Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023

William H. Elson*, Gavin Jamie, Rashmi Wimalaratna, Anna Forbes, Meredith Leston, Cecilia Okusi, Rachel Byford, Utkarsh Agrawal, Dan Todkill, Alex J. Elliot, Conall Watson, Maria Zambon, Roger Morbey, Jamie L. Lopez Bernal, F. D.Richard Hobbs, Simon de Lusignan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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, noninfectious 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.

Original languageEnglish
JournalEurosurveillance
Volume29
Issue number35
DOIs
Publication statusPublished - 29 Aug 2024

Bibliographical note

Publisher Copyright:
copyright of the authors or their affiliated institutions, 2024.

Fingerprint

Dive into the research topics of 'Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023'. Together they form a unique fingerprint.

Cite this