Background Although COVID-19 has greatly affected many low-income and middle-income countries, detailed information about patients admitted to the intensive care unit (ICU) is still scarce. Our aim was to examine ventilation characteristics and outcomes in invasively ventilated patients with COVID-19 in Argentina, an upper middle-income country. Methods In this prospective, multicentre cohort study (SATICOVID), we enrolled patients aged 18 years or older with RT-PCR-confirmed COVID-19 who were on invasive mechanical ventilation and admitted to one of 63 ICUs in Argentina. Patient demographics and clinical, laboratory, and general management variables were collected on day 1 (ICU admission); physiological respiratory and ventilation variables were collected on days 1, 3, and 7. The primary outcome was all-cause in-hospital mortality. All patients were followed until death in hospital or hospital discharge, whichever occurred first. Secondary outcomes were ICU mortality, identification of independent predictors of mortality, duration of invasive mechanical ventilation, and patterns of change in physiological respiratory and mechanical ventilation variables. The study is registered with ClinicalTrials.gov , NCT04611269 , and is complete. Findings Between March 20, 2020, and Oct 31, 2020, we enrolled 1909 invasively ventilated patients with COVID-19, with a median age of 62 years [IQR 52–70]. 1294 (67·8%) were men, hypertension and obesity were the main comorbidities, and 939 (49·2%) patients required vasopressors. Lung-protective ventilation was widely used and median duration of ventilation was 13 days (IQR 7–22). Median tidal volume was 6·1 mL/kg predicted bodyweight (IQR 6·0–7·0) on day 1, and the value increased significantly up to day 7; positive end-expiratory pressure was 10 cm H 2 O (8–12) on day 1, with a slight but significant decrease to day 7. Ratio of partial pressure of arterial oxygen (PaO 2 ) to fractional inspired oxygen (FiO 2 ) was 160 (IQR 111–218), respiratory system compliance 36 mL/cm H 2 O (29–44), driving pressure 12 cm H 2 O (10–14), and FiO 2 0·60 (0·45–0·80) on day 1. Acute respiratory distress syndrome developed in 1672 (87·6%) of patients; 1176 (61·6%) received prone positioning. In-hospital mortality was 57·7% (1101/1909 patients) and ICU mortality was 57·0% (1088/1909 patients); 462 (43·8%) patients died of refractory hypoxaemia, frequently overlapping with septic shock (n=174). Cox regression identified age (hazard ratio 1·02 [95% CI 1·01–1·03]), Charlson score (1·16 [1·11–1·23]), endotracheal intubation outside of the ICU (ie, before ICU admission; 1·37 [1·10–1·71]), vasopressor use on day 1 (1·29 [1·07–1·55]), D-dimer concentration (1·02 [1·01–1·03]), PaO 2 /FiO ...

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Abstract. One of the most important studies of the earth sciences is that of the Earth's interior structure. There are many sources of data for Earth tomography models: first-arrival passive seismic data (from the actual earthquakes), first-arrival active seismic data (from the seismic experiments), gravity data, and surface waves. Currently, each of these datasets is processed separately, resulting in several different Earth models that have specific coverage areas, different spatial resolutions and varying degrees of accuracy. These models often provide complimentary geophysical information on earth structure (P and S wave velocity structure).Combining the information derived from each requires a joint inversion approach. Designing such joint inversion techniques presents an important theoretical and practical challenge. While such joint inversion methods are being developed, as a first step, we propose a practical solution: to fuse the Earth models coming from different datasets. Since these Earth models have different areas of coverage, model fusion is especially important because some of the resulting models provide better accuracy and/or spatial resolution in some spatial areas and in some depths while other models provide a better accuracy and/or spatial resolution in other areas or depths.The models used in this paper contain measurements that have not only different accuracy and coverage, but also different spatial resolution. We describe how to fuse such models under interval and probabilistic uncertainty.The resulting techniques can be used in other situations when we need to merge models of different accuracy and spatial resolution.