Background: Spain had some of Europe’s highest incidence and mortality rates for coronavirus disease 2019 (COVID-19). Here we describe the epidemiology and trends in hospitalizations, the number of critical patients, and deaths in Spain in 2020 and 2021.Methods: We performed a descriptive, retrospective, nationwide study using an administrative database, the Minimum Basic Data Set at Hospitalization, which includes 95–97% of discharge reports for patients hospitalized in Spain in 2020 and 2021. We analyzed the number of hospitalizations, admissions to intensive care units, and deaths and their geographic distribution across regions of Spain.Results: As of December 31, 2021, a total of 498,789 patients (1.04% of the entire Spanish population) had needed hospitalization. At least six waves of illness were identified. Men were more prone to hospitalization than women. The median age was 66. A total of 54,340 patients (10.9% of all hospitalizations) had been admitted to the intensive care unit. We identified 71,437 deaths (mortality rate of 14.3% among hospitalized patients). We also observed important differences among regions, with Madrid being the epicenter of hospitalizations and mortality.Conclusions: We analyzed Spain’s response to COVID-19 and describe here its experiences during the pandemic in terms of hospitalizations, critical illness, and deaths. This research highlights changes over several months and waves and the importance of factors such as vaccination, the predominant variant of the virus, and public health interventions in the rise and fall of the outbreaks.

Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.

Objective : Although uncommon, Fusobacterium infections have a wide clinical spectrum, ranging from local pharyngeal infections to septic shock. Our aim was to characterize and analyze the clinical features and outcomes in patients with Fusobacterium infections, and determine which variables were able to predict a poor outcome. Methods : We conducted a retrospective, hospital-based study using the computerized records of a second-level Spanish general hospital, serving a population of 155,000 inhabitants. The cohort was enrolled among patients cared for at the hospital between 2007 and 2016. Demographic, clinical data, microbiological characterization and outcomes at discharge, were analyzed. Results : We collected data for all 26 patients over a 10-year period (annual incidence of 1.78 per 100,000), with an incidence of bacteremia of 0.53 cases per 100,000 population per year. F. nucleatum and F. necrophorum were the most frequent isolations (53.8% and 38.5%, respectively). F. necrophorum was found to be associated with a younger population. Although we found no deaths attributable to Fusobacterium, 15 patients (57%) were found to have severe infections due to this pathogen, and 7 patients (26.9%) were admitted to the Intensive Care Unit (ICU). The only identifiable risk factor for a severe infection (sepsis, septic shock or ICU admission) was the presence of bacteremia. Conclusions : Fusobacterium infections are uncommon. F. necrophorum tends to cause infection in younger individuals, while F. nucleatum has a preference for older patients. The clinical spectrum is wide, ranging from local, non-severe infections, such as sinusitis or pharyngitis, to abscess formation and life-threatening infections.