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Enregistrement W2970149320 · doi:10.1373/jalm.2018.028399

Professional Insights from a Pioneer in Autoimmune Disease Testing: The Future of Antinuclear/Anticellular Antibody Testing

2019· article· en· W2970149320 sur OpenAlexaff
Marvin J. Fritzler

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueSystemic Lupus Erythematosus Research
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésAnti-nuclear antibodyAutoantibodyIIfImmunologyCounterimmunoelectrophoresisMedicineAntibodySystemic lupus erythematosusDiseaseInternal medicine

Résumé

récupéré en direct d'OpenAlex

Almost half a century has lapsed since I embarked on a career-long study of systemic autoimmune rheumatic diseases (SARDs)2 with a focus on their autoantibodies directed against an astounding spectrum of cellular antigens (1). The discovery of the lupus erythematosus cell and the development of the lupus erythematosus cell test serves as a historic reference point for the study of antinuclear antibodies (ANAs), or what today international consensus advocated should more correctly be referred to as anticellular antibodies (ACAs) (2, 3). Paralleling the explosion of the spectrum of ACAs was a remarkable transition in the technologies used to detect autoantibodies (1). Although some of the “octogenarian” assays such as double immunodiffusion, hemagglutination, complement fixation, and counterimmunoelectrophoresis are fading into oblivion, the ACA indirect immunofluorescence (IIF) test is increasingly used as a screening test and entry criterion for SARD. However, the emergence of newer multianalyte array technologies that have higher throughput, sensitivity, and specificity and detect a broader range of autoantibodies in comparatively miniscule serum samples may eventually replace the ACA IIF test (4, 5). ACA testing was once regarded the primary domain of rheumatologists and clinical immunologists, but with a growing spectrum of autoimmune diseases linked to ACA and other biomarkers, the spectrum of clinicians using these tests has also remarkably widened (1). More than 180 target autoantibodies have been described in systemic lupus erythematosus and >30 in systemic sclerosis. Nevertheless, fewer than 15 ACA in each of these diseases are used in routine diagnostic assays. Some of these ACA have perished in the “death valley” of translation because they fail to meet criteria for clinical applications (4). The continuously expanding spectrum of ACAs in SARD might be considered by some as unnecessary, but, for one thing, these efforts are closing the “seronegative gap” (6). In addition, some of these “orphan” ACA may be rediscovered and find an important niche when artificial intelligence is applied to diagnostics in the future (6). As witnessed by recommendations endorsed by the American College of Rheumatology and the Canadian Rheumatology Association in the Choosing Wisely paradigm, and “consensus” statements of the American College of Physicians and the College of American Pathologists, ACA testing has been under considerable review and criticism (reviewed in 7, 8). Most recently, the Effective Health Care Program characterized the evidence about ACA testing as “broad clinical consensus that ANA testing (including ANA subserologies) should not be used to screen for SARDs in primary care”; therefore, “there is no clinical uncertainty that a new systematic review could potentially address” (8). In other words, despite evidence to the contrary (reviewed in 7), ACA testing should be curtailed (particularly in primary care) because of its “poor positive and negative predictive values (PPV, 29%; NPV, 77%), leading to increased healthcare costs with unclear clinical benefit.” With these issues in mind, my personal perspectives on the future of ACA testing is summarized in 4 questions. First, what should we do with evidence that ACA and related biomarkers antedate the diagnosis of SARD by up to 20 years? Unfortunately, the proclamations of Choosing Wisely and Effective Health Care arise from a rather myopic perspective that ANA testing should be done on only high-PPV/low-NPV SARD patients. Second, the circular logic is difficult to rationalize because, if the patient has high-PPV SARD, why should the ACA test be done at all? An important aspect that seems to be overlooked is that when the “intent to treat” approach to ACA testing is restricted to high-PPV SARD, the diagnosis of SARDs is often delayed so that a considerable proportion of patients have active disease and end organ damage at the time of diagnosis (reviewed in 9). This delay in diagnosis is associated with remarkably high healthcare costs because renal disease, pulmonary fibrosis and hypertension, and irreversible joint damage, to name a few, require much more intensive and expensive care attended by decreased health-related quality of life. These observations are prompting many clinicians to reconsider the approach to SARDs by making concerted efforts to make a much earlier diagnosis (6). It needs to be appreciated that an earlier diagnosis is the domain of primary healthcare providers who serve as the SARD “case finders” (7). Screening tests such as ACA for SARD are used as part of “case finding,” and then, on the basis of clinical acumen, patients are referred to subspecialists for evaluation and appropriate management. Third, if primary care physicians aren't the early SARD case finders in the real world where there is a severe shortage of tertiary care specialists, who is? And fourth, given the documented and perceived limitations of the ANA/ACA IIF test as a screen for SARD (10), what should replace it? Perceived abuse of ANA/ACA testing is leading to revised laboratory approaches to screen for SARDs. ANA/ACA testing needs to move beyond the paradigm of confirming a diagnosis in high-pretest probability patients and “intent to treat” to “case finding” of very early autoinflammatory disease in which the clinical paradigm is “intent to prevent” morbidity and thereby decrease healthcare costs. Practical and realistic considerations indicate a continuing key role for primary care health providers as “case finders” who then refer patients for further investigation and treatment. There are advantages and trends indicating that ANA/ACA IIF as a screening testing will be replaced by high-throughput multianalyte array technologies. As an abbreviated reply to the last question, modern laboratories are migrating to technology platforms that have higher throughput with faster turnaround times. While newer ANA/ACA IIF assay platforms have also moved in this direction (1), it will be a challenge to meet the performance and clinical value of newer multi-analyte array technologies (MAAT) (4). Recent evidence indicates that the best approach for ACA testing is to combine ACA by IIF with MAAT (1). In conclusion, there is a strong need for unbiased approaches to ACA testing, based on most recent evidence. The laboratory approaches of the future need to consider the importance of disease prevention fostered by “case finding” and attenuation of significant morbidity and healthcare expenditures. As MAAT improve and decrease in price, it is likely that ACA IIF will no longer be the SARD screening assay of choice. systemic autoimmune rheumatic diseases antinuclear antibodies anticellular antibodies indirect immunofluorescence positive predictive value negative predictive value. The author's career and any measure of success would not have been made possible without the tremendous mentorship of Dr. Eng Tan (Emeritus: The Scripps Research Institute: see: Fritzler MJ, Chan EKL. Dr Eng M. Tan: a tribute to an enduring legacy in autoimmunity. Lupus 2017;26:208–217.). In addition, the author has had outstanding collaborators from around the world and the benefits of extremely brilliant graduate students and postdoctoral fellows. The author apologizes to colleagues whose works are not cited in this short perspective owing to publication limits.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,032
score de la tête « metaresearch » (Gemma)0,054
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,032
Score d'incertitude au seuil0,169

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0320,054
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0050,002
Études des sciences et des technologies0,0020,016
Communication savante0,0100,018
Science ouverte0,0030,005
Intégrité de la recherche0,0120,031
Charge utile insuffisante (le modèle a refusé de juger)0,0090,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,287
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2019
Routes d'admission1
Résumé présentoui

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