Evaluation of different algorithms to identify the scleroderma pattern in nailfold videocapillaroscopy

Although the role of nailfold videocapillaroscopy (NVC) in the investigation of Raynaud's phenomenon (RP) and systemic sclerosis (SSc) is well established, there is significant heterogeneity in the parameters used to identify the scleroderma pattern. Recently, different algorithms have been proposed for the identification of the scleroderma pattern associated with SSc. This study aimed to explore the accuracy of different capillaroscopic parameters and algorithms (the Fast Track algorithm and the CAPI-score) for identifying the scleroderma pattern in individuals with and without RP and autoimmune rheumatic diseases.

A total of 258 NVCs were analysed. The accuracy and area under the curve (AUC) of qualitative and quantitative NVC parameters were analysed to discriminate between scleroderma and non-scleroderma patterns.

The scleroderma pattern was identified in 101 (39.15%) NVCs. A density of ≤8 capillaries/mm was defined as the optimal cut-off point (AUC 0.911, 95% CI 0.871-0.950), yielding the highest accuracy (87.94%) for identifying the SD pattern versus normal and nonspecific microangiopathy. Cut-off values of ≤3 or ≤6 capillaries/mm resulted in lower sensitivity despite high specificity. The presence of giant capillaries demonstrated high specificity (98.09%) and an accuracy of 85.66%. The accuracy improved when the presence of giant capillaries and ≤8 capillaries/mm or ≤7 capillaries/mm were combined (accuracies of 91.08% and 86.82%, respectively).

The combination of two capillaroscopy parameters (giant capillaries and capillary density) inspired by the Fast Track and CAPI-score, was highly accurate for defining the scleroderma pattern in our cohort.