Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Mortality by COVID-19 Before Vaccination - One Year Experience of Hospitalized Patients in Madrid.

OBJECTIVES: The aim of the study was to analyze the mortality and characteristics of deceased patients with COVID-19 during the first year of the pandemic. METHODS: All admissions owing to COVID-19 at a tertiary hospital in Madrid were analyzed. Three waves were considered: March 2020 to June 2020, July 2020 to November 2020, and December 2020 to April 2021. RESULTS: A total of 3,676 patients were identified. Among inpatients, no differences regarding age, sex, length of admission, or mortality were found between the 3 waves (p >0.05). The overall mortality rate was 12.9%. Among deceased patients, the median age was 82 years and the median Charlson Comorbidity Index was 6. Considering the main predictors for mortality by COVID-19 (age, sex, and concomitant comorbidities), only patients with previous lung disease were more prevalent in the third period (p <0.01). Finally, higher intensive care unit admission rates, a lower rate of patients coming from nursing homes, and a lower rate of patients with dementia were noted in the third period (p <0.05) among deceased patients. CONCLUSION: One year after the onset of the pandemic, the mortality rate of hospitalized patients and the profile of non-survivors have not changed significantly. In the absence of vaccine benefits, advanced age and multiple pathologies are uniform characteristics of non-survivors.