Comparison of the Evolution of Energy Intensity in Spain and in the EU15. Why is Spain Different?

Energy intensity in Spain has increased since 1990, while the opposite has happened in the EU15. Decomposition analysis of primary energy intensity ratios has been used to identify which are the key sectors driving the Spanish evolution and those responsible for most of the difference with the EU15 energy intensity levels. It is also a useful tool to quantify which countries and economic sectors have had most influence in the EU15 evolution. The analysis shows that the Spanish economic structure is driving the divergence in energy intensity ratios with the EU15, mainly due to the strong transport growth, but also because of the increase of activities linked to the construction boom, and the convergence to EU levels of household energy demand. The results can be used to pinpoint successful EU strategies for energy efficiency that could be used toMassachusetts Institute of Technology. Center for Energy and Environmental Policy Research

Impact on outcomes across KDIGO-2012 AKI criteria according to baseline renal function

Producción CientíficaAcute kidney injury (AKI) and Chronic Kidney Disease (CKD) are global health problems. The pathophysiology of acute-on-chronic kidney disease (AoCKD) is not well understood. We aimed to study clinical outcomes in patients with previous normal (pure acute kidney injury; P-AKI) or impaired kidney function (AoCKD) across the 2012 Kidney Disease Improving Global Outcomes (KDIGO) AKI classification. We performed a retrospective study of patients with AKI, divided into P-AKI and AoCKD groups, evaluating clinical and epidemiological features, distribution across KDIGO-2012 criteria, in-hospital mortality and need for dialysis. One thousand, two hundred and sixty-nine subjects were included. AoCKD individuals were older and had higher comorbidity. P-AKI individuals fulfilled more often the serum creatinine (SCr) > 3.0x criterion in AKI-Stage3, AoCKD subjects reached SCr > 4.0 mg/dL criterion more frequently. AKI severity was associated with in-hospital mortality independently of baseline renal function. AoCKD subjects presented higher mortality when fulfilling AKI-Stage1 criteria or SCr > 3.0x criterion within AKI-Stage3. The relationship between mortality and associated risk factors, such as the net increase of SCr or AoCKD status, fluctuated depending on AKI stage and stage criteria sub-strata. AoCKD patients that fulfil SCr increment rate criteria may be exposed to more severe insults, possibly explaining the higher mortality. AoCKD may constitute a unique clinical syndrome. Adequate staging criteria may help prompt diagnosis and administration of appropriate therapy

Glutathione determination and a study of the activity of glutathione-peroxidase, glutathione-transferase, and glutathione-reductase in renal transplants

Producción CientíficaThe aim of this work is to study the temporary variation of oxidative stress in renal transplants, both in plasma andin erythrocytes (CR). In order to do so, we determined total glutathione (GST) levels, both oxidized (GSSG) and reduced (GSH), and the activity of enzymes, glutathione peroxidase (G-px), glutathione reductase (G-red) and glutathione transferase (GSt), in renal transplant patients. Determinations were made 48 h before the transplant 1 week and 2 weeks after the renal transplant. The results obtainedconfirm a high ‘‘oxidative stress’’ rate, resulting from the equilibrium between the production of free radicals andthe activity of antioxidants, the former being higher proportionally. Immediately after the transplant there is an increase of oxidative stress, which results in an increase of G-red, a marked decrease of G-px in plasma andin erythrocytes (CR) andan abrupt drop both in GST levels in plasma andin GSG (as well as in the [GSH]/[GSSG] relationship). As times goes on, after the transplant, there is a significant improvement in the activity of antioxidant enzymes, but there is no normalization, which is easily seen in the fact that total glutathione levels andthe activity of the various enzymes approach the average values of the control group

A randomized clinical trial for the timing of tracheotomy in critically ill patients: factors precluding inclusion in a single center study

Introduction: We investigated the potential benefits of early tracheotomy performed before day eight of mechanical ventilation (MV) compared with late tracheotomy (from day 14 if it still indicated) in reducing mortality, days of MV, days of sedation and ICU length of stay (LOS). Methods: Randomized controlled trial (RCT) including all-consecutive ICU admitted patients requiring seven or more days of MV. Between days five to seven of MV, before randomization, the attending physician (AP) was consulted about the expected duration of MV and acceptance of tracheotomy according to randomization. Only accepted patients received tracheotomy as result of randomization. An intention to treat analysis was performed including patients accepted for the AP and those rejected without exclusion criteria. Results: A total of 489 patients were included in the RCT. Of 245 patients randomized to the early group, the procedure was performed for 167 patients (68.2%) whereas in the 244 patients randomized to the late group was performed for 135 patients (55.3%) (P <0.004). Mortality at day 90 was similar in both groups (25.7% versus 29.9%), but duration of sedation was shorter in the early tracheotomy group median 11 days (range 2 to 92) days compared to 14 days (range 0 to 79) in the late group (P <0.02). The AP accepted the protocol of randomization in 205 cases (42%), 101 were included in early group and 104 in the late group. In these subgroup of patients (per-protocol analysis) no differences existed in mortality at day 90 between the two groups, but the early group had more ventilator-free days, less duration of sedation and less LOS, than the late group. Conclusions: This study shows that early tracheotomy reduces the days of sedation in patients undergoing MV, but was underpowered to prove any other benefit. In those patients selected by their attending physicians as potential candidates for a tracheotomy, an early procedure can lessen the days of MV, the days of sedation and LOS. However, the imprecision of physicians to select patients who will require prolonged MV challenges the potential benefits of early tracheotomy

Reproducibility and clinical relevance of the ocular response analyzer in nonoperated eyes: corneal biomechanical and tonometric implications

To assess the reproducibility of the ocular response analyzer (ORA) in nonoperated eyes and the impact of corneal biomechanical properties on intraocular pressure (IOP) measurements in normal and glaucomatous eyes. METHODS: In the reliability study, two independent examiners obtained repeated ORA measurements in 30 eyes. In the clinical study, the examiners analyzed ORA and IOP-Goldmann values from 220 normal and 42 glaucomatous eyes. In both studies, Goldmann-correlated IOP measurement (IOP-ORAg), corneal-compensated IOP (IOP-ORAc), corneal hysteresis (CH), and corneal resistance factor (CRF) were evaluated. IOP differences of 3 mm Hg or greater between the IOP-ORAc and IOP-ORAg were considered outcome significant. RESULTS: Intraexaminer intraclass correlation coefficients and interexaminer concordance correlation coefficients ranged from 0.78 to 0.93 and from 0.81 to 0.93, respectively, for all parameters. CH reproducibility was highest, and the IOP-ORAg readings were lowest. The median IOP was 16 mm Hg with the Goldmann tonometer, 14.5 mm Hg with IOP-ORAg (P < 0.001), and 15.7 mm Hg with IOP-ORAc (P < 0.001). Outcome-significant results were found in 77 eyes (29.38%). The IOP-ORAc, CH, and CRF were correlated with age (r = 0.22, P = 0.001; r = -0.23, P = 0.001; r = -0.14, P = 0.02, respectively), but not the IOP-ORAg or IOP-Goldmann. CONCLUSIONS: The ORA provides reproducible corneal biomechanical and IOP measurements in nonoperated eyes. Considering the effect of ORA, corneal biomechanical metrics produces an outcome-significant IOP adjustment in at least one quarter of glaucomatous and normal eyes undergoing noncontact tonometry. Corneal viscoelasticity (CH) and resistance (CRF) appear to decrease minimally with increasing age in healthy adults

Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients

Producción CientíficaPre-transplant prognostic scores help to optimize donor/recipient allocation and to minimize organ discard rates. Since most of these scores come from the US, direct application in non-US populations is not advisable. The Survival Benefit Estimator (SBE), built upon the Estimated Post-Transplant Survival (EPTS) and the Kidney Donor Profile Index (KDPI), has not been externally validated. We aimed to examine SBE in a cohort of Spanish kidney transplant recipients. We designed a retrospective cohortbased study of deceased-donor kidney transplants carried out in two different Spanish hospitals. Unadjusted and adjusted Cox models were applied for patient survival. Predictive models were compared using Harrell’s C statistics. SBE, EPTS and KDPI were independently associated with patient survival (p ≤ 0.01 in all models). Model discrimination measured with Harrell’s C statistics ranged from 0.57 (KDPI) to 0.69 (SBE) and 0.71 (EPTS). After adjustment, SBE presented similar calibration and discrimination power to that of EPTS. SBE tended to underestimate actual survival, mainly among high EPTS recipients/high KDPI donors. SBE performed acceptably well at discriminating posttransplant survival in a cohort of Spanish deceased-donor kidney transplant recipients, although its use as the main allocation guide, especially for high KDPI donors or high EPTS recipients requires further testing.Rio Hortega contract (ISCIII-11453)Fondo de Investigaciones Sanitarias - Fondo Europeo de Desarrollo Regional (project PI16/0617)Redinren (project RD16/0009/001

How to increase technology transfers to developing countries: a synthesis of the evidence

The existing United Nations Framework Convention on Climate Change (UNFCCC) has failed to deliver the rate of low-carbon technology transfer (TT) required to curb GHG emissions in developing countries. This failure has exposed the limitations of universalism and renewed interest in bilateral approaches to TT. Gaps are identified in the UNFCCC approach to climate change TT: missing links between international institutions and the national enabling environments that encourage private investment; a non-differentiated approach for (developing) country and technology characteristics; and a lack of clear measurements of the volume and effectiveness of TTs. Evidence from econometric literature and business experience on climate change TT is reviewed, so as to address the identified pitfalls of the UNFCCC process. Strengths and weaknesses of different methodological approaches are highlighted. International policy recommendations are offered aimed at improving the level of emission reductions achieved through TT

Iniciativas matemático computacionales desde la Universidad de Buenos Aires para contribuir a la toma de decisiones en el contexto del COVID-19

Con la llegada de la pandemia a la Argentina, en marzo de 2020, se creó un grupo multidisciplinario con base en la Universidad de Buenos Aires y amplia trayectoria y experiencia en el desarrollo e investigación de herramientas matemático-computacionales, para colaborar en la toma de decisiones en el contexto del COVID-19. Análisis de datos en el país y en el mundo, simulación de escenarios, y proyectos en territorio fueron parte del desafío encarado. En este artículo se reseñan algunas de las actividades realizadas por el grupo y seanaliza el impacto de ellas.publishedVersionFil: Arrar, Mehrnoosh. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Arrar, Mehrnoosh. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Cálculo; Argentina.Fil: Arrar, Mehrnoosh. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Belloli, Laouen Mayal Louan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales.; Argentina.Fil: Belloli, Laouen Mayal Louan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Cálculo; Argentina.Fil: Belloli, Laouen Mayal Louan. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Bianco, Ana María. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Bianco, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Bianco, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Cálculo; Argentina.Fil: Boechi, Leonardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Boechi, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Boechi, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Cálculo; Argentina.Fil: Castro, Rodrigo Daniel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Duran, Guillermo Alfredo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo; Argentina.Fil: Duran, Guillermo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Duran, Guillermo Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Cálculo; Argentina.Fil: Etchenique, Roberto Argentino. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Etchenique, Roberto Argentino. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina.Fil: Fernández, Natalia Brenda. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Fernández, Natalia Brenda. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biociencias, Biotecnología y Biología Traslacional; Argentina.Fil: Ferrer, Luciana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Ferrer, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Garbervetsky, Diego David. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Garbervetsky, Diego David. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Goldsmit, Rodrigo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Grillo, Carolina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Maidana, Rodrigo. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina.Fil: Mendiluce, Mauricio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Minoldo, Sol. Universidad Nacional de Córdoba. Facultad de Ciencias Sociales; Argentina.Fil: Minoldo, Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Estudios sobre Cultura y Sociedad; Argentina.Fil: Pepino, Leonardo Daniel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Pepino, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Pecker Marcosig, Ezequiel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Pecker Marcosig, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Puerta, Ezequiel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Puerta, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina.Fil: Quiroga, Rodrigo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas; Argentina.Fil: Quiroga, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina.Fil: Solovey, Guillermo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Solovey, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Calculo; Argentina.Fil: Valdora, Marina Silvia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Valdora, Marina Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Calculo; Argentina.Fil: Zapatero, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Zapatero, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigación en Ciencias de la Computación; Argentina

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