P052 Hydroxychloroquine retinopathy incidence in a UK specialist centre: a machine learning/natural language processing pilot study

Abstract

Abstract Background/Aims Hydroxychloroquine (HCQ) is an antimalarial drug commonly used in autoimmune conditions such as systemic lupus erythematous (SLE) and rheumatoid arthritis (RA). In SLE, it has been shown to significantly improve survival and morbidity, but is known to be rarely associated with sight-threatening retinopathy. Data from the US suggested HCQ retinopathy was significantly more common than previously thought (up to 7%), causing the Royal College of Ophthalmology to recommend increased screening in 2018. Natural language processing (NLP) is a machine learning technique which purports to be able to identify patients with a specific diagnosis or taking specific mediations from unstructured data such as clinic letters. This study aimed to understand whether NLP could be used to (i) identify patients taking HCQ and (ii) determine rates of HCQ retinopathy from clinic letters in a UK tertiary Rheumatology centre. Methods The study was set across the Kellgren Centre for Rheumatology (regional tertiary lupus centre) and the Manchester Eye Hospital. Analysis was performed using CogStack/MedCat, an open source self-supervised NLP framework which extracts SNOMED coded items from unstructured electronic health data such as clinic letters. All clinical documents 2012-2022 were identified for patients attending clinics coded as HCQ retinopathy screening. These were processed through Cogstack/MedCat in two stages. Firstly, it identified all patients exposed to HCQ according to their clinic letters. Subsequently, MedCat searched all clinical documents for those patients for references to HCQ retinopathy, HCQ retinopathy screening or retinopathy. Diabetic retinopathy was used to sense-check for other forms of retinopathy. Finally, manual validation of the HCQ retinopathy positive cases was performed. Results From an original 202,704 documents, HCQ exposure was identified in 3904 unique patients. These patients had 74,377 associated clinical documents, from which the NLP process indicated 2815 referenced HCQ retinopathy or retinopathy alone (including screening). From clinic codes 1299 individual patients (33.3% of the HCQ exposed population) were confirmed to have undergone screening; 1094 (84.2%) were female, mean age 54.8 years at the time of screening. Of those screened, NLP highlighted 568 references to HCQ retinopathy from 507 patients, within which 10 (0.77%) were confirmed to have HCQ retinopathy by manual validation. Conclusion The study has demonstrated that NLP is a promising technique to quickly identify subsets of patients (e.g. those taking HCQ) from large numbers of outpatient clinic letters. Further training of the algorithm may improve its ability to identify rare but important clinical outcomes. In a tertiary SLE centre, roughly one third of patients on HCQ were screened for HCQ retinopathy and 0.77% confirmed HCQ retinopathy. This is much lower than data from the previous studies in the US, but is in keeping with more recent publications in UK data, reflected in recent amendments to screening guidelines. Disclosure Y. Mushtaq: None. G. Tilston: None. K.L. Hyrich: None. A. Barton: None. N. Peek: None. B. Parker: None. D. Griffiths: None. J.H. Humphreys: None.