Background/Aims Prediction of short-term mortality in patients with acute-on-chronic liver failure (ACLF) admitted to the intensive care unit (ICU) may enhance effective management.
Methods To develop, explain, and validate a predictive machine learning (ML) model for short-term mortality in patients with ACLF with two or more organ failures (OFs). Utilizing a large ICU cohort with detailed clinical information, we identified ACLF patients with two or more OFs according to the EASL-CLIF and NACSELD definitions. ML model was developed for each definition to predict 30-day mortality. The Shapley value was estimated to explain the models. Validation and calibration of these models were performed.
Results Of 5,994 patients with cirrhosis admitted to ICU, 1,511 met NACSELD criteria, and 1,692 met EASL-CLIF grade II or higher criteria. The CatBoost ACLF (CBA) model had the greatest accuracy in the NACSELD cohort (area under curve [AUC] of 0.87), while the Random Forest ACLF (RFA) model performed best in the EASL-CLIF cohort (AUC of 0.83). Both models showed robust calibration. The models were explained by SHAP score analysis, yielding a rank list, and the top twelve predictors were selected. Both simplified models demonstrated similar performance (CBA model: AUC 0.89, RFA model: AUC 0.81) and significantly outperformed contemporary scoring systems, including CLIF-C ACLF and MELD 3.0. The models were validated in both internal and external cohorts. A simple-to-use online tool was created to predict mortality rates.
Conclusions We presented explainable, well-validated, and calibrated predictive models for ACLF patients with two or more OFs, which outperformed existing predictive scores.
MAFLD or MASLD: Which better represents the prognosis of the steatotic liver population: Letter to the editor on “Evolutionary changes in metabolic dysfunction-associated steatotic liver disease and risk of hepatocellular carcinoma: A nationwide cohort st Ying Wang, Shengfeng Wang, Xiude Fan, Jiajun Zhao, Yongfeng Song Clinical and Molecular Hepatology.2025; 31(2): e128. CrossRef
Michael H. Le, David M. Le, Thomas C. Baez, Hansen Dang, Vy H. Nguyen, KeeSeok Lee, Christopher D. Stave, Takanori Ito, Yuankai Wu, Yee Hui Yeo, Fanpu Ji, Ramsey Cheung, Mindie H. Nguyen
Clin Mol Hepatol 2024;30(2):235-246. Published online January 26, 2024
Background/Aims Nonalcoholic fatty liver disease (NAFLD) is associated with a multitude of adverse outcomes. We aimed to estimate the pooled incidence of NAFLD-related adverse events.
Methods We performed a systematic review and meta-analysis of cohort studies of adults with NAFLD to evaluate the pooled incidence of adverse events.
Results 19,406 articles were screened, 409 full-text articles reviewed, and 79 eligible studies (1,377,466 persons) were included. Mean age was 51.47 years and body mass index 28.90 kg/m2. Baseline comorbidities included metabolic syndrome (41.73%), cardiovascular disease (CVD) (16.83%), cirrhosis (21.97%), and nonalcoholic steatohepatitis (NASH) (58.85%). Incidence rate per 1,000 person-years for mortality included: all-cause (14.6), CVD-related (4.53), non-liver cancer-related (4.53), and liver-related (3.10). Incidence for liver-related events included overall (24.3), fibrosis progression (49.0), cirrhosis (10.9), liver transplant (12.0), and hepatocellular carcinoma (HCC) (3.39). Incidence for non-liver events included metabolic syndrome (25.4), hypertension (25.8), dyslipidemia (26.4), diabetes (19.0), CVD (24.77), renal impairment (30.3), depression/anxiety (29.1), and non-liver cancer (10.5). Biopsy-proven NASH had higher incidence of HCC (P=0.043) compared to non-NASH. Higher rates of CVD and mortality were observed in North America and Europe, hypertension and non-liver cancer in North America, and HCC in Western Pacific/Southeast Asia (P<0.05). No significant differences were observed by sex. Time-period analyses showed decreasing rates of cardiovascular and non-liver cancer mortality and increasing rates of decompensated cirrhosis (P<0.05).
Conclusions People with NAFLD have high incidence of liver and non-liver adverse clinical events, varying by NASH, geographic region, and time-period, but not sex.
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Background/Aims Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive and personalized care to improve outcomes. ChatGPT (Generative Pre-trained Transformer), a large language model, holds the potential to provide professional yet patient-friendly support. We aimed to examine the accuracy and reproducibility of ChatGPT in answering questions regarding knowledge, management, and emotional support for cirrhosis and HCC.
Methods ChatGPT’s responses to 164 questions were independently graded by two transplant hepatologists and resolved by a third reviewer. The performance of ChatGPT was also assessed using two published questionnaires and 26 questions formulated from the quality measures of cirrhosis management. Finally, its emotional support capacity was tested.
Results We showed that ChatGPT regurgitated extensive knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The performance was better in basic knowledge, lifestyle, and treatment than in the domains of diagnosis and preventive medicine. For the quality measures, the model answered 76.9% of questions correctly but failed to specify decision-making cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines variations, such as HCC screening criteria. However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis.
Conclusions We analyzed the areas of robustness and limitations of ChatGPT’s responses on the management of cirrhosis and HCC and relevant emotional support. ChatGPT may have a role as an adjunct informational tool for patients and physicians to improve outcomes.
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Background/Aims Due to increases in obesity and type 2 diabetes, the prevalence of nonalcoholic fatty liver disease (NAFLD) has also been increasing. Current forecast models may not include non-obese NAFLD. Here, we used the Bayesian approach to forecast the prevalence of NAFLD through the year 2040.
Methods Prevalence data from 245 articles involving 2,699,627 persons were used with a hierarchical Bayesian approach to forecast the prevalence of NAFLD through 2040. Subgroup analyses were performed for age, gender, presence of metabolic syndrome, region, and smoking status. Sensitivity analysis was conducted for clinical setting and study quality.
Results The forecasted 2040 prevalence was 55.7%, a three-fold increase since 1990 and a 43.2% increase from the 2020 prevalence of 38.9%. The estimated average yearly increase since 2020 was 2.16%. For those aged <50 years and ≥50 years, the 2040 prevalence were not significantly different (56.7% vs. 61.5%, P=0.52). There was a significant difference in 2040 prevalence by sex (males: 60% vs. 50%) but the trend was steeper for females (annual percentage change: 2.5% vs. 1.5%, P=0.025). There was no difference in trends overtime by region (P=0.48). The increase rate was significantly higher in those without metabolic syndrome (3.8% vs. 0.84%, P=0.003) and smokers (1.4% vs. 1.1%, P=0.011). There was no difference by clinical/community setting (P=0.491) or study quality (P=0.85).
Conclusion By 2040, over half the adult population is forecasted to have NAFLD. The largest increases are expected to occur in women, smokers, and those without metabolic syndrome. Intensified efforts are needed to raise awareness of NAFLD and to determine long-term solutions addressing the driving factors of the disease.
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