ABSTRACT
OBJECTIVES: The objective of the present study was to determine the use of systemic corti-costeroids (SCs) in patients with bronchial asthma using big data analysis. METHODS: We performed an observational, retrospective, noninterventional study based on secondary data captured from free text in the electronic health records. This study was per-formed based on data from the regional health service of Castille-La Mancha (SESCAM), Spain. We performed the analysis using big data and artificial intelligence via Savana® Manager version 3.0. RESULTS: During the study period, 103 667 patients were diagnosed with and treated for asthma at different care levels. The search was restricted to patients aged 10 to 90 years (mean age, 43.5 [95%CI, 43.4-43.7] years). Of these, 59.8% were women. SCs were taken for treatment of asthma by 58 745 patients at some point during the study period. These patients were older, with a higher prevalence of hypertension, dyslipidemia, diabetes, ob-esity, depression, and hiatus hernia. SCs are used frequently in the general population with asthma (31.4% in 2015 and 39.6% in 2019). SCs were prescribed mainly in primary care (59%), allergy (13%) and pulmonology (20%). The frequency of prescription of SCs had a direct impact on the main associated adverse effects. CONCLUSION: In clinical practice, SCs are frequently prescribed to patients with asthma, especially in primary care. Use of SCs is associated with a greater number of adverse events. It is necessary to implement measures to reduce prescription of SCs to patients with asthma, especially in primary care.
ABSTRACT
Analysis of urine samples from COVID-19 patients by 1H NMR reveals important metabolic alterations due to SAR-CoV-2 infection. Previous studies have identified biomarkers in urine that reflect metabolic alterations in COVID-19 patients. We have used 1H NMR to better define these metabolic alterations since this technique allows us to obtain a broad profile of the metabolites present in urine. This technique offers the advantage that sample preparation is very simple and gives us very complete information on the metabolites present. To detect these alterations, we have compared urine samples from COVID-19 patients (n = 35) with healthy people (n = 18). We used unsupervised (Robust PCA) and supervised (PLS-LDA) multivariate analysis methods to evaluate the differences between the two groups: COVID-19 and healthy controls. The differences focus on a group of metabolites related to energy metabolism (glucose, ketone bodies, glycine, creatinine, and citrate) and other processes related to bacterial flora (TMAO and formic acid) and detoxification (hippuric acid). The alterations in the urinary metabolome shown in this work indicate that SARS-CoV-2 causes a metabolic change from a normal situation of glucose consumption towards a gluconeogenic situation and possible insulin resistance.