Objectives: This study was designed to propose a simple "Fast Track algorithm" for capillaroscopists of any level of experience to differentiate "scleroderma patterns" from "non-scleroderma patterns" on capillaroscopy and to assess its inter-rater reliability. Methods: Based on existing definitions to categorise capillaroscopic images as "scleroderma patterns" and taking into account the real life variability of capillaroscopic images described standardly according to the European League Against Rheumatism (EULAR) Study Group on Microcirculation in Rheumatic Diseases, a fast track decision tree, the "Fast Track algorithm" was created by the principal expert (VS) to facilitate swift categorisation of an image as "non-scleroderma pattern (category 1)" or "scleroderma pattern (category 2)". Mean inter-rater reliability between all raters (experts/attendees) of the 8th EULAR course on capillaroscopy in Rheumatic Diseases (Genoa, 2018) and, as external validation, of the 8th European Scleroderma Trials and Research group (EUSTAR) course on systemic sclerosis (SSc) (Nijmegen, 2019) versus the principal expert, as well as reliability between the rater pairs themselves was assessed by mean Cohen's and Light's kappa coefficients. Results: Mean Cohen's kappa was 1/0.96 (95% CI 0.95-0.98) for the 6 experts/135 attendees of the 8th EULAR capillaroscopy course and 1/0.94 (95% CI 0.92-0.96) for the 3 experts/85 attendees of the 8th EUSTAR SSc course. Light's kappa was 1/0.92 at the 8th EULAR capillaroscopy course, and 1/0.87 at the 8th EUSTAR SSc course. C Conclusion: For the first time, a clinical expert based fast track decision algorithm has been developed to differentiate a "non-scleroderma" from a "scleroderma pattern" on capillaroscopic images, demonstrating excellent reliability when applied by capillaroscopists with varying levels of expertise versus the principal expert and corroborated with external validation.
Fast track algorithm: How to differentiate a "scleroderma pattern" from a "non-scleroderma pattern" / V. Smitha, A. Vanhaecke, L. Herrick Ariane, O. Distler, G. Guerra Miguel, P. Denton Christopher, E. Deschepper, I. Foeldvari, M. Gutierrez, E. Hachulla, F. Ingegnoli, S. Kubo, U. Müller-Ladner, V. Riccieri, A. Sulli, M. van Laar Jaap, C. Vonk Madelon, A. Walker Ulrich, M. Cutolo, M. Allain Wouterlood, P. Akshat, B. Amorosi, S. Arnold, D. Bajo, A. Balbir, A. Baric, F. Baron, S. Barreira, L. Barrio Nogal, J. Bartosinska, A. Bazela-Zadura, R. Bech, A. Begovic, D. Bendjenna, D. Benfaremo, E. Bertoldo, R. Besseling, M. Bevan, K. Bouayed, V. Boyadzhieva, L. Brites, J. Broen, C. Carton, V. Cazac, R. Chetouane-Bennafaa, J. Chodorowski, J. Ciaffi, M. Cirillo, A. Codina, B. Coleiro, I. Condeiro, C. Corrado, F. Crisafulli, N. Damjanov, Y. Damyana, A. Danczak-Pazdrowska, R. De Angelis, M. de Kanter, G. De Luca, M. De Moor, J. de Vries-Bouwstra, F. Del Galdo, A. Depicker, B. Dhamo, B. Dharmanand, C. Dias, M. Dudra-Jastrzebska, E. Emperiale Valentina, N. Eshak, W. Fage Simon, E. Farina, I. Ferdinand, M. Fonseca Diogo, T. Frech, H. Fretheim, O. Gaidarji, N. Galanopoulos, C. Gallo, S. Ganhao Santos, O. Gercik, G. Voerman, K. Gheorghe, S. Giryes, K. Glas, J. Gonçalves Maria, D. Gonzalez Benitez Roberto, K. Gruszecka, H. Guerboukha, K. Gunnarsson, V. Hajdu-Toth Kata, Y. Hellmi Rakhma, I. Hindi, T. Hinze, A. Hoeger, L. Host, A. Hoxha, P. Huang, C. Ickinger, C. Isabelle, S. Ismail, M. Jakubaszek, C. Kedor, B. Kersten, F. Kerstens, P. Keskitalo, A. Khan Khalid, N. Khmelinskii, I. Klein-Wieringa, A. Koulouri, E. Kucharz, S. Kyllönen Minna, M. Lazzaroni, J. Lemmers, M. Leone, A. Lescoat, H. Li Ying, C. Lopes, A. Lopez-Ceron, F. Lötscher, M. Luis, M. Lukinac Ana, K. Ly, B. Lynch, R. Machado Ana, N. Madeira, I. Mahieu, J. Malgorzata-Michalska, E. Martinez Robles, P. Martins, P. Mashru, D. Mazzocchi, Y. Medina, M. Medjadi, K. Melsens, V. Messiniti, B. Miziolek, A. Moiseev, F. Moser, F. Mrsic, A. Neto, T. Nguyen Thanh Hien, T. Nielsen Christoffer, M. Osman, J. Ostrovrsnik, G. Pacini, M. Patanè, M. Pendolino, K. Perdan-Pirkmajar, G. Pettiti, J. Pflugfelder, M. Pham, M. Phaneuf, Y. Piette, C. Pomîrleanu Daniela, M. Pontalti, S. Poriau, R. Prate Ana, A. Predoiu, P. Pretel Ruiz, M. Priora, M. Radic, B. Radovits, M. Raquel, E. Rein Siv, V. Reynaert, G. Rinzis Mirela, K. Romanowska-Prochnicka, V. Romao, L. Ross, G. Rovera, D. Ruiz, B. Russo, I. Rusu, S. Saavedra Gutierrez, N. Saidi, A. Sari, H. Satis, L. Schoneveld, L. Seitz, A. Senoh, A. Santosa, M. Seyed Mardani Seyed, Q. Shah, M. Sikora, D. Silva Filipa, V. Silvestri, T. Simopolou, R. Singh, M. Smits, M. Snow, S. Soldano, L. Soto, M. Soyfoo Shahnawaz, J. Spierings, A. Steelandt, W. Stevens, N. Stoilov, G. Strugariu, M. Suitner, M. Supe, D. Suput Skvarca, W. Suryo Anggoro Kusumo, B. Sy Kane, A. Tarasova, S. Tardito, Y. Tavor, C. Tenazinha, A. Thoma, M. Tjeuw, A. Trombetta, A. Valido, F. van den Hoogen, N. Van Herwaarden, A. Van Meerendonck, E. Van Spil, M. Vanden Bulcke, E. Verduci, L. Verniers, N. Vivar, K. Voigt, I. Vos, K. Wiefel, A. Wojteczek, R. Yokochi, G. Zampogna. - In: AUTOIMMUNITY REVIEWS. - ISSN 1873-0183. - 18:11(2019 Nov). [10.1016/j.autrev.2019.102394]
Fast track algorithm: How to differentiate a "scleroderma pattern" from a "non-scleroderma pattern"
F. Ingegnoli;D. Mazzocchi;
2019
Abstract
Objectives: This study was designed to propose a simple "Fast Track algorithm" for capillaroscopists of any level of experience to differentiate "scleroderma patterns" from "non-scleroderma patterns" on capillaroscopy and to assess its inter-rater reliability. Methods: Based on existing definitions to categorise capillaroscopic images as "scleroderma patterns" and taking into account the real life variability of capillaroscopic images described standardly according to the European League Against Rheumatism (EULAR) Study Group on Microcirculation in Rheumatic Diseases, a fast track decision tree, the "Fast Track algorithm" was created by the principal expert (VS) to facilitate swift categorisation of an image as "non-scleroderma pattern (category 1)" or "scleroderma pattern (category 2)". Mean inter-rater reliability between all raters (experts/attendees) of the 8th EULAR course on capillaroscopy in Rheumatic Diseases (Genoa, 2018) and, as external validation, of the 8th European Scleroderma Trials and Research group (EUSTAR) course on systemic sclerosis (SSc) (Nijmegen, 2019) versus the principal expert, as well as reliability between the rater pairs themselves was assessed by mean Cohen's and Light's kappa coefficients. Results: Mean Cohen's kappa was 1/0.96 (95% CI 0.95-0.98) for the 6 experts/135 attendees of the 8th EULAR capillaroscopy course and 1/0.94 (95% CI 0.92-0.96) for the 3 experts/85 attendees of the 8th EUSTAR SSc course. Light's kappa was 1/0.92 at the 8th EULAR capillaroscopy course, and 1/0.87 at the 8th EUSTAR SSc course. C Conclusion: For the first time, a clinical expert based fast track decision algorithm has been developed to differentiate a "non-scleroderma" from a "scleroderma pattern" on capillaroscopic images, demonstrating excellent reliability when applied by capillaroscopists with varying levels of expertise versus the principal expert and corroborated with external validation.
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