Shear wave elastography as a screening modality for hepatic fibrosis in a comprehensive sickle cell disease clinic
Notice bibliographique
Résumé
Abstract Background: Sickle cell disease (SCD) is associated with a spectrum of liver complications, including sickle cell hepatopathy, driven by chronic hemolysis, iron overload, and intrahepatic sickling. These mechanisms predispose patients to hepatic fibrosis and, ultimately, cirrhosis. Despite this risk, there are currently no standardized screening guidelines for liver fibrosis in SCD. Shear wave elastography (SWE), a non-invasive ultrasound-based imaging modality, measures liver stiffness to assess fibrosis severity. This study describes a quality improvement initiative using ultrasound with SWE (US-SWE) in a Canadian SCD clinic to detect early hepatic fibrosis and guide potential interventions. Aim: To determine the proportion of patients with any degree of liver fibrosis identified through US-SWE and to characterize relevant clinical, laboratory, and treatment factors associated with fibrosis in a comprehensive adult SCD population. Methods: This retrospective cohort study included adults (≥18 years) with SCD followed at the Southern Alberta Rare Blood and Bleeding Disorders Clinic in Calgary, Canada, who underwent US-SWE between 2019 and 2024. US-SWE results were categorized by METAVIR-equivalent staging (F0–F4). Laboratory values obtained within six months of US-SWE included hemoglobin (Hgb), platelet count, total bilirubin, albumin, international normalized ratio (INR), alanine aminotransferase (ALT), and ferritin. Abnormal US-SWE was defined as ≥F2 (significant fibrosis). Descriptive statistics summarized patient characteristics and clinical outcomes. Results: Of 219 patients screened, 153 (70%) underwent US-SWE. Median age was 38.9 years (range 18–72); 90 (59%) were female. Genotypes included 100 (65%) with HbSS, 47 (31%) with HbSC, and 6 (4%) with HbSβ-thalassemia. US-SWE staging showed 141 (92%) with no or mild fibrosis (F0–F1), 10 (7%) with moderate fibrosis (F2), 0 with bridging fibrosis (F3), and 2 (1%) with cirrhosis (F4). All but one patient with abnormal SWE (≥F2) had HbSS genotype. Four patients with abnormal US-SWE demonstrated additional radiographic features suggestive of cirrhosis, including nodular liver contour and/or signs of portal hypertension. Among the 12 patients with abnormal US-SWE, three had transfusion-associated iron overload and were either receiving or recommended to initiate iron chelation therapy. Hepatitis B surface antigen was negative in all patients; one was positive for hepatitis B core antibody and six for hepatitis B surface antibody, indicating prior vaccination or resolved infection. Two patients were hepatitis C antibody-positive and had completed antiviral treatment. Hepatology was involved in seven cases—two prior to and five following US-SWE findings. No patients underwent liver biopsy. Among patients with abnormal US-SWE, four were on automated red cell exchange (RCE), six on hydroxyurea, and two on no SCD-directed therapy at the time of screening. No new disease-modifying therapies were initiated based solely on US-SWE results. Mean laboratory values for the entire cohort were: Hgb 94 g/L (reference: males 135–175 g/L; females 120–160 g/L), platelet count 271 × 10⁹/L (reference: 140–400 × 10⁹/L), total bilirubin 58 µmol/L (reference: <20 µmol/L), albumin 38 g/L (reference: 35–50 g/L), INR 1.2 (reference: 0.8–1.2), ALT 24 U/L (reference: <70 U/L), and ferritin 1267 µg/L (reference: 30–500 µg/L). Conclusions: US-SWE screening, implemented as routine surveillance in a comprehensive adult SCD clinic, identified significant hepatic fibrosis (≥F2) in 8% of patients. Importantly, testing was not prompted by clinical suspicion but incorporated into standard care. This low-barrier, non-invasive modality enabled early identification of hepatic fibrosis and facilitated timely hepatology referral. While US-SWE is not currently standard of care for liver disease surveillance in SCD, our experience highlights its feasibility, safety, and potential clinical value in this high-risk population. Routine incorporation of US-SWE may provide a proactive strategy to identify at-risk individuals and guide multidisciplinary interventions to prevent cirrhosis progression.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».