The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

Abdominal obesity and low physical activity are associated with insulin resistance in overweight adolescents: a cross-sectional study

ABSTRACT: Background: Previous studies have assessed the metabolic changes and lifestyles associated with overweight adolescents. However, these associations are unclear amongst overweight adolescents who have already developed insulin resistance. This study assessed the associations between insulin resistance and anthropometric, metabolic, inflammatory, food consumption, and physical activity variables amongst overweight adolescents. Methods: This cross-sectional study divided adolescents (n = 120) between 10 and 18 years old into 3 groups: an overweight group with insulin resistance (O + IR), an overweight group without insulin resistance (O-IR), and a normal-weight control group (NW). Adolescents were matched across groups based on age, sex, pubertal maturation, and socioeconomic strata. Anthropometric, biochemical, physical activity, and food consumption variables were assessed. Insulin resistance was assessed using homeostatic model assessment (HOMA Calculator Version 2.2.2 from ©Diabetes Trials Unit, University of Oxford), and overweight status was assessed using body mass index according to World Health Organization (2007) references. A chi-square test was used to compare categorical variables. ANOVAs or Kruskal-Wallis tests were used for continuous variables. Multiple linear regression models were used to calculate the probability of the occurrence of insulin resistance based on the independent variables. Results: The risk of insulin resistance amongst overweight adolescents increases significantly when they reach a waist circumference > p95 (OR = 1.9, CIs = 1.3-2.7, p = 0.013) and watch 3 or more hours/day of television (OR = 1.7, CIs = 0.98-2.8, p = 0.033). Overweight status and insulin resistance were associated with higher levels of inflammation (hsCRP ≥1 mg/L) and cardiovascular risk according to arterial indices. With each cm increase in waist circumference, the HOMA index increased by 0.082; with each metabolic equivalent (MET) unit increase in physical activity, the HOMA index decreased by 0.026. Conclusions: Sedentary behaviour and a waist circumference > p90 amongst overweight adolescents were associated with insulin resistance, lipid profile alterations, and higher inflammatory states. A screening that includes body mass index, in waist circumference, and physical activity evaluations of adolescents might enable the early detection of these alterations

Parasite-Guest Infection Modeling: Social Science Applications

In this study we argue that parasite-host infections are a major research topic because of their implications for human health, agriculture and wildlife. The evolution of infection mechanisms is a research topic in areas such as virology and ecology. Mathematical modelling has been an essential tool to obtain a better systematic and quantitative understanding of the processes of parasitic infection that are difficult to discern through strictly experimental approaches. In this article we review recent attempts using mathematical models to discriminate and quantify these infection mechanisms. We also emphasize the challenges that these models could bring to new fields of study such as social sciences and economics