Defining clinical subtypes of adult asthma using electronic health records: Analysis of a large UK primary care database with external validation. (February 2023)

• Record Type: Journal Article
• Authors: Horne, Elsie M.F.; McLean, Susannah; Alsallakh, Mohammad A.; Davies, Gwyneth A.; Price, David B.; Sheikh, Aziz; Tsanas, Athanasios
• Is Part Of: International journal of medical informatics. Volume 170(2023)
• Publication Date: 2023-02
• Subjects: Asthma -- Electronic health records -- Cluster analysis; EHR Electronic Health Record -- OPCRD Optimum Patient Care Research Database -- SAIL Secure Anonymised Information Linkage -- ICS Inhaled Corticosteroid -- GINA Global Initiative for Asthma -- MCA Multiple correspondence analysis -- BMI Body mass index -- RF Random forest -- t-SNE t-distributed stochastic neighbour embedding -- CCI Charlson Comorbidity Index -- COPD Chronic Obstructive Pulmonary Disease -- SABA Short-Acting Beta-Agonist; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2022.104942
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Six subtypes of asthma identified from electronic health records. Methods included multiple correspondence analysis, k-means and random forests. Subtypes were validated at future timepoints and in an external database. Abstract: Introduction: Asthma is one of the commonest chronic conditions in the world. Subtypes of asthma have been defined, typically from clinical datasets on small, well-characterised subpopulations of asthma patients. We sought to define asthma subtypes from large longitudinal primary care electronic health records (EHRs) using cluster analysis. Methods: In this retrospective cohort study, we extracted asthma subpopulations from the Optimum Patient Care Research Database (OPCRD) to robustly train and test algorithms, and externally validated findings in the Secure Anonymised Information Linkage (SAIL) Databank. In both databases, we identified adults with an asthma diagnosis code recorded in the three years prior to an index date. Train and test datasets were selected from OPCRD using an index date of Jan 1, 2016. Two internal validation datasets were selected from OPCRD using index dates of Jan 1, 2017 and 2018. Three external validation datasets were selected from SAIL using index dates of Jan 1, 2016, 2017 and 2018. Each dataset comprised 50, 000 randomly selected non-overlapping patients. Subtypes were defined by applying multiple correspondence analysis and k-means cluster analysis to the train dataset, and were validated in the internal and external validation datasets. Results: We defined six asthma subtypes with clear clinical interpretability: low inhaled corticosteroid (ICS) use and low healthcare utilisation (30% of patients); low-to-medium ICS use (36%); low-to-medium ICS use and comorbidities (12%); varied ICS use and comorbid chronic obstructive pulmonary disease (4%); high (10%) and very high ICS use (7%). The subtypes were replicated with high accuracy in internal (91–92%) and external (84–86%) datasets. Conclusion: Asthma subtypes derived and validated in large independent EHR databases were primarily defined by level of ICS use, level of healthcare use, and presence of comorbidities. This has important clinical implications towards defining asthma subtypes, facilitating patient stratification, and developing more personalised monitoring and treatment strategies.