Impact of age on NIS2+™ and other non-invasive blood tests for the evaluation of liver disease and detection of at-risk MASH
Notice bibliographique
Résumé
Background & Aims: other NITs. Methods: An analysis cohort (N = 1,926) with biopsy-proven MASLD was selected among individuals screened for the phase III RESOLVE-IT clinical trial and divided into ≤45, 46-55, 56-64, and ≥65 years groups. To avoid potential confounding effects, a well-balanced cohort (n = 708; n = 177/age group) was obtained by applying a propensity score-matching algorithm to the analysis cohort. Baseline values of biomarkers and NITs were compared across age groups using one-way ANOVA, and the impact of age and histology were compared through three-way ANOVA. The impact of age on NIT performance for the detection of at-risk metabolic dysfunction-associated steatohepatitis (MASH; MASLD activity score [MAS] ≥4 and fibrosis stage [F] ≥2) was also evaluated. Results: <0.0001) impacted those of NFS (NAFLD fibrosis score), FIB-4 (Fibrosis-4 index), and Enhanced Liver Fibrosis (ELF™) score. NIS2+™ was the only NIT on which fibrosis and MAS exerted a moderate to large effect. While the impact of fibrosis on APRI was moderate, that of MAS was low. The impact of age on FIB-4 and NFS was larger than that of fibrosis. NIS2+™ exhibited the highest AUROC values for detecting at-risk MASH across age groups, with stable performances irrespective of cut-offs. Conclusions: NIS2+™ was not significantly impacted by age and was sensitive to both fibrosis and MAS grade, demonstrating a robust performance to rule in/out at-risk MASH with fixed cut-offs. Impact and Implications: While metabolic dysfunction-associated steatotic liver disease (MASLD) can affect individuals of all ages, patient age could represent an important confounding factor when interpreting non-invasive test (NIT) results, highlighting the need for reliable and efficient NITs that are not impacted by age and that could be interpreted with fixed cut-offs, irrespective of patient age. We report the impact of age on different well-established NITs - among those tested, only two panels, NIS2+™ and APRI, were not impacted by age and can be used and interpreted independently of patient age. NIS2+™ was also sensitive to both fibrosis and MAS, further confirming its efficiency for the detection of the composite endpoint of at-risk MASH and its potential as a valuable candidate for large-scale implementation in clinical practice and clinical trials.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».