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Abstract

Introduction

Respiratory sentinel surveillance systems leveraging computerised medical records (CMR) use phenotyping algorithms to identify cases of interest, such as acute respiratory infection (ARI). The Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) is the English primary care-based sentinel surveillance network.

Aim

This study describes and validates the RSC’s new ARI phenotyping algorithm.

Methods

We developed the phenotyping algorithm using a framework aligned with international interoperability standards. We validated our algorithm by comparing ARI events identified during the 2022/23 influenza season in England through use of both old and new algorithms. We compared clinical codes commonly used for recording ARI.

Results

The new algorithm identified an additional 860,039 cases and excluded 52,258, resulting in a net increase of 807,781 cases (33.84%) of ARI compared to the old algorithm, with totals of 3,194,224 cases versus 2,386,443 cases. Of the 860,039 newly identified cases, the majority (63.7%) were due to identification of symptom codes suggestive of an ARI diagnosis not detected by the old algorithm. The 52,258 cases incorrectly identified by the old algorithm were due to inadvertent identification of chronic, recurrent, non-infectious and other non-ARI disease.

Conclusion

We developed a new ARI phenotyping algorithm that more accurately identifies cases of ARI from the CMR. This will benefit public health by providing more accurate surveillance reports to public health authorities. This new algorithm can serve as a blueprint for other CMR-based surveillance systems wishing to develop similar phenotyping algorithms.

© Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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/content/10.2807/1560-7917.ES.2024.29.35.2300682
2024-08-29
2024-11-21
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2024.29.35.2300682
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Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=William+H+Elson&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Elson William H</a>, <a href="/search?value1=Gavin+Jamie&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Jamie Gavin</a>, <a href="/search?value1=Rashmi+Wimalaratna&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wimalaratna Rashmi</a>, <a href="/search?value1=Anna+Forbes&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Forbes Anna</a>, <a href="/search?value1=Meredith+Leston&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Leston Meredith</a>, <a href="/search?value1=Cecilia+Okusi&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Okusi Cecilia</a>, <a href="/search?value1=Rachel+Byford&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Byford Rachel</a>, <a href="/search?value1=Utkarsh+Agrawal&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Agrawal Utkarsh</a>, <a href="/search?value1=Dan+Todkill&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Todkill Dan</a>, <a href="/search?value1=Alex+J+Elliot&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Elliot Alex J</a>, <a href="/search?value1=Conall+Watson&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Watson Conall</a>, <a href="/search?value1=Maria+Zambon&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Zambon Maria</a>, <a href="/search?value1=Roger+Morbey&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Morbey Roger</a>, <a href="/search?value1=Jamie+Lopez+Bernal&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Lopez Bernal Jamie</a>, <a href="/search?value1=FD+Richard+Hobbs&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Hobbs FD Richard</a>, <a href="/search?value1=Simon+de+Lusignan&option1=author&noRedirect=true" class="nonDisambigAuthorLink">de Lusignan Simon</a></span>. Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023. <a href="/content/ecdc">Euro Surveill.</a> 2024;29(35):pii=2300682. <a href="https://doi.org/10.2807/1560-7917.ES.2024.29.35.2300682" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2024.29.35.2300682</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 28 Nov 2023;&nbsp;&nbsp; <span class="generated">Accepted</span>: 22 May 2024 </span> </p>
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