AI-Safe-C Score: Assessing Liver-Related Event Risks in Non-Cirrhotic Patients after Successful Direct-Acting Antiviral Treatment.

BACKGROUND & AIMS: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, post-sustained virological response (SVR) follow-up typically neglects the risk of liver-related events (LREs). This study introduces and validates artificial intelligence-safe score (AI-Safe-C score) to assess the risk of LREs in non-cirrhotic patients after successful DAA treatment. METHODS: The random survival forest model was trained to predict LREs in 913 non-cirrhotic HCV patients after SVR in Korea and was further tested in a combined cohort from Hong Kong and France (N = 1264). The model's performance was assessed using Harrell's C-index and the area under the time-dependent receiver operating characteristic curve (AUROC). RESULTS: The AI-Safe-C score, which incorporated liver stiffness measurement (LSM), age, sex, and six other biochemical tests-with LSM being ranked as the most important among 9 clinical features-demonstrated a C-index of 0.86 (95% confidence interval [CI]: 0.82-0.90) in predicting LREs in an external validation cohort. It achieved 3- and 5-year LRE AUROCs of 0.88 (95%CI, 0.84-0.92) and 0.79 (95%CI, 0.71-0.87), respectively, and for hepatocellular carcinoma, a C-index of 0.87 (95%CI, 0.81-0.92) with 3- and 5-year AUROCs of 0.88 (95%CI, 0.84-0.93) and 0.82 (95%CI, 0.75-0.90), respectively. Using a cut-off of 0.7, the 5-year LRE rate within a high-risk group was between 3.2% and 6.2%, mirroring the incidence observed in individuals with advanced fibrosis, in stark contrast to the significantly lower incidence of 0.2% to 0.6% in a low-risk group. CONCLUSION: AI-Safe-C score is a useful tool for identifying patients without cirrhosis who are at higher risk of developing LREs. The post-SVR LSM, as integrated within the AI-Safe-C score, plays a critical role in predicting future LREs. IMPACT AND IMPLICATIONS: The AI-Safe-C score introduces a paradigm shift in the management of non-cirrhotic patients post-DAA treatment, a cohort traditionally not included in routine surveillance protocols for LREs. By accurately identifying a subgroup at a comparably high risk of LREs, akin to those with advanced fibrosis, this predictive model facilitates a strategic reallocation of surveillance and clinical resources.

Effects of Diabetes and Glycemic Control on Risk of Hepatocellular Carcinoma After Seroclearance of Hepatitis B Surface Antigen.

BACKGROUND & AIMS: Diabetes is associated with a 2-fold increase in risk of hepatocellular carcinoma (HCC) among patients with chronic hepatitis B virus (HBV) infection. However, we know little about the effect of diabetes on HCC risk after seroclearance of hepatitis B surface antigen (HBsAg). We evaluated the effect of diabetes and glycemic control on HCC development after HBsAg seroclearance in a population-wide study in Hong Kong. METHODS: We performed a retrospective study of 4568 patients with chronic HBV infection who cleared HBsAg from January 2000 through August 2016, using the Clinical Data Analysis and Reporting System of the Hospital Authority, Hong Kong. We collected and analyzed data on patient demographics, comorbidities, medications, laboratory test results, and subsequent development of HCC. The presence of diabetes was defined by International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code, with level of hemoglobin A1c (HbA1c) above 6.5%, fasting glucose level of 7 mmol/L or more, or treatment with any antidiabetic agent. RESULTS: We identified 1560 patients with diabetes; 29 patients (1.9%) developed HCC after a median follow-up time of 3.4 years (interquartile range, 1.5-5.0 years). Diabetes was associated with increased risk of HCC after adjustment of age, sex, presence of cirrhosis, and the use of medications (adjusted hazard ratio, 1.85; 95% CI, 1.04-3.28; P = .036). Among patients with diabetes, time-weighted average level of HbA1c was an independent risk factor for HCC, after adjustment for age at clearance, use of statins, and other important covariates (adjusted hazard ratio: 1.51; 95% CI, 1.20-1.91; P < .001). A time-weighted average level of HbA1c of 7% or more was associated with a higher 5-year cumulative incidence of HCC (4.0%) than a time-weighted average HbA1c level below 7% (1.8%; log-rank test P = .035). CONCLUSIONS: In a population-based analysis of patients with chronic HBV infection in Hong Kong, we found diabetes to be an independent risk factor for HCC after HBsAg seroclearance. However, glycemia control appears to reduce the risk of HCC.

Utility of multi-biomarker panel on discriminating disease activity in patients with psoriatic arthritis.

OBJECTIVE: To investigate the correlation of serum protein biomarkers and disease activity in patients with PsA. METHODS: 176 patients fulfilled the CASPAR (ClASsification criteria for Psoriatic ARthritis) were recruited in this cross-sectional study. The level of 48 protein biomarkers, cartilage and bone turn-over markers were assessed. The patients were randomly divided into a derivation-cohort and a validation-cohort at a ratio of 7:3. Patients were further categorized based on their disease activity states using cDAPSA (remission/low disease activity and moderate/high disease activity). Least absolute shrinkage and selection operator (LASSO) was used to select biomarkers which were associated with moderate/high disease activity in the derivation cohort. Receiver operating characteristic (ROC) curve, GiViTI calibration belt were used to assess the performance of the model in both cohorts. RESULTS: The cohort [age: 55.5 (44.0-62.75) years, male: 80 (45.5 %)] had moderate disease activity [DAPSA: 15.9 (8.3-26.9); PASI: 3.2 (0.5-6.8)]. 101 PsA patients (57.4 %) had clinical DAPSA moderate/high disease activity. Biomarker levels associated with moderate/high disease activity included SAA (Serum amyloid A), IL-8 (Interleukin 8), IP10 (Interferon gamma-induced protein 10)/CXCL10, M-CSF (Macrophage colony-stimulating factor), SCGF-ß (Stem cell growth factor), SDF-1α (Stromal cell-derived factor 1α)/CXCL12. The model's equation including the 6 biomarker levels was applied to the validation-cohort. The area under the ROC curve (AUC) for discriminating moderate/high disease activity was 0.802 and 0.835 for the derivation-and-validation-cohorts, respectively. The multi-biomarkers panel model had higher-AUC when compared with that of C-reactive protein (CRP) (AUC = 0.727, p = 0.022). The P-values of calibration charts in the two sets were 0.902 and 0.123. CONCLUSIONS: The multi-biomarkers panel demonstrated the ability to discriminate patients with moderate/high disease activity from those with low disease activity/remission.