Suppr超能文献

一项关于预测基层医疗中儿童下呼吸道感染住院情况的临床预测规则的系统评价及其在新队列中的验证

A Systematic Review of Clinical Prediction Rules to Predict Hospitalisation in Children with Lower Respiratory Infection in Primary Care and their Validation in a New Cohort.

作者信息

Wildes Dermot M, Chisale Master, Drew Richard J, Harrington Peter, Watson Chris J, Ledwidge Mark T, Gallagher Joe

机构信息

gHealth Research Group, UCD Conway Institute, School of Medicine, University College Dublin, Ireland.

Biological Science Department, Faculty of Science, Technology & Innovations, Mzuzu University, Malawi.

出版信息

EClinicalMedicine. 2021 Oct 18;41:101164. doi: 10.1016/j.eclinm.2021.101164. eCollection 2021 Nov.

Abstract

Our goal was to identify existing clinical prediction rules for predicting hospitalisation due to lower respiratory tract infection (LRTI) in children in primary care, guiding antibiotic therapy. A validation of these rules was then performed in a novel cohort of children presenting to primary care in Malawi with World Health Organisation clinically defined pneumonia. MEDLINE & EMBASE databases were searched for studies on the development, validation and clinical impact of clinical prediction models for hospitalisation in children with lower respiratory tract infection between January 11946-June 30 2021. Two reviewers screened all abstracts and titles independently. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews & Meta-Analyses guidelines. The BIOTOPE cohort (BIOmarkers TO diagnose PnEumonia) recruited children aged 2-59 months with WHO-defined pneumonia from two primary care facilities in Mzuzu, Malawi. Validation of identified rules was undertaken in this cohort. 1023 abstracts were identified. Following the removal of duplicates, a review of 989 abstracts was conducted leading to the identification of one eligible model. The CHARMS checklist for prediction modelling studies was utilized for evaluation. The area under the curve (AUC) of the STARWAVe rule for hospitalisation in BIOTOPE was found to be 0.80 (95% C.I of 0.75-0.85). The AUC of STARWAVe for a confirmed diagnosis of bacterial pneumonia was 0.39 (95% C.I 0.25-0.54). This review highlights the lack of clinical prediction rules in this area. The STARWAVe rule identified was useful in predicting hospitalisation from bacterial infection as defined. However, in the absence of a gold standard indicator for bacterial LRTI, this is a reasonable surrogate and could lead to reductions in antibiotic prescription rates, should clinical impact studies prove its utility. Further work to determine the clinical impact of STARWAVe and to identify diagnostic tests for bacterial LRTI in primary care is required.

摘要

我们的目标是确定用于预测初级保健中儿童因下呼吸道感染(LRTI)而住院的现有临床预测规则,以指导抗生素治疗。然后,在马拉维一群因世界卫生组织临床定义的肺炎而就诊于初级保健机构的新儿童队列中,对这些规则进行了验证。检索MEDLINE和EMBASE数据库,查找1946年1月至2021年6月30日期间关于儿童下呼吸道感染住院临床预测模型的开发、验证及临床影响的研究。两名评审员独立筛选所有摘要和标题。该研究按照系统评价与Meta分析的首选报告项目指南进行。BIOTOPE队列(用于诊断肺炎的生物标志物)从马拉维姆祖祖的两个初级保健机构招募了2至59个月大、患有世界卫生组织定义肺炎的儿童。在该队列中对确定的规则进行验证。共识别出1023篇摘要。去除重复项后,对989篇摘要进行了审查,最终确定了一个符合条件的模型。使用预测建模研究的CHARMS清单进行评估。发现BIOTOPE队列中用于住院预测的STARWAVe规则的曲线下面积(AUC)为0.80(95%置信区间为0.75 - 0.85)。STARWAVe用于确诊细菌性肺炎的AUC为0.39(95%置信区间为0.25 - 0.54)。本综述突出了该领域临床预测规则的缺乏。所确定的STARWAVe规则在预测所定义的细菌感染导致的住院方面很有用。然而,由于缺乏细菌性下呼吸道感染的金标准指标,这是一个合理的替代指标,并且如果临床影响研究证明其效用,可能会降低抗生素处方率。需要进一步开展工作以确定STARWAVe的临床影响,并确定初级保健中细菌性下呼吸道感染的诊断测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9f9/8529204/640f6b6eb458/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验