Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.

Critical appraisal of pharmacoeconomic studies comparing TNF-alpha antagonists for rheumatoid arthritis treatment.

Clinical trials and economic models have showed that anti-TNF agents are effective and cost effective compared with standard therapies (mainly disease-modifying antirheumatic drugs) in patients with rheumatoid arthritis. However, no head-to-head clinical trials between these agents are available and the relative effectiveness and cost-effectiveness is uncertain. We have conducted a literature review in order to identify full economic evaluations that compared two or more TNF antagonists. A description of each study was given and key methodological issues identified. These included methods for evidence synthesis, model characteristics and methods to address uncertainty in model parameters. Important differences in methodological features and results have been found between studies. More attention in future studies should be given to methods for indirect comparisons between the biological agents.