-023-35571-0 |
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