Details of HTA project
Last updated: 1 February 2012 - Next update due: 8 February 2012
Research type: |
Secondary Research (e.g. systematic review) |
Project title: |
Systematic review and validation of clinical prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care |
 Outputs in journals arising from this project
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Project ref: |
07/37/05 |
Cost: |
£125,326 |
Chief Investigator : |
Dr Matthew Thompson, Senior Clinical Scientist and GP, Department of Primary Health Care, University of Oxford |
Start Date: |
March 2009 |
Estimated date of publication in HTA journal series: |
March 2012. This project is at the editorial review stage. Delays in the review process can cause the forecast publication date to be delayed. |
Plain English Summary |
Children who present with infections form a large part of the work of emergency departments, paramedics and urgent care (walk-in) primary care centres. The major challenge facing front-line clinicians working in these settings is deciding whether a child has a serious infection and needs hospital attention, or can be safely sent home and followed up in the community by their GP. If children are incorrectly suspected of having a serious infection, this involves considerable NHS resources such as 999 calls, A&E attendances etc as well as the disruption to the child and their family. Indeed, despite advances in vaccination and overall health of children in the UK, attendances at A&E and urgent access primary care continue to rise. On the other hand, if children who truly have a serious infection are not correctly picked up then their care will be delayed, possibly leading to worsening of their condition and greater risk of dying. We would like to improve the accuracy of the clinical tools that paramedics, nurses and doctors use to indentify children with serious infections. This should mean that children who really need emergency treatment are correctly identified, while those who don't have a serious condition can safely be sent home and followed up in the community. We plan to do this by first identifying all the clinical studies that have already been done in this area. We will then obtain the data from authors who have published reports on this topic that are relevant to the UK system of care, and will attempt to see if the clinical predictors that were identified in one study are also useful in children from another study. The clinical predictors of serious infection that we are most interested in are children's symptoms, examination findings, and simple investigations - these are the clinical tools that clinicians use on a daily basis. Our study allows us to use data collected on thousands of children (from settings relevant to the NHS) without having the expense and difficulty of doing further primary studies. The analysis of this data will allow us to identify the best combination of clinical features, and test how well they perform for front-line clinicians in identifying children who are at most risk of serious infection. |
Project Abstract: |
Acute illness is one of the most common problems encountered in children attending emergency departments as well as by urgent-access primary care services in the UK. Distinguishing children who may have serious infections or complications of infections (e.g. meningitis, bacteraemia, hypoxia from bronchiolitis, dehydration from gastroenteritis) from the vast majority with self-limiting or minor infections who can safely be managed as outpatients or referred to primary care services is a challenging yet vitally important task. We will use a systematic review and meta-analysis of the existing literature on clinical predictors of serious infection in children, which will allow us to identify the clinical features (including history, examination, basic investigations) which offer the best diagnostic value for clinicians in differentiating children with serious from self-limiting infections. We will add to these findings by using data from existing studies (involving approximately 10,000 children from the UK, Netherlands, and Belgium) to perform an individual patient data meta-analysis which will allow us to derive the optimal clinical prediction rules for identifying children with serious infections. We will cross-validate these prediction rules so that we will be able to give clinicians a robust prediction rule that can be adopted in routine NHS settings. |
Project Protocol: |
Project protocol not available |
URL of this page: |
http://www.hta.ac.uk/1751 |
Outputs from this project
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