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LINKED CONTENT: This article is linked to Verma et al papers. To view these articles, visit https://doi.org/10.1111/apt.17891 and https://doi.org/10.1111/apt.17902 [ABSTRACT FROM AUTHOR]

Background: Hyponatremia portends a poor prognosis in decompensated cirrhosis and is an independent predictor of mortality. Multiple modalities have been evaluated in the management of hyponatremia, including albumin infusion. However, the effect of albumin infusion on the resolution of hyponatremia is unclear. We conducted a systematic review to explore the available literature on the use of albumin infusion in hyponatremia. Methods: We performed a comprehensive search up to 31st December 2022 using MEDLINE, EMBASE, and Scopus for studies reporting the effectiveness of albumin infusion in the resolution of hyponatremia. The impact of albumin infusion of any dose, administration frequency, and duration of therapy was recorded. The study protocol was prospectively registered (CRD42021245914). Results: The literature search yielded 1322 references after duplicate removal. Only seven studies (three randomized trials, three cohort studies, and one case series) satisfied the predefined selection criteria after a full-text review. While hyponatremia was clearly defined as serum sodium < 130 meEq/L in all studies, two studies explicitly defined hyponatremia resolution (serum sodium > 135 mEq/L). No differentiation was made between the types of hyponatremia. The strength of the albumin infusion used was 5% and 20%. All but one study reported significant improvement in hyponatremia with albumin infusion. A subgroup analysis showed albumin infusion improved 30-day survival (odds ratio 0.43, 95% CI 0.25–0.74, I2 = 0.) No studies reported adverse events or the impact of concomitant associations (diuretic withdrawal, lactulose use, sepsis). Conclusion: Despite available literature on the use of albumin infusion for the resolution of hyponatremia, the level of evidence remains low. Large prospective studies with pre-defined selection criteria and endpoints are required to generate the evidence. [ABSTRACT FROM AUTHOR]

Background: Cyproheptadine is an easily available over-the-counter first-generation antihistaminic that is often used as an appetite stimulant. Although it is usually a safe drug, rare instances of drug-induced liver injury may occur. Case presentation: We report a case of cholestatic liver injury secondary to cyproheptadine in a young pharmacy student with chronic hepatitis B and review the literature of cyproheptadine-induced liver injury. Conclusion: Although cyproheptadine is largely a safe drug, its potential for significant liver toxicity cannot be ignored. [ABSTRACT FROM AUTHOR]

Summary: Background: The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non‐alcoholic fatty liver disease (NAFLD/MASLD). Aims: We evaluated the performance of machine learning (ML) and non‐patented scores for ruling out SF among NAFLD/MASLD patients. Methods: Twenty‐one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically‐proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological‐SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1‐score as model‐selection criteria). Results: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological‐SF were included in the study. Patients with SFvs.no‐SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%–12% better discrimination than FIB‐4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB‐4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set). Conclusions: ML with clinical, anthropometric data and simple blood investigations perform better than FIB‐4 for ruling out SF in biopsy‐proven Asian NAFLD/MASLD patients. [ABSTRACT FROM AUTHOR]