TY - JOUR
T1 - Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023
AU - Elson, William H.
AU - Jamie, Gavin
AU - Wimalaratna, Rashmi
AU - Forbes, Anna
AU - Leston, Meredith
AU - Okusi, Cecilia
AU - Byford, Rachel
AU - Agrawal, Utkarsh
AU - Todkill, Dan
AU - Elliot, Alex J.
AU - Watson, Conall
AU - Zambon, Maria
AU - Morbey, Roger
AU - Lopez Bernal, Jamie L.
AU - Hobbs, F. D.Richard
AU - de Lusignan, Simon
N1 - Publisher Copyright:
copyright of the authors or their affiliated institutions, 2024.
PY - 2024/8/29
Y1 - 2024/8/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85203077768&partnerID=8YFLogxK
U2 - 10.2807/1560-7917.ES.2024.29.35.2300682
DO - 10.2807/1560-7917.ES.2024.29.35.2300682
M3 - Article
C2 - 39212059
AN - SCOPUS:85203077768
SN - 1025-496X
VL - 29
JO - Eurosurveillance
JF - Eurosurveillance
IS - 35
ER -