METHODS: We conducted a questionnaire-based survey of consecutive out-patients with no diagnosed mental health illness (n = 289) and their primary caregivers (n = 247) from 10 centers across eight countries (Bangladesh, India, Iran, Malaysia, Myanmar, Nepal, Pakistan, Thailand) of IBD-Emerging Nations' Consortium (ENC). Patients were assessed for anxiety (PHQ-9), depression (GAD-7), quality of life (SIBDQ, IBDCOPE) and medication adherence (MMAS-8). Caregiver burden was assessed by Zarit-Burden Interview (ZBI), Ferrans and Power Quality of Life (QOL) scores and coping strategies (BRIEF-COPE). Multivariate logistic regression and correlation analyses were performed to identify risk factors and the impact on QOL in patients and caregivers.
RESULTS: Moderate to severe depression and anxiety were noted in 33% (severe 3.5%) and 24% (severe 3.8%) patients, respectively. The risk factor for depression was active disease (p
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 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.
METHODS: Patients with MAFLD-ACLF were recruited from the AARC registry. The diagnosis of MAFLD-ACLF was made when the treating unit had identified the etiology of chronic liver disease (CLD) as MAFLD (or previous nomenclature such as NAFLD, NASH, or NASH-cirrhosis). Patients with coexisting other etiologies of CLD (such as alcohol, HBV, HCV, etc.) were excluded. Data was randomly split into derivation (n=258) and validation (n=111) cohorts at a 70:30 ratio. The primary outcome was 90-day mortality. Only the baseline clinical, laboratory features and severity scores were considered.
RESULTS: The derivation group had 258 patients; 60% were male, with a mean age of 53. Diabetes was noted in 27%, and hypertension in 29%. The dominant precipitants included viral hepatitis (HAV and HEV, 32%), drug-induced injury (DILI, 29%) and sepsis (23%). MELD-Na and AARC scores upon admission averaged 32±6 and 10.4±1.9. At 90 days, 51% survived. Non-viral precipitant, diabetes, bilirubin, INR, and encephalopathy were independent factors influencing mortality. Adding diabetes and precipitant to MELD-Na and AARC scores, the novel MAFLD-MELD-Na score (+12 for diabetes, +12 for non-viral precipitant) and MAFLD-AARC score (+5 for each) were formed. These outperformed the standard scores in both cohorts.
CONCLUSION: Almost half of MAFLD-ACLF patients die within 90 days. Diabetes and non-viral precipitants such as DILI and sepsis lead to adverse outcomes. The new MAFLD-MELD-Na and MAFLD-AARC scores provide reliable 90-day mortality predictions for MAFLD-ACLF patients.