Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach

Impact of cholecystectomy on the gut-liver axis and metabolic disorders

Cholecystectomy is considered as a safe procedure to treat patients with gallstones. However, epidemiological studies highlighted an association between cholecystectomy and metabolic disorders, such as type 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease (MASLD), independently of the gallstone disease. Following cholecystectomy, bile acids flow directly from the liver into the intestine, leading to changes in the entero-hepatic circulation of bile acids and their metabolism. The changes in bile acids metabolism impact the gut microbiota. Therefore, cholecystectomized patients display gut dysbiosis characterized by a reduced diversity, a loss of bacteria producing short-chain fatty acids and an increase in pro-inflammatory bacteria. Alterations of both bile acids metabolism and gut microbiota occurring after cholecystectomy can promote the development of metabolic disorders. In this review, we discuss the impact of cholecystectomy on bile acids and gut microbiota and its consequences on metabolic functions.

Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance

Hepatocellular carcinoma (HCC) presents high mortality rates worldwide, with limited evidence on prognostic factors at diagnosis. This study evaluates the utility of common scores incorporating albumin as predictors of mortality at HCC diagnosis using Machine Learning techniques. They are also compared to other scores and variables commonly used. A retrospective cohort study was conducted with 191 patients from Virgen de la Luz Hospital of Cuenca and University Hospital of Guadalajara. Demographic, analytical, and tumor-specific variables were included. Various Machine Learning algorithms were implemented, with eXtreme Gradient Boosting (XGB) as the reference method. In the predictive model developed, the Barcelona Clinic Liver Cancer score was the best predictor of mortality, closely followed by the Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores. Albumin levels alone also showed high relevance. Other scores, such as C-Reactive Protein/albumin and Child-Pugh performed less effectively. XGB proved to be the most accurate method across the metrics analyzed, outperforming other ML algorithms. In conclusion, the Barcelona Clinic Liver Cancer, Platelet-Albumin-Bilirubin and Albumin-Bilirubin scores are highly reliable for assessing survival at HCC diagnosis. The XGB-developed model proved to be the most reliable for this purpose compared to the other proposed methods.

The bile-gut axis and metabolic consequences of cholecystectomy

Cholelithiasis and cholecystitis affect individuals of all ages and are often treated by surgical removal of the gallbladder (cholecystectomy), which is considered a safe, low-risk procedure. Nevertheless, recent findings show that bile and its regulated storage and excretion may have important metabolic effects and that cholecystectomy is associated with several metabolic diseases postoperatively. Bile acids have long been known as emulsifiers essential to the assimilation of lipids and absorption of lipid-soluble vitamins, but more recently, they have also been reported to act as metabolic signaling agents. The nuclear receptor, farnesoid X receptor (FXR), and the G protein-coupled membrane receptor, Takeda G protein-coupled receptor 5 (TGR5), are specific to bile acids. Through activation of these receptors, bile acids control numerous metabolic functions. Cholecystectomy affects the storage and excretion of bile acids, which in turn may influence the activation of FXR and TGR5 and their effects on metabolism including processes leading to metabolic conditions such as metabolic dysfunction-associated steatotic liver disease and metabolic syndrome. Here, with the aim of elucidating mechanisms behind cholecystectomy-associated dysmetabolism, we review studies potentially linking cholecystectomy and bile acid-mediated metabolic effects and discuss possible pathophysiological mechanisms behind cholecystectomy-associated dysmetabolism.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.