REAL-WORLD APPLICATION OF THE SLE RISK PROBABILITY INDEX TO TRIAGE ANA POSITIVE PATIENTS: A PRAGMATIC RETROSPECTIVE SINGLE-CENTER STUDY
Notice bibliographique
Résumé
PV141 / #368 Poster Topic: AS17 - Miscellaneous Background/Purpose Antinuclear antibodies (ANA) testing is crucial for identifying patients with systemic lupus erythematosus (SLE). However, its specificity is suboptimal and may result in unnecessary referrals to Rheumatology. Triage tools that enable early differentiation between SLE and non-SLE among ANA-positive patients could expedite initial assessment and improve patient outcomes,[1] making them particularly valuable in lupus referral centers. The SLE Risk Probability Index (SLERPI) is a simple-to-use, machine-based model utilizing 8 clinical and 6 laboratory variables, designed to assist SLE diagnosis, with potential clinical utility for this purpose.[2] Methods We retrospectively reviewed consecutive referrals that were received between January 1st and October 31st, 2024 to rule out SLE. Referrals for patients younger than 16 years old or without documented positive ANA (1:80 or greater by immunofluorescence) or with a prior diagnosis of connective tissue disease were excluded. A single reviewer analyzed each consultation request from the referring clinicians and scored SLERPI items individually, using only the information included in the referral. Based on this scoring, the total SLERPI score was classified as positive or negative, using a cut-off of >7 points, as previously described. Patients were classified as either non-SLE or diagnosed with SLE based on clinical documentation from their charts. Additional collected data included initial triage priority (Urgent, Semi-Urgent, Routine) assigned by the referral center, and a retrospective triage reassessment by the principal reviewer. Descriptive statistics were used. Results Fifty referrals meeting the selection criteria were identified. Of these, 44 were from general practitioners and 6 from specialists, with 90% showing positive ANA by immunofluorescence at titers ≥1/160. Initial triage classified 20% of cases as Semi-Urgent and 80% as Routine. On average, 8.7 out of 14 SLERPI items were undocumented by the referring provider. Only 4 items—Arthritis, Platelet levels, Leukocyte levels, and Proteinuria—were documented in ≥50% of referrals, while 8 out of 14 items were documented in ≤20% of referrals. Among the 50 charts reviewed, 5 had a positive SLERPI score (>7), while 45 were negative. Four patients were diagnosed with SLE, and 46 were deemed non-SLE. The preliminary analysis demonstrated a positive predictive value of 60% for a SLERPI score >7 to identify SLE patients, with a high negative predictive value of 98%, for an 8% SLE prevalence (Figure 1). SLERPI score >7 showed a positive likelihood ratio of 17.3 and a negative likelihood ratio of 0.26 for SLE identification. A SLERPI score >7 would have reclassified 6 non-SLE patients as Routine and prioritized 1 SLE case as Semi Urgent (Figure 2). The SLERPI score’s negative likelihood ratio was 0.13 for identifying patients ultimately classified as Routine, including those initially marked as Routine who remained so after review and those reclassified from Semi-Urgent to Routine. Figure 1. Figure 2. Conclusions SLERPI shows potential as an effective triage tool for distinguishing SLE from non-SLE among ANA-positive patients. With a high negative predictive value of 98% for SLE diagnosis and a negative likelihood ratio of 0.13, the SLERPI score could help prioritize referrals. This may, in turn, accelerate early rheumatology assessment in newly diagnosed SLE patients, thus improving outcomes. This approach might also allow for more efficient resource allocation in lupus referral centers. References: [1.] Adamichou C. Ann Rheum Dis 2021;80(6):758-66. [2.] Floris A. Arthritis Care Res 2020;72:1794-9.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».