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Autoimmun Rev. 2019 Nov;18(11):102394. doi: 10.1016/j.autrev.2019.102394. Epub 2019 Sep 11.

Fast track algorithm: How to differentiate a "scleroderma pattern" from a "non-scleroderma pattern".

Author information

1
Department of Internal Medicine, Ghent University, Ghent, Belgium; Department of Rheumatology, Ghent University Hospital, Ghent, Belgium; Unit for Molecular Immunology and Inflammation, VIB Inflammation Research Center (IRC), Ghent, Belgium. Electronic address: vanessa.smith@ugent.be.
2
Department of Internal Medicine, Ghent University, Ghent, Belgium; Department of Rheumatology, Ghent University Hospital, Ghent, Belgium. Electronic address: amber.vanhaecke@ugent.be.
3
Division of Musculoskeletal & Dermatological Sciences, The University of Manchester, Salford Royal NHS Foundation Trust, Manchester, UK; NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. Electronic address: ariane.herrick@manchester.ac.uk.
4
Department of Rheumatology, University Hospital Zurich, Zurich, Switzerland. Electronic address: oliver.distler@usz.ch.
5
Rheumatology Department, Centro Hospitalar Vila Nova de Gaia/Espinho, Vil Nova de Gaia, Portugal. Electronic address: mlgomesg@gmail.com.
6
Department of Rheumatology, University College London, Royal Free Hospital, London, UK. Electronic address: c.denton@medsch.ucl.ac.uk.
7
Biostatistics Unit, Department of Public Health, Ghent University, Ghent, Belgium. Electronic address: ellen.deschepper@ugent.be.
8
Centre for Paediatric and Adolescent Rheumatology, Hamburg, Germany. Electronic address: foeldvari@t-online.de.
9
Division of Musculoskeletal and Rheumatic Disorders, Instituto Nacional de Rehabilitación, Mexico City, Mexico. Electronic address: dr.gmarwin@gmail.com.
10
Univ. Lille, CHU Lille, Département de Médecine Interne et Immunologie Clinique, Centre de Référence des Maladies Systémiques et Auto-Immunes Rares du Nord-Ouest (CERAINO), LIRIC, INSERM, Lille, France. Electronic address: ehachulla2@yahoo.fr.
11
Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy; Division of Rheumatology, ASST G. Pini, Milan, Italy. Electronic address: francesca.ingegnoli@unimi.it.
12
The First Department of Internal Medicine, University of Occupational and Environmental Health, Fukuoka, Japan. Electronic address: kubosato@med.uoeh-u.ac.jp.
13
Department of Rheumatology and Clinical Immunology, Justus-Liebig University of Giessen, Campus Kerckhoff, Bad Nauheim, Germany. Electronic address: u.mueller-ladner@kerckhoff-klinik.de.
14
Department of Internal Medicine and Medical Specialties, Sapienza University of Rome, Italy. Electronic address: valeria.riccieri@uniroma1.it.
15
Research Laboratory and Academic Division of Clinical Rheumatology, Department of Internal Medicine, University of Genoa, IRCCS San Martino Polyclinic Hospital, Genoa, Italy. Electronic address: albertosulli@unige.it.
16
Department of Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, the Netherlands. Electronic address: j.m.vanlaar@umcutrecht.nl.
17
Department of Rheumatology, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: madelon.vonk@radboudumc.nl.
18
Department of Rheumatology, University Hospital Basel, Basel, Switzerland. Electronic address: ulrich.walker@usb.ch.
19
Research Laboratory and Academic Division of Clinical Rheumatology, Department of Internal Medicine, University of Genoa, IRCCS San Martino Polyclinic Hospital, Genoa, Italy. Electronic address: mcutolo@unige.it.

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.

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.

KEYWORDS:

Algorithm; Capillaroscopy; EULAR Study Group on Microcirculation in Rheumatic Diseases; Experts; Novices; Reliability; “Scleroderma patterns”

PMID:
31520797
DOI:
10.1016/j.autrev.2019.102394
[Indexed for MEDLINE]
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