ABSTRACT
Introduction: In the pandemic, porlonged weaning(PW) of mechanical ventilation (wMV) of COVID patients in the intermediate respiratory care unit IRCU was performed. It was necessary to use predictive indexes for(wMV), which do not generate aerosolization of viral particles. Objetive: To develop a oredictive indexes for wMV and tracheostomy decannulation (TCHd) for COVID-19 pneumonia in IRCU. Method(s): The sample consists of 76 serial cases to the IRCU, in 2020 and 2021. Indexes were developed with an oxygenation variable (PaO2/FiO2) or (SatO2/FiO2), respiratory rate (RR) and corrected (C) in based alveolar ventilation (PCO2), the following indexes were developed as predictors of wMV;ventilation-oxygenation index IVOX= (PaFi/RR), IVOX corrected for PCO2 is IVOX-C=(IVOX x Oco2) and with SaFi the SIVOX-C= [(SaFi/RR) x PCO2]. The StatPlus 7.3 program forWindows was used of the Mann-Whitney U (M-WU) comparing their mean values, using binary logistic regression (BLR) and area under curve AUC ROC to compare their predictability. Result(s): Mean age 58,9 +/-14,4;male 53,7% and the stay in the IRCU was 16,7+/- 11 days, mortality of 28,3%(22);received MV (71,0%) 54. wMV was(70,4%)38 and TCHd was (67,3)35. The mean differrences in disconnected and non-diconnected from MV analyzed by M-WU are significante. An BLR model was built to analyze the predictive behavior ofIVOX, IVOX-C and SIVOX-C for wMV. It was observed that the three indexes are predictive, but IVOX-C and SIVOX-C have the highest predictive weight. In turn the AUC ROC was significance. Conclusion(s): The construction of a predictive indexes of wMV and TCHd in this sample the patients who reached the objective.
ABSTRACT
After COVID-19, a full recovery compared to the 2019 situation with a subsequent growth of global air traffic is expected for the next three to six years [1]. Regarding carbon dioxide emissions, Coronavirus lockdown helped the environment to bounce back, but this will be a temporary situation. It is important to continue investigating additional mitigation measurements to achieve long-term environmental benefits, especially after the recovery. At that point, the question of how to reduce aviation's impact on the climate change will certainly arise again, and will re-gain its importance for the world-wide community. Since no fundamental breakthroughs in CO reduction in aviation are expected in the near future, research should focus on several measures to sustainably reduce the environmental impact of aviation. The air traffic management can contribute to an overall reduction of emissions of greenhouse gases by optimizing traffic flows not only towards maximum airspace capacity and maximum efficiency, but also increasingly towards minimum environmental impact. A set of concept elements that were investigated in the frame of the European-Chinese project Greener Air Traffic Operations (GreAT) can already constitute simple and suitable means towards a greener air traffic management. One of these concept elements is the 'Lowest Impact of Deviation' principle: Whenever two flights need to deviate from their most fuel-efficient route, speed or altitude due to de-conflicting, this deviation should be done by the flight with the lowest fuel consumption, and consequently, with the lowest amount of emissions produced with this maneuver. This principle is currently neither reflected in air traffic control regulations, nor in common practices. In the frame of the work presented in this paper, this principle has been further investigated and analyzed with a fast-time simulation, which models a free route airspace environment under ideal conditions. The flights are generated according to a configurable traffic density. De-conflicting is done automatically either by following the standard right of way rules, which also often serve as a guiding principle for air traffic controllers;or by following the 'Lowest Impact of Deviation' principle. Based on EUROCONTROL's Base of Aircraft Data (BADA), the simulation estimates the fuel consumption for each flight as well as for the whole simulation, and consequently also the CO emissions, as a function of traffic density.This paper gives basic information about the principle itself, which is then further tailored down and applied to a free route airspace environment for en-route traffic. It briefly describes the used fast time simulation and illustrates the obtained results. This paper quantifies the theoretical benefit that can be achieved by applying the mentioned principle in the described way. When knowing the traffic density of real air traffic control sectors, the results can easily and directly be transferred to them. © 2021 IEEE.