[中国五岁以下儿童严重呼吸道合胞病毒感染个体风险预测工具的开发与验证]。

B B Cong, S Y Deng, S H Ma, Y M Miao, Y Li
{"title":"[中国五岁以下儿童严重呼吸道合胞病毒感染个体风险预测工具的开发与验证]。","authors":"B B Cong, S Y Deng, S H Ma, Y M Miao, Y Li","doi":"10.3760/cma.j.cn112150-20231206-00406","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objective:</b> To construct a predictive model to assess the risk of severe respiratory syncytial virus infection among children under five years in China, conduct preliminary validation of this model by using external data, and develop an individual risk assessment tool available for their parents. <b>Methods:</b> The admission after RSV infection was used as a marker of severe infection. Based on the evidence of RSV hospitalization-related risk factors and real-world data, such as the prevalence of various risk factors in children under five years old in China, a Monte Carlo-based individual RSV hospitalization risk prediction model for children under five years old was constructed. Taking Suzhou City as an example, the model was externally validated, and an interactive risk prediction tool (RSV HeaRT) was developed on the WeChat mini-program platform. <b>Results:</b> The estimation model showed that in children under five years old in China if the population did not have any risk factors for severe RSV infection, the RSV annual hospitalization rate was 2.2/1 000 (95%<i>CI</i>: 0.9/1 000-7.5/1 000). Based on this baseline hospitalization rate and the prevalence of related risk factors in Suzhou, the model predicted an RSV hospitalization rate of 8.0/1 000 (95%<i>CI</i>: 4.6/1 000-24.4/1 000) for children under five years old annually in Suzhou, which was close to the reported RSV hospitalization rate in literature (10/1 000-20/1 000). In the developed RSV HeaRT WeChat mini-program, target users (such as parents of children) could input basic information, disease history, and social environmental factors of the child into the mini-program, and the tool could provide real-time feedback on the following predicted results: First, the relative risk of hospitalization due to RSV infection in current children compared to general children; Second, the probability of hospitalization due to RSV infection within the next year; Third, the relative risk of adverse outcomes during hospitalization in the event of RSV infection. <b>Conclusion:</b> This study is based on real-world evidence related to RSV hospitalization risk and constructs an RSV hospitalization risk prediction model suitable for Chinese children based on the combination of the current prevalence of risk factors in children under five years old in China. The accuracy of the prediction model results has been preliminarily demonstrated. Based on this design, the RSV HeaRT developed can facilitate parents to evaluate the hospitalization risk of children.</p>","PeriodicalId":24033,"journal":{"name":"中华预防医学杂志","volume":"58 8","pages":"1135-1142"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Development and validation of an individual risk prediction tool for severe respiratory syncytial virus infection among children under five years in China].\",\"authors\":\"B B Cong, S Y Deng, S H Ma, Y M Miao, Y Li\",\"doi\":\"10.3760/cma.j.cn112150-20231206-00406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Objective:</b> To construct a predictive model to assess the risk of severe respiratory syncytial virus infection among children under five years in China, conduct preliminary validation of this model by using external data, and develop an individual risk assessment tool available for their parents. <b>Methods:</b> The admission after RSV infection was used as a marker of severe infection. Based on the evidence of RSV hospitalization-related risk factors and real-world data, such as the prevalence of various risk factors in children under five years old in China, a Monte Carlo-based individual RSV hospitalization risk prediction model for children under five years old was constructed. Taking Suzhou City as an example, the model was externally validated, and an interactive risk prediction tool (RSV HeaRT) was developed on the WeChat mini-program platform. <b>Results:</b> The estimation model showed that in children under five years old in China if the population did not have any risk factors for severe RSV infection, the RSV annual hospitalization rate was 2.2/1 000 (95%<i>CI</i>: 0.9/1 000-7.5/1 000). Based on this baseline hospitalization rate and the prevalence of related risk factors in Suzhou, the model predicted an RSV hospitalization rate of 8.0/1 000 (95%<i>CI</i>: 4.6/1 000-24.4/1 000) for children under five years old annually in Suzhou, which was close to the reported RSV hospitalization rate in literature (10/1 000-20/1 000). In the developed RSV HeaRT WeChat mini-program, target users (such as parents of children) could input basic information, disease history, and social environmental factors of the child into the mini-program, and the tool could provide real-time feedback on the following predicted results: First, the relative risk of hospitalization due to RSV infection in current children compared to general children; Second, the probability of hospitalization due to RSV infection within the next year; Third, the relative risk of adverse outcomes during hospitalization in the event of RSV infection. <b>Conclusion:</b> This study is based on real-world evidence related to RSV hospitalization risk and constructs an RSV hospitalization risk prediction model suitable for Chinese children based on the combination of the current prevalence of risk factors in children under five years old in China. The accuracy of the prediction model results has been preliminarily demonstrated. Based on this design, the RSV HeaRT developed can facilitate parents to evaluate the hospitalization risk of children.</p>\",\"PeriodicalId\":24033,\"journal\":{\"name\":\"中华预防医学杂志\",\"volume\":\"58 8\",\"pages\":\"1135-1142\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中华预防医学杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3760/cma.j.cn112150-20231206-00406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华预防医学杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/cma.j.cn112150-20231206-00406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

目的:构建一个预测模型,以评估中国五岁以下儿童感染严重呼吸道合胞病毒的风险:构建评估中国五岁以下儿童严重呼吸道合胞病毒感染风险的预测模型,利用外部数据对该模型进行初步验证,并开发供家长使用的个人风险评估工具。方法将感染 RSV 后的入院时间作为严重感染的标志。根据RSV住院相关风险因素的证据和真实世界的数据,如中国5岁以下儿童各种风险因素的流行率,构建了基于蒙特卡洛的5岁以下儿童RSV住院风险预测模型。以苏州市为例,对模型进行了外部验证,并在微信小程序平台上开发了交互式风险预测工具(RSV HeaRT)。结果显示估算模型显示,在中国,如果人群不存在任何严重RSV感染的风险因素,5岁以下儿童的RSV年住院率为2.2/1000(95%CI:0.9/1000-7.5/1000)。根据这一基线住院率和相关风险因素在苏州的流行情况,模型预测苏州 5 岁以下儿童 RSV 年住院率为 8.0/1 000(95%CI:4.6/1 000-24.4/1 000),接近文献报道的 RSV 住院率(10/1 000-20/1 000)。在开发的RSV HeaRT微信小程序中,目标用户(如患儿家长)可将患儿的基本信息、疾病史、社会环境因素等输入小程序,工具可实时反馈以下预测结果:第一,与普通儿童相比,当前儿童因感染 RSV 而住院的相对风险;第二,未来一年内因感染 RSV 而住院的概率;第三,一旦感染 RSV,住院期间不良后果的相对风险。结论:本研究基于真实世界中与 RSV 住院风险相关的证据,结合目前中国 5 岁以下儿童的风险因素流行情况,构建了适合中国儿童的 RSV 住院风险预测模型。预测模型结果的准确性已得到初步验证。基于此设计开发的RSV HeaRT可帮助家长评估儿童的住院风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
[Development and validation of an individual risk prediction tool for severe respiratory syncytial virus infection among children under five years in China].

Objective: To construct a predictive model to assess the risk of severe respiratory syncytial virus infection among children under five years in China, conduct preliminary validation of this model by using external data, and develop an individual risk assessment tool available for their parents. Methods: The admission after RSV infection was used as a marker of severe infection. Based on the evidence of RSV hospitalization-related risk factors and real-world data, such as the prevalence of various risk factors in children under five years old in China, a Monte Carlo-based individual RSV hospitalization risk prediction model for children under five years old was constructed. Taking Suzhou City as an example, the model was externally validated, and an interactive risk prediction tool (RSV HeaRT) was developed on the WeChat mini-program platform. Results: The estimation model showed that in children under five years old in China if the population did not have any risk factors for severe RSV infection, the RSV annual hospitalization rate was 2.2/1 000 (95%CI: 0.9/1 000-7.5/1 000). Based on this baseline hospitalization rate and the prevalence of related risk factors in Suzhou, the model predicted an RSV hospitalization rate of 8.0/1 000 (95%CI: 4.6/1 000-24.4/1 000) for children under five years old annually in Suzhou, which was close to the reported RSV hospitalization rate in literature (10/1 000-20/1 000). In the developed RSV HeaRT WeChat mini-program, target users (such as parents of children) could input basic information, disease history, and social environmental factors of the child into the mini-program, and the tool could provide real-time feedback on the following predicted results: First, the relative risk of hospitalization due to RSV infection in current children compared to general children; Second, the probability of hospitalization due to RSV infection within the next year; Third, the relative risk of adverse outcomes during hospitalization in the event of RSV infection. Conclusion: This study is based on real-world evidence related to RSV hospitalization risk and constructs an RSV hospitalization risk prediction model suitable for Chinese children based on the combination of the current prevalence of risk factors in children under five years old in China. The accuracy of the prediction model results has been preliminarily demonstrated. Based on this design, the RSV HeaRT developed can facilitate parents to evaluate the hospitalization risk of children.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
中华预防医学杂志
中华预防医学杂志 Medicine-Medicine (all)
CiteScore
1.20
自引率
0.00%
发文量
12678
期刊介绍: Chinese Journal of Preventive Medicine (CJPM), the successor to Chinese Health Journal , was initiated on October 1, 1953. In 1960, it was amalgamated with the Chinese Medical Journal and the Journal of Medical History and Health Care , and thereafter, was renamed as People’s Care . On November 25, 1978, the publication was denominated as Chinese Journal of Preventive Medicine . The contents of CJPM deal with a wide range of disciplines and technologies including epidemiology, environmental health, nutrition and food hygiene, occupational health, hygiene for children and adolescents, radiological health, toxicology, biostatistics, social medicine, pathogenic and epidemiological research in malignant tumor, surveillance and immunization.
期刊最新文献
[A retrospective cohort study based on propensity score matching evaluated the effect of bronchoalveolar lavage on the clinical prognosis of children with macrolide drug-resistant Mycoplasma pneumoniae pneumonia]. [Analysis of antinuclear antibody in 9 528 pregnant women during early pregnancy in a hospital in Qingdao City]. [Construction and application of a staged early warning model for dengue fever]. [Analysis of clinical characteristics and risk factors for recurrence of combined EB virus infection in patients with inflammatory bowel disease treated with biological agents]. [Analysis of temporal trends of the incidence rate of tuberculosis in Shaanxi Province].
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1