A251 NON-ALCOHOLIC FATTY LIVER DISEASE AT A CANADIAN TERTIARY CARE CENTRE: RISK FACTORS AND SEVERITY OF DISEASE
Notice bibliographique
Résumé
Abstract Background Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disease worldwide with an increasing prevalence of 25-40%. Although the prevalence increases to 57.5-74% in obese patients, lean individuals also develop NAFLD. Patients are commonly asymptomatic and the diagnosis is often incidental or when progressed to cirrhosis. Once fibrosis has developed, the risk of cardiovascular and liver-related death increases exponentially. The increasing prevalence of NAFLD presents significant healthcare and economic consequences. The severity of NAFLD and its risk factors have been studied in various countries, which guide decisions on screening and management. Similar studies have not been performed in Canada. Purpose: To determine the severity of NAFLD in a tertiary care centre and associated risk factors. Method Retrospective review of patients with NAFLD diagnosed on ultrasound, or fibroscan from January 1, 2019 to December 31, 2021. Patients were 18 years or older and were excluded if they had co-existent liver disease, significant alcohol use or CAP <238. CAP and fibrosis scores were determined using transient elastography. Chi-square and multivariate analysis were performed for statistical analysis. Result(s): A total of 583 patients were included in the study; 312 (53.5%) were male and the mean age was 54.04 years. The majority of cases, 317 (56.8%), were diagnosed by ultrasound or CT scan and only 30 (5%) patients had a known family history of NAFLD. Lean-NAFLD (L-NAFLD) was present in 83 (15.2%) patients, overweight NAFLD (OW-NAFLD) 220 (40.2%), obese-NAFLD (OB-NAFLD) 206 (37.7%) and morbidly obese-NAFLD (MB-NAFLD) 38 (6.9%). The prevalence of T2DM was 28.6%, dyslipidemia 37.4%, hypertension 35.5%, coronary artery disease 6.2% and obstructive sleep apnea 6.7%. Risk factors for Stage 3 steatosis (CAP>290) included BMI>30 (2.84) and type 2 diabetes (OR 2.45), but not dyslipidemia, hypertension, age or gender. Type 2 diabetes, dyslipidemia, hypertension, BMI, gender and age were not significantly predictive of moderate steatosis (CAP 260-289). The proportion of patients with transient elastography scores of F2, F3, and F4 were 16.0%, 7.7%, and 11.0%, respectively. F2 and F3 scores were associated with T2DM (OR 1.94), BMI > 30 (OR 1.83, p<0.05) and age >60 (OR 1.90, p<0.05), but not dyslipidemia or hypertension. F4 scores were significantly associated with age > 60 (OR 1.89), T2DM (OR 2.60), dyslipidemia (OR 1.79), hypertension (OR 1.86) and BMI>30 (OR 2.54). Conclusion(s) Diabetes and BMI were associated with severe steatosis and fibrosis. Dyslipidemia and hypertension were only associated with advanced fibrosis. Our study demonstrates challenges in identification of early NAFLD given that metabolic syndrome factors were not associated with mild to moderate steatosis. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».