Background: Although the diagnostic and prognostic value of capillaroscopy is indisputable, it is still a technique with great heterogeneity both in its performance and interpretation. With regard to its performance, several studies support the exhaustive analysis of 8 mm as a sufficient subset to obtain a valid diagnosis, but in clinical practice we find very different fields within the same capillaroscopy. New tools, such as artificial intelligence, make it possible to perform a systematic analysis of the entire nail bed very quickly, providing us with more information.
Objectives: This study aims to determine the impact of the extent of analysed millimeters on the accuracy of identifying capillaroscopic patterns.
Methods: One thousand five hundred and nine new capillaroscopies with 32 images (4 images of different fields per finger) of patients evaluated for Raynaud’s phenomenon with an analysis of the entire nail bed were analysed. An automated quantitative analysis of the images was performed through Capillary.io® and the CAPI-SCORE algorithm was used to obtain a gold standard. Subsequently, the central fields were selected together (2 images per finger) as well as each one separately (1 image per finger) repeating the same system and using the same algorithm to obtain a pattern. Finally, a central area of 8 mm was selected by capillaroscopy by performing the analysis discussed above. The findings obtained between the analysis of the complete nail bed and the rest were compared.
Results: Of all cases, 38% were identified as nonspecific pattern, 32% as normal pattern, 16% as early, 9.5% as active and 4.5% as late with an average of 59 mm analysed per capillaroscopy.
When analysing 16 images per finger (30.6 average mm) a total agreement of 85% was obtained (1281 capillaroscopies). In 44 (3%) cases the pattern changed from non-scleroderma to scleroderma and in 29 capillaroscopies (2%) the pattern changed in the opposite direction. When analysing 8 images (15.64 average mm), the percentage of agreement dropped to 74.69% with 4.3% (65) changing from non-scleroderma to scleroderma pattern and 3.2% (48) changing the other way around. When 8 mm area was analysed the percentage of agreement decreased to 61% with a change to a scleroderma pattern in 7.6% (117) of cases and to a non-scleroderma pattern in 5% (76).
Conclusion: In conclusion, the extent of the analysed millimeters in nailfold capillaroscopy images significantly influences the accuracy of capillaroscopic pattern identification. This suggests a potential reduction in diagnostic accuracy with a decreased field of analysis. Moreover, failing to include important findings in some fields due to a reduced capillaroscopic analysis will often lead to the wrong conclusion of the capillaroscopy belonging to an SSc pattern or not.
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[2] Cutolo M, Melsens K, Herrick AL, Foeldvari I, Deschepper E, De Keyser F, et al; EULAR Study Group on Microcirculation in Rheumatic Diseases. Reliability of simple capillaroscopic definitions in describing capillary morphology in rheumatic diseases. Rheumatology (Oxford) 2018;57:757-9.
[3] Smith V, Ickinger C, Hysa E, Snow M, Frech T, Sulli A, et al. Nailfold capillaroscopy. Best Pract Res Clin Rheumatol 2023.5:101849.
[4] Gracia Tello BC, Ramos Ibañez E, Saez Comet L, Guillén Del Castillo A, Simeón Aznar CP, Selva-O’Callaghan A, et al. External clinical validation of automated software to identify structural abnormalities and microhaemorrhages in nailfold videocapillaroscopy images. Clin Exp Rheumatol 2023;41:1605-1611.
Acknowledgements: Spanish Society of Internal Medicine (SEMI), Spanish Multidisciplinary Society of Systemic Autoimmune Diseases (SEMAIS)
Disclosure of Interests: None declared.