Spread in climate policy scenarios unravelled

Analysis of climate policy scenarios has become an important tool for identifying mitigation strategies, as shown in the latest Intergovernmental Panel on Climate Change Working Group III report1. The key outcomes of these scenarios differ substantially not only because of model and climate target differences but also because of different assumptions on behavioural, technological and socio-economic developments2-4. A comprehensive attribution of the spread in climate policy scenarios helps policymakers, stakeholders and scientists to cope with large uncertainties in this field. Here we attribute this spread to the underlying drivers using Sobol decomposition5, yielding the importance of each driver for scenario outcomes. As expected, the climate target explains most of the spread in greenhouse gas emissions, total and sectoral fossil fuel use, total renewable energy and total carbon capture and storage in electricity generation. Unexpectedly, model differences drive variation of most other scenario outcomes, for example, in individual renewable and carbon capture and storage technologies, and energy in demand sectors, reflecting intrinsic uncertainties about long-term developments and the range of possible mitigation strategies. Only a few scenario outcomes, such as hydrogen use, are driven by other scenario assumptions, reflecting the need for more scenario differentiation. This attribution analysis distinguishes areas of consensus as well as strong model dependency, providing a crucial step in correctly interpreting scenario results for robust decision-making.

Factors associated with a persistent delirium in the intensive care unit: A retrospective cohort study

PURPOSE

To explore differences between ICU patients with persistent delirium (PD), non-persistent delirium (NPD) and no delirium (ND), and to determine factors associated with PD.

MATERIALS AND METHODS

Retrospective cohort study including all ICU adults admitted for ≥12 h (January 2015-February 2020), assessable for delirium and followed during their entire hospitalization. PD was defined as ≥14 days of delirium. Factors associated with PD were determined using multivariable logistic regression analysis.

RESULTS

Out of 10,295 patients, 3138 (30.5%) had delirium, and 284 (2.8%) had PD. As compared to NPD (n = 2854, 27.7%) and ND (n = 7157, 69.5%), PD patients were older, sicker, more physically restrained, longer comatose and mechanically ventilated, had a longer ICU and hospital stay, more ICU readmissions and a higher mortality rate. Factors associated with PD were age (adjusted odds ratio [aOR] 1.03; 95% confidence interval [CI] 1.02-1.04); emergency surgical (aOR 1.84; 95%CI 1.26-2.68) and medical (aOR 1.57; 95%CI 1.12-2.21) referral, mean Sequential Organ Failure Assessment (SOFA) score before delirium onset (aOR 1.18; 95%CI 1.13-1.24) and use of physical restraints (aOR 5.02; 95%CI 3.09-8.15).

CONCLUSIONS

Patients with persistent delirium differ in several characteristics and had worse short-term outcomes. Physical restraints were the most strongly associated with PD.

Non-invasive method to detect high respiratory effort and transpulmonary driving pressures in COVID-19 patients during mechanical ventilation

BACKGROUND

High respiratory drive in mechanically ventilated patients with spontaneous breathing effort may cause excessive lung stress and strain and muscle loading. Therefore, it is important to have a reliable estimate of respiratory effort to guarantee lung and diaphragm protective mechanical ventilation. Recently, a novel non-invasive method was found to detect excessive dynamic transpulmonary driving pressure (∆PL) and respiratory muscle pressure (Pmus) with reasonable accuracy. During the Coronavirus disease 2019 (COVID-19) pandemic, it was impossible to obtain the gold standard for respiratory effort, esophageal manometry, in every patient. Therefore, we investigated whether this novel non-invasive method could also be applied in COVID-19 patients.

METHODS

∆PL and Pmus were derived from esophageal manometry in COVID-19 patients. In addition, ∆PL and Pmus were computed from the occlusion pressure (∆Pocc) obtained during an expiratory occlusion maneuver. Measured and computed ∆PL and Pmus were compared and discriminative performance for excessive ∆PL and Pmus was assessed. The relation between occlusion pressure and respiratory effort was also assessed.

RESULTS

Thirteen patients were included. Patients had a low dynamic lung compliance [24 (20-31) mL/cmH2O], high ∆PL (25 ± 6 cmH2O) and high Pmus (16 ± 7 cmH2O). Low agreement was found between measured and computed ∆PL and Pmus. Excessive ∆PL > 20 cmH2O and Pmus > 15 cmH2O were accurately detected (area under the receiver operating curve (AUROC) 1.00 [95% confidence interval (CI), 1.00-1.00], sensitivity 100% (95% CI, 72-100%) and specificity 100% (95% CI, 16-100%) and AUROC 0.98 (95% CI, 0.90-1.00), sensitivity 100% (95% CI, 54-100%) and specificity 86% (95% CI, 42-100%), respectively). Respiratory effort calculated per minute was highly correlated with ∆Pocc (for esophageal pressure time product per minute (PTPes/min) r2 = 0.73; P = 0.0002 and work of breathing (WOB) r2 = 0.85; P < 0.0001).

CONCLUSIONS

∆PL and Pmus can be computed from an expiratory occlusion maneuver and can predict excessive ∆PL and Pmus in patients with COVID-19 with high accuracy.

Afforestation for climate change mitigation: Potentials, risks and trade-offs

Afforestation is considered a cost-effective and readily available climate change mitigation option. In recent studies afforestation is presented as a major solution to limit climate change. However, estimates of afforestation potential vary widely. Moreover, the risks in global mitigation policy and the negative trade-offs with food security are often not considered. Here we present a new approach to assess the economic potential of afforestation with the IMAGE 3.0 integrated assessment model framework. In addition, we discuss the role of afforestation in mitigation pathways and the effects of afforestation on the food system under increasingly ambitious climate targets. We show that afforestation has a mitigation potential of 4.9 GtCO₂ /year at 200 US$/tCO₂ in 2050 leading to large-scale application in an SSP2 scenario aiming for 2°C (410 GtCO₂ cumulative up to 2100). Afforestation reduces the overall costs of mitigation policy. However, it may lead to lower mitigation ambition and lock-in situations in other sectors. Moreover, it bears risks to implementation and permanence as the negative emissions are increasingly located in regions with high investment risks and weak governance, for example in Sub-Saharan Africa. Afforestation also requires large amounts of land (up to 1,100 Mha) leading to large reductions in agricultural land. The increased competition for land could lead to higher food prices and an increased population at risk of hunger. Our results confirm that afforestation has substantial potential for mitigation. At the same time, we highlight that major risks and trade-offs are involved. Pathways aiming to limit climate change to 2°C or even 1.5°C need to minimize these risks and trade-offs in order to achieve mitigation sustainably.

Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies

A rapid and deep decarbonization of power supply worldwide is required to limit global warming to well below 2 °C. Beyond greenhouse gas emissions, the power sector is also responsible for numerous other environmental impacts. Here we combine scenarios from integrated assessment models with a forward-looking life-cycle assessment to explore how alternative technology choices in power sector decarbonization pathways compare in terms of non-climate environmental impacts at the system level. While all decarbonization pathways yield major environmental co-benefits, we find that the scale of co-benefits as well as profiles of adverse side-effects depend strongly on technology choice. Mitigation scenarios focusing on wind and solar power are more effective in reducing human health impacts compared to those with low renewable energy, while inducing a more pronounced shift away from fossil and toward mineral resource depletion. Conversely, non-climate ecosystem damages are highly uncertain but tend to increase, chiefly due to land requirements for bioenergy.

Cardiac dysfunction after aneurysmal subarachnoid hemorrhage: relationship with outcome

OBJECTIVE

To assess whether cardiac abnormalities after aneurysmal subarachnoid hemorrhage (aSAH) are associated with delayed cerebral ischemia (DCI) and clinical outcome, independent from known clinical risk factors for these outcomes.

METHODS

In a prospective, multicenter cohort study, we performed echocardiography and ECG and measured biochemical markers for myocardial damage in patients with aSAH. Outcomes were DCI, death, and poor clinical outcome (death or dependency for activities of daily living) at 3 months. With multivariable Poisson regression analysis, we calculated risk ratios (RRs) with corresponding 95% confidence intervals. We used survival analysis to assess cumulative percentage of death in patients with and without echocardiographic wall motion abnormalities (WMAs).

RESULTS

We included 301 patients with a mean age of 57 years; 70% were women. A wall motion score index ≥1.2 had an adjusted RR of 1.2 (0.9-1.6) for DCI, 1.9 (1.1-3.3) for death, and 1.8 (1.1-3.0) for poor outcome. Midventricular WMAs had adjusted RRs of 1.1 (0.8-1.4) for DCI, 2.3 (1.4-3.8) for death, and 2.2 (1.4-3.5) for poor outcome. For apical WMAs, adjusted RRs were 1.3 (1.1-1.7) for DCI, 1.5 (0.8-2.7) for death, and 1.4 (0.8-2.5) for poor outcome. Elevated troponin T levels, ST-segment changes, and low voltage on the admission ECGs had a univariable association with death but were not independent predictors for outcome.

CONCLUSION

WMAs are independent risk factors for clinical outcome after aSAH. This relation is partly explained by a higher risk of DCI. Further study should aim at treatment strategies for these aSAH-related cardiac abnormalities to improve clinical outcome.