Juan Antonio Vargas

The progressive ageing of the population is associated with a higher prevalence of chronic diseases, many of which occur simultaneously. Comorbidity causes the prescription of a high number of medications, which leads to a higher frequency of adverse effects, interactions, hospital admissions, poorer quality of life and lack of therapeutic compliance. Qualitatively, polypharmacy is understood as the fact of taking more medications than clinically appropriate, and there may be inadequate polypharmacy and adequate polypharmacy. Quantitatively, the most widespread criterion is to place a level of polypharmacy from 5 or more drugs and extreme polypharmacy or hyperpolypharmacy to the use of 10 or more. But more than quantity, quality matters, with the term appropriate polypharmacy becoming relevant. This term recognizes that patients can benefit from multiple medications if the prescription is based on the best possible evidence, reflects the patients’ clinical conditions, and considers potential interactions. It is necessary to actively monitor prescriptions in polymedicated elderly patients to establish the benefit-risk ratio of each indication. The systematic review of medication has been associated with improved adherence and the indication of drugs appropriate to the clinical situation and greater efficiency. Deprescribing consists of reviewing and evaluating the patient’s therapeutic plan with the aim of withdrawing, substituting or reducing the dose of unnecessary drugs or with an unfavorable benefit-risk ratio. Inpatient deprescribing interventions are very likely to reduce the risk of readmission in the short term, although their effect on mortality is uncertain.

Background: Metabolic inflammation (MI), long COVID (LC) and systemic lupus erythematosus (SLE) share some metabolic common manifestations and inflammatory pathophysiological similarities. Health-related quality of life (HRQoL) and metabolic age are indicators of health status. The “METAINFLAMMATION-CM Y2020/BIO-6600” project, a prospective controlled study, aimed to identify differential diagnostic tools and clinical features among three inflammatory conditions by comparing obesity status (low BMI vs. high BMI). Methods: A total of 272 adults of both Caucasian and Hispanic descent, diagnosed with MI, LC or SLE, and a range of BMI, were recruited. Clinical and phenotypic traits were measured to analyze body composition, metabolic and inflammatory markers, HRQoL data, metabolic age and lifestyle habits using a 3 × 2 (disease × BMI) factorial design. Results: Some inflammatory related variables, such as fibrinogen, RDW (red cell blood distribution width), ESR (erythrocyte sedimentation rate) and NLR (neutrophil/lymphocyte ratio), showed effect modifications depending on the BMI and disease type. In relation to HRQoL, the Physical Component Summary (PCS12) showed no relevant changes, while the Mental Component Summary (MCS12) showed a significant effect modification according to the disease type and BMI (p < 0.05). Furthermore, a significant interaction was identified between the disease type and BMI in relation to metabolic age (p = 0.02). Conclusions: Assessing the impact of BMI on these three inflammatory diseases may help to prevent clinical complications and to design personalized treatments, especially for patients with SLE, who have a worse prognosis with an increased BMI compared to the other two inflammatory diseases.

Patients with COVID-19 may develop a hypercoagulable state due to tissue and endothelial injury, produced by an unbalanced immune response. Therefore, an increased number of thromboembolic events has been reported in these patients. The aim of this study is to investigate the presence of antiphospholipid antibodies (aPL) in COVID-19 patients, their role in the development of thrombosis and their relationship with the severity of the disease. In this retrospective study, serum samples from 159 COVID-19 patients and 80 healthy donors were analysed for the presence of aPL. A total of 29 patients (18.2%) and 14 healthy donors (17.5%) were positive for aPL. Nineteen COVID-19 patients (12%) but no healthy donor presented a positive percentage of the IgA isotype aPL. IgA anti-β2-glycoprotein I antibodies (anti-β2GPI) were the most frequent type (6.3%) in patients but was not detected in any healthy donor. The positivity of this antibody was found to be significantly elevated in patients with thromboembolic events (25% vs. 5%, p = 0.029); in fact, patients with positive IgA anti-β2GPI had an incidence of thrombosis over six times higher than those who had normal antibody concentrations [OR (CI 95%) of 6.67 (1.5–30.2), p = 0.014]. Additionally, patients with moderate-severe disease presented a higher aPL positivity than patients with mild disease according to the Brescia (p = 0.029) and CURB-65 (p = 0.011) severity scales. A multivariate analysis showed that positivity for IgA anti-β2GPI is significantly associated with disease severity measured by CURB-65 [OR (CI 95%) 17.8 (1.7–187), p = 0.0016]. In conclusion, COVID-19 patients have a significantly higher positive percentage of the IgA isotype aPL than healthy donors. IgA anti-β2GPI antibodies were the most frequently detected aPL in COVID-19 patients and were associated with thrombosis and severe COVID-19 and are thus proposed as a possible marker to identify high-risk patients.

Background: This study aimed to validate the role of the D-dimer to lymphocyte ratio (DLR) for mortality prediction in a large national cohort of hospitalized coronavirus disease 2019 (COVID-19) patients. Methods: A retrospective, multicenter, observational study that included hospitalized patients due to SARS-CoV-2 infection in Spain was conducted from March 2020 to March 2022. All biomarkers and laboratory indices analyzed were measured once at admission. Results: A total of 10,575 COVID-19 patients were included in this study. The mean age of participants was 66.9 (±16) years, and 58.6% (6202 patients) of them were male. The overall mortality rate was 16.3% (n = 1726 patients). Intensive care unit admission was needed in 10.5% (n = 1106 patients), non-invasive mechanical ventilation was required in 8.8% (n = 923 patients), and orotracheal intubation was required in 7.5% (789 patients). DLR presented a c-statistic of 0.69 (95% CI, 0.68–0.71) for in-hospital mortality with an optimal cut-off above 1. Multivariate analysis showed an independent association for in-hospital mortality for DLR > 1 (adjusted OR 2.09, 95% CI 1.09–4.04; p = 0.03); in the same way, survival analysis showed a higher mortality risk for DLR > 1 (HR 2.24; 95% CI 2.03–2.47; p < 0.01). Further, no other laboratory indices showed an independent association for mortality in multivariate analysis. Conclusions: This study confirmed the usefulness of DLR as a prognostic biomarker for mortality associated with SARS-CoV-2 infection, being an accessible, cost-effective, and easy-to-use biomarker in daily clinical practice.

Systemic autoimmune diseases, such systemic lupus erythematosus (SLE), show an increased morbidity and cardiovascular mortality and a higher incidence of early and accelerated atherosclerosis than the general population that cannot be explained only by the presence of classic risk factors such arterial hypertension, mellitus diabetes, hypercholesterolemia, obesity or smoking, but it seems to depend on factors associated with the disease. The imbalance between vascular damage and repair plays a critical role in the initiation of atherosclerotic lesion. This balance may be broken by factors associated with chronic inflammation, oxidative stress, pharmacological treatments, altered molecules and cells involved in the repair of the endothelium. Research is ongoing to identify biomarkers that can help clinicians to predict which SLE patients are the greatest risk for cardiovascular disease.

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO₂) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO₂ presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO₂ in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO₂ in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

We aimed to evaluate the clinical outcome of Systemic Autoimmune Diseases (SADs) patients hospitalized with COVID-19 in Spain, before the introduction of SARS-CoV-2 vaccines. A nationwide, retrospective and observational analysis of the patients admitted during 2020, based on the ICD10 codes in the National Registry of Hospital Discharges, was performed. Among 117,694 patients, only 892 (0.8%) presented any type of SAD before COVID-19-related admission: Sjogren’s Syndrome constituted 25%, Systemic Vasculitides 21%, Systemic Lupus Erythematosus 19%, Sarcoidosis 17%, Systemic Sclerosis 11%, Mixed and Undifferentiated Connective Tissue Disease 4%, Behçet’s Disease 4% and Inflammatory Myopathies 2%. The in-hospital mortality rate was higher in SAD individuals (20% vs. 16%, p < 0.001). After adjustment by baseline conditions, SADs were not associated with a higher mortality risk (OR = 0.93, 95% CI 0.78–1.11). Mortality in the SADs patients was determined by age (OR = 1.05, 95% CI 1.04–1.07), heart failure (OR = 1.67, 95% CI 1.10–2.49), chronic kidney disease (OR = 1.29, 95% CI 1.05–1.59) and liver disease (OR = 1.97, 95% CI 1.13–3.44). In conclusion, the higher COVID-19 mortality rate seen in SADs patients hospitalized in Spain in 2020 was related to the higher burden of comorbidities, secondary to direct organ damage and sequelae of their condition. Whilst further studies should evaluate the impact of baseline immunosuppression on COVID-19 outcomes in this population, efforts should be focused on the optimal management of SAD to minimize the impact of the organ damage that has been shown to determine COVID-19 prognosis.

COVID-19 has overloaded health system worldwide; thus, it demanded a triage method for an efficient and early discrimination of patients with COVID-19. The objective of this research was to perform a model based on commonly requested hematological variables for an early featuring of patients with COVID-19 form other viral pneumonia. This investigation enrolled 951 patients (mean of age 68 and 56% of male) who underwent a PCR test for respiratory viruses between January 2019 and January 2020, and those who underwent a PCR test for detection of SARS-CoV-2 between February 2020 and October 2020. A comparative analysis of the population according to PCR tests and logistic regression model was performed. A total of 10 variables were found for the characterization of COVID-19: age, sex, anemia, immunosuppression, C-reactive protein, chronic obstructive pulmonary disease, cardiorespiratory disease, metastasis, leukocytes and monocytes. The ROC curve revealed a sensitivity and specificity of 75%. A deep analysis showed low levels of leukocytes in COVID-19-positive patients, which could be used as a primary outcome of COVID-19 detection. In conclusion, this investigation found that commonly requested laboratory variables are able to help physicians to distinguish COVID-19 and perform a quick stratification of patients into different prognostic categories.

Background The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations. Objective The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model. Design Retrospective cohort study with patient data collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide SEMI-COVID-19 Registry. The primary study outcome was in-hospital mortality. As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category. Key Results A total of 17,225 patients were included in the study. Patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (ppp<0.001). The predictive regression model for in-hospital mortality with our proposed combined ordinal scale reached an AUC=0.871, superior to the two models separately. Conclusions The present study proposes a new severity grading scale for COVID-19 hospitalized patients. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date.