603 Remission and low disease activity are associated with lower health care costs in an international inception cohort of patients with systemic lupus erythematosus
Notice bibliographique
Résumé
Background/Purpose Remission and low disease activity (LDA) are associated with decreased flares, damage, and mortality. However, little is known about the impact of disease activity states (DAS) on health care costs. We determined the independent impact of different definitions of remission and LDA on direct and indirect costs (DC, IC) in a multicentre, multi- ethnic inception cohort. Methods Patients fulfilling revised ACR classification criteria for SLE from 33 centres in 11 countries were enrolled within 15 months of diagnosis and assessed annually. Patients with ≥2 annual assessments were included. Five mutually independent DAS were defined: 1) Remission off-treatment: clinical (c) SLEDAI-2K=0, without prednisone or immunosuppressants 2) Remission on-treatment: cSLEDAI-2K=0, prednisone ≤5mg/d and/or maintenance immunosuppressants 3) LDA-Toronto Cohort (TC): cSLEDAI-2K≤2, without prednisone or immunosuppressants 4) Modified Lupus LDA State (mLLDAS): SLEDAI-2K≤4, no activity in major organs/systems, no new disease activity, prednisone ≤7.5mg/d and/or maintenance immunosuppressants 5) Active: all remaining assessments Antimalarials were permitted in all DAS. At each assessment, patients were stratified into 1 DAS; if >1 definition was fulfilled per assessment, the patient was stratified into the most stringent. The proportion of time patients were in a specific DAS at each assessment since cohort entry was determined. At each assessment, annual DC and IC were based on health resource use and lost workforce/non-workforce productivity over the preceding year. Resource use was costed using 2021 Canadian prices and lost productivity using Statistics Canada age-and-sex-matched wages. To examine the association between the proportion of time in a specific DAS at each assessment since cohort entry and annual DC and IC, multivariable random-effects linear regression modelling was used. Potential covariates included age at diagnosis, disease duration, sex, race/ethnicity, education, region, smoking, and alcohol use. Results 1631 patients (88.7% female, 48.9% White, mean age at diagnosis 34.5) were followed for a mean of 7.7 (SD 4.7) years (table 1, Panel A). Across 12,281 assessments, 49.3% were classified as active (table 1, Panel B). Patients spending <25% vs 75-100% of their time since cohort entry in an active DAS had lower annual DC and IC (DC $4042 vs $9101, difference - $5060, 95%CI -$5983, -$4136; IC $21,922 vs $32,049, difference -$10,127, 95% -$16,754, - $3499) (table 2, Panel B&C). In multivariable models, remission and LDA (per 25% increase in time spent in specified DAS vs active) were associated with lower annual DC and IC: remission off-treatment (DC -$1296, 95%CI -$1800, -$792; IC -$3353, 95%CI -$5382, -$1323), remission on-treatment (DC -$987, 95%CI -$1550, -$424; IC -$3508, 95%CI -$5761, -$1256), LDA-TC (DC -$1037, 95%CI -$1853, -$222; IC -$3229, 95%CI -$5681, -$778) and mLLDAS (DC -$1307, 95%CI -$2194, -$420; IC - $3822, 95%CI -$6309, $-1334) (table 3, Model B). There were no differences in costs between remission and LDA. Conclusions Remission and LDA are associated with lower costs, likely mediated through the known association of these DAS with more favourable clinical outcomes.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».