Predictors of Development of Hepatocellular Carcinoma in Non-Cirrhotic Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease/Metabolic Dysfunction-Associated Steatohepatitis - A Retrospective Analysis of the National Inpatient Sample Database
Notice bibliographique
Résumé
Background: One-quarter of the world population is thought to have metabolic dysfunction-associated steatotic liver disease (MASLD). The incidence of metabolic dysfunction-associated steatohepatitis (MASH) and MASLD is rapidly increasing due to the ongoing global epidemic of type 2 diabetes mellitus and obesity. Hepatitis B and C have declined in incidence due to advances in prevention and treatment, yet the overall burden of hepatocellular carcinoma (HCC) continues to rise, largely driven by the growing prevalence of MASLD/MASH. MASLD/MASH is now the fastest-growing etiology of HCC in the USA, France and the UK, with an estimated annual incidence of HCC ranging from 0.5% to 2.6% in patients with MASH cirrhosis. The incidence of HCC among patients with non-cirrhotic MASLD/MASH is lower, approximately 0.1% to 1.3%. There are no screening guidelines currently for HCC in non-cirrhotic MASLD/MASH patients. Our study highlights the dire need to develop HCC predictive strategies and algorithm in this non-cirrhotic population and to move away from a cirrhotic-centered approach but rather use risk-based models. We have identified predictors of development of HCC in this patient population that can be used to develop risk-based HCC screening guidelines and models in a non-cirrhotic population with MASLD/MASH. Methods: -test and were used to establish association between two variables. The significant variables were included in the logistic regression model to identify independent association between variables. Results: From the NIS database, 1,326,230 non-cirrhotic MASLD/MASH patients were identified. The mean age was 53.75 years; 52% were female. Older age (P < 0.0001), female gender (adjusted odds ratio (AOR) = 1.303, P < 0.001), and Asian race (AOR = 1.135, P = 0.01) were associated with increased HCC risk. Anemia, leukopenia, hyponatremia, and hypoalbuminemia were independent predictors (all P < 0.001). Benign liver lesions such as focal nodular hyperplasia (AOR = 1.269) and hemangiomas (1.475), as well as infections like cholangitis (3.093) and liver abscess (2.073), were linked to higher risk. Autoimmune diseases, including rheumatoid arthritis (0.679) and systemic lupus erythematosus (SLE, 0.456), were associated with decreased HCC risk (P < 0.001). Conclusions: This study provides compelling evidence that HCC can develop in non-cirrhotic MASLD/MASH patients. These findings highlight an urgent need to shift from a cirrhosis-centric approach to a more comprehensive, risk-based HCC screening model - especially given that MASLD/MASH is now the most common and fastest-growing etiology of HCC around the globe. This study can potentially help develop those screening guidelines to prevent the development or early detection of HCC in non-cirrhotic MASLD/MASH patients.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| 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 ».