AB1201 DEVELOPMENT OF A MEASURING APP FOR FINGER LESIONS AS AN OUTCOME MEASURE FOR SYSTEMIC SCLEROSIS-RELATED DIGITAL ULCERATION
Authors: A. Davison, A. Krishan, R. P. New, A. Murray, G. Dinsdale, F. Hall, J. D. Pauling, A. Vail, W. Dixon, M. Dickinson, C. Taylor, A. L. Herrick
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Painful, disabling digital ulcers develop in approximately 50% of patients with systemic sclerosis (SSc). Current treatments are often only partly effective (if at all). High inter-observer variability in defining and measuring 'ulcers' (referred to here as 'lesions') is a major problem because in trials of treatment, the primary endpoint is usually the number of new lesions or time to healing. Mobile phone monitoring of digital lesions could overcome this difficulty. <h3>Objectives:</h3> Our overall aim was to test the hypothesis that photographs and self-reported data collected daily using a smartphone app designed specifically for digital lesions could provide a reliable and objective outcome measure, sensitive to change, for use in clinical trials. Specific objectives were to: a.Develop a smartphone app to guide patients through the image (photograph) capture process and also capture patient reported outcome measures (PROMS), then test the app in patients with SSc and current digital lesions. b.Develop methods to extract and analyse lesion area data from the photographs, to track healing status reliably and completely automatically, comparing manual to automated measurement. <h3>Methods:</h3> The app was co-designed with a patient user group, and in addition to collecting images, captured PROMS including daily assessment of pain (Pain-VAS, scale 0-10) and, at study entry and end, the Hand Disability in Systemic Sclerosis-Digital Ulcers (HDISS-DU) questionnaire. Patients with SSc photographed their lesion(s) each day for 30 days and uploaded images to a secure repository. Lesions were analysed both manually (a single observer drew around the lesion) and automatically, using a machine learning approach using segmentation. A regression model was fitted to manual and automated data (for each lesion) to calculate the average gradient of area. Patients rated feasibility of using the app via a post-study questionnaire. <h3>Results:</h3> 25 patients (mean age 57.6 years, 22 female, mean disease duration 19.0 years) consented of whom 19 completed the study, with evaluable data from 27 lesions. Mean (Standard deviation [SD]) baseline Pain-VAS was 5.7 (2.4) and HDISS-DU 2.2 (0.9), indicating high lesion and disease-related morbidity. PROMs data are shown in Table 1. 602 images were returned of which 506 (84%) were used in the analysis (mean number of used images per lesion 18.7, SD 8.3). In 86 of these 506 images the machine learning model was unable to predict that a lesion existed. Mean (SD) manual and automated lesion areas at Day 1 were 11.6 (16.0) and 13.9 (16.7) mm<sup>2</sup> respectively. Lesion area decreased over the 30-day study period: manual area by 0.08mm<sup>2</sup> per day (2.4mm<sup>2</sup> over 30 days) and automated area by 0.1mm<sup>2</sup> per day (3.0mm<sup>2</sup> over 30 days). The correlation between the average gradients of manual and automated measurements over 30 days was 0.81. The Bland-Altman plot (Figure 1, each colour represents a different lesion) suggests that the manual measurements were on average 40% lower than automated measurements, with wide limits of agreement. Using the app was feasible as rated by patients: median (range) 'overall experience' 2.5 (1-10: 1=very easy, 10=very difficult). <h3>Conclusion:</h3> 1.Using the app was feasible even in patients with significant hand disability. 2.Although manual and automated measures correlated, limits of agreement were wide. There was stronger agreement between estimated gradients, suggesting internal consistency of the automated measurement that could make it valuable as an outcome measurement for most lesions. 3.ome lesions were challenging to measure because of different lighting conditions and angles of view, and lesion position (e.g. at the nailbed). Further work is required to allow the app to highlight to users when lighting condition or angle of view differs from previous images.REFERENCES: NIL. <h3>Acknowledgements:</h3> This work was funded by Versus Arthritis. <h3>Disclosure of Interests:</h3> Adrian Davison: None declared, Ashma Krishan: None declared, Robert Paul New: None declared, Andrea Murray Arena, Gesynta Pharma, Graham Dinsdale: None declared, Frances Hall Boehringer-Ingelheim, John D Pauling Astra Zeneca, Boehringer Ingelheim, Janssen, Permeatus Inc, Sojournix Pharma and IsoMab, Andy Vail: None declared, William Dixon Google, Mark Dickinson: None declared, Chris Taylor: None declared, Ariane L. Herrick Janssen, Arena, Boehringer-Ingelheim, Camurus, CSL Behring, Galderma, Gesynta.