Associated Factors for Liver Fibrosis and Histological Activity Index in Treatment-Naïve HBeAg-Positive Chronic Hepatitis B Infection: Insights from a Retrospective Analysis.

Objective: The study aimed to identify predictors of advanced fibrosis score and histological activity index (HAI) in HBeAg-positive patients to facilitate early disease detection and reduce the need for invasive biopsies. Materials and Methods: The single-center retrospective study included treatment-naïve HBeAg-positive chronic hepatitis B (CHB) patients. Patients with HAI ≥6 and/or fibrosis ≥2 were considered to have significant liver damage. Independent determinants were identified by univariate and multivariate logistic regression analysis. Cut-off values for variables were determined by receiver operating characteristics (ROCs) curve analysis. Results: The study enrolled a cohort of 66 patients, with 51.5% male and a median age of 26 (22.7-34.2) years. In assessing necroinflammation, no significant differences were observed in age and gender between patients with HAI <6 and HAI ≥6. However, patients with HAI ≥6 exhibited higher aspartate aminotransferase (AST) levels compared to those with HAI <6. Furthermore, lower albumin levels and platelet (PLT) counts, along with higher fibrosis-4 (FIB-4) scores, were associated with HAI ≥6. In the evaluation of fibrosis, while gender distribution did not differ significantly, patients with fibrosis grade ≥2 were older and had higher HAI scores, HAI ≥6 ratios, and FIB-4 scores compared to those with fibrosis grade <2. Multivariate analysis identified albumin as a significant predictor for both HAI ≥6 and fibrosis grade ≥2. The area under ROC (AUROC) values of albumin for predicting HAI ≥6 and fibrosis ≥2 were 0.696 and 0.698, respectively, indicating moderate predictive ability. Conclusion: Albumin was found to be an independent predictor of liver damage in HBeAg-positive CHB patients. The fact that the optimal threshold values detected for both HAI ≥6 and fibrosis ≥2 in this patient group were close to normal values suggests that clinicians should be more cautious in monitoring albumin levels and other pre-defined parameters to avoid delays in diagnosis.

Predictors of Significant Histological Hepatic Abnormality in Treatment-Naive Patients Infected with HBeAg-Negative Chronic Hepatitis B.

Objective: This study aimed to determine the predictors for significant hepatic abnormality (SHA), a treatment indication, by assessing demographic, laboratory, and radiological results of chronic hepatitis B (CHB) patients who underwent liver biopsy. Materials and Methods: In this retrospective study, individuals with untreated hepatitis B e-antigen (HBeAg)-negative CHB infection were enrolled. Multivariate analysis modeling was conducted with parameters identified as predictors for SHA in univariate analysis. Optimal threshold levels for variables to predict SHA in patients with chronic hepatitis B were determined based on receiver operating characteristic (ROC) curve analysis. Results: A total of 566 patients with untreated chronic hepatitis B were included in the cohort; 61% (345/566) were male, and the median age was 41 years (interquartile range [IQR]=34-50). Notably, 36.9% (209/566) had SHA. In the multivariate analysis, utilizing different models, age, gender, HBV-DNA, LDL, ALT, and platelet count were identified as the most reliable predictors for SHA in CHB patients. For predicting SHA, the area under the ROC curve values of HBV-DNA, AST, and ALT were 0.704 (sensitivity=62.8%, specificity=76.2%; p<0.0001), 0.747 (sensitivity=51.9%, specificity=88.9%; p<0.0001), and 0.737 (sensitivity=68.6%, specificity=68.4%; p<0.0001), respectively. Conclusion: In our study, age, male gender, ALT, AST, HBV-DNA, LDL cholesterol, platelet count, and FIB-4 score were independent predictors of SHA in HBeAg-negative chronic hepatitis B. The most sensitive parameters for SHA were LDL and ALT. The most specific parameters were age, AST, and APRI score. SHA may occur in patients with high HBV-DNA levels, even if ALT values are normal in HBeAg-negative patients.

How Reliable is ChatGPT as a Novel Consultant in Infectious Diseases and Clinical Microbiology?

Objective: The study aimed to investigate the reliability of ChatGPT's answers to medical questions, including those sourced from patients and guide recommendations. The focus was on evaluating ChatGPT's accuracy in responding to various types of infectious disease questions. Materials and Methods: The study was conducted using 200 questions sourced from social media, experts, and guidelines related to various infectious diseases like urinary tract infection, pneumonia, HIV, various types of hepatitis, COVID-19, skin infections, and tuberculosis. The questions were arranged for clarity and consistency by excluding repetitive or unclear ones. The answers were based on guidelines from reputable sources like the Infectious Diseases Society of America (IDSA), Centers for Disease Control and Prevention (CDC), European Association for the Study of Liver Disease (EASL) and Joint United Nations Programme on HIV/AIDS (UNAIDS) AIDSinfo. According to the scoring system, completely correct answers were given 1-point, and completely incorrect ones were given 4-points. To assess reproducibility, each question was posed twice on separate computers. Repeatability was determined by the consistency of the answers' scores. Results: In the study, ChatGPT was posed with 200 questions: 107 from social media platforms and 93 from guidelines. The questions covered a range of topics: urinary tract infections (n=18 questions), pneumonia (n=22), HIV (n=39), hepatitis B and C (n=53), COVID-19 (n=11), skin and soft tissue infections (n=38), and tuberculosis (n=19). The lowest accuracy was 72% for urinary tract infections. ChatGPT answered 92% of social media platform questions correctly (scored 1-point) versus 69% of guideline questions (p=0.001; OR=5.48, 95% CI=2.29-13.11). Conclusion: Artificial intelligence is widely used in the medical field by both healthcare professionals and patients. Although ChatGPT answers questions from social media platforms quite properly, we recommend that healthcare professionals be conscientious when using it.

Use of high-dose steroid therapy: addition of anakinra in the treatment of severe COVID-19.

OBJECTIVE: The aim of this study was to compare the clinical effects of the addition of anakinra to high-dose steroid therapy in COVID-19 patients with macrophage activation syndrome. METHODS: This was a single-center retrospective study conducted in Ümraniye Training and Research Hospital between March 11, 2020, and April 28, 2021. Patients receiving only high-dose steroid or anakinra+steroid were enrolled. The first day of anakinra was considered as day 0. Laboratory values and oxygen requirements were followed up for 7 days. Patients were divided into two groups: 66 patients in the high-dose steroid group and 67 patients in the anakinra+steroid group. The primary outcome was 28-day mortality. RESULTS: After treatment, a significant decrease in ferritin levels was detected only in the anakinra+steroid group (p=0.001). In both groups, there were significant changes in lymphocytes, C-reactive protein, lactate dehydrogenase, and fibrinogen levels during the 7-day follow-up. Changes in oxygen status according to the World Health Organization clinical scale on day 3 and day 7 between high-dose steroid and anakinra+steroid groups were similar (p=0.976). Complications were higher in the anakinra+steroid group than in the steroid group (26% vs. 12%, p=0.03). The rates of 28-day mortality were 57% in the anakinra+steroid group and 42% in the high-dose steroid group (p=0.48). In multivariate regression, anakinra did not affect 28-day mortality (p=0.67). CONCLUSION: The addition of anakinra to steroid treatment resulted in a significant decrease in biochemical parameters. However, no significant difference was observed in the oxygen status between the groups. The addition of anakinra to steroid treatment did not decrease mortality. Clinicians should be aware of the complications of anti-inflammatory therapies.

Independent predictors for non-alcoholic fatty liver disease in patients with treatment-naïve chronic hepatitis B.

OBJECTIVE: There are limited data on non-alcoholic fatty liver disease in chronic hepatitis B virus infection. We aimed to determine the predictors for non-alcoholic fatty liver disease in patients with treatment-naïve chronic hepatitis B virus infection. METHODS: All consecutive treatment-naïve patients with chronic hepatitis B virus infection at the Haseki Training and Research Hospital between October 1, 2021, and September 31, 2022, were retrospectively enrolled. Chronic hepatitis B virus infection is defined by positive serum hepatitis B surface antigen for 6 months or more. Patients with significant alcohol consumption, prolonged steatogenic drug use, malignancy, monogenic hereditary disorders, patients co-infected with hepatitis D virus, hepatitis C virus infection, or human immunodeficiency virus were excluded. Demographic characteristics, anthropometric determinants, laboratory findings, and virological parameters were retrospectively collected from patients' charts and electronic medical records. RESULTS: A total of 457 patients with treatment-naïve chronic hepatitis B virus infection were included in the study. The three multivariate regression models revealed that age (p<0.028), body mass index (p=0.046), diabetes mellitus (p=0.030), hemoglobin (p=0.008), platelet (p=0.012), and triglyceride (p=0.002) in Model 1; body mass index (p=0.033), diabetes mellitus (p<0.001), hemoglobin (p=0.008), platelet (p=0.004), LDL (p=0.023), and HDL (p=0.020) in Model 2; and age (p<0.001), body mass index (p=0.033), hemoglobin (p=0.004), platelet (p=0.004), and HDL (p=0.007) in Model 3 were independent predictors. CONCLUSION: Non-alcoholic fatty liver disease was observed in about one-third of patients with chronic hepatitis B virus infection and was positively associated with older age, higher body mass index, presence of comorbid conditions including diabetes mellitus, increased levels of metabolic laboratory parameters, especially serum triglyceride and LDL, and decreased HDL.

Independent predictors for non-alcoholic fatty liver disease in patients with treatment-naïve chronic hepatitis B

SUMMARY OBJECTIVE: There are limited data on non-alcoholic fatty liver disease in chronic hepatitis B virus infection. We aimed to determine the predictors for non-alcoholic fatty liver disease in patients with treatment-naïve chronic hepatitis B virus infection. METHODS: All consecutive treatment-naïve patients with chronic hepatitis B virus infection at the Haseki Training and Research Hospital between October 1, 2021, and September 31, 2022, were retrospectively enrolled. Chronic hepatitis B virus infection is defined by positive serum hepatitis B surface antigen for 6 months or more. Patients with significant alcohol consumption, prolonged steatogenic drug use, malignancy, monogenic hereditary disorders, patients co-infected with hepatitis D virus, hepatitis C virus infection, or human immunodeficiency virus were excluded. Demographic characteristics, anthropometric determinants, laboratory findings, and virological parameters were retrospectively collected from patients' charts and electronic medical records. RESULTS: A total of 457 patients with treatment-naïve chronic hepatitis B virus infection were included in the study. The three multivariate regression models revealed that age (p<0.028), body mass index (p=0.046), diabetes mellitus (p=0.030), hemoglobin (p=0.008), platelet (p=0.012), and triglyceride (p=0.002) in Model 1; body mass index (p=0.033), diabetes mellitus (p<0.001), hemoglobin (p=0.008), platelet (p=0.004), LDL (p=0.023), and HDL (p=0.020) in Model 2; and age (p<0.001), body mass index (p=0.033), hemoglobin (p=0.004), platelet (p=0.004), and HDL (p=0.007) in Model 3 were independent predictors. CONCLUSION: Non-alcoholic fatty liver disease was observed in about one-third of patients with chronic hepatitis B virus infection and was positively associated with older age, higher body mass index, presence of comorbid conditions including diabetes mellitus, increased levels of metabolic laboratory parameters, especially serum triglyceride and LDL, and decreased HDL.