Critically ill COVID-19 patients with acute respiratory failure, in the intensive care unit (ICU), often feature high respiratory drive, determining large inspiratory efforts resulting in high pressures and global and regional over-distention, leading to lung injury. SARS-CoV-2 neurotropic-penetration in control centers in medulla oblongata might contribute to dysregulation and to excessively high respiratory drive observed in these patients. These pathophysiological conditions may often lead to the development of patient-ventilator asynchronies in aptients under mechanical ventilation, again leading to high tidal volumes and increased lung injury. These phenomena can contribute to prolonged duration of mechanical ventilation and ICU length of stay, but also can result in long term adverse outcomes like emotional/psychological and cognitive sequelae. All them compromising the quality of life of critically ill survivors after ICU discharge. The investigators will conduct a multicenter study in adult critically ill COVID-19 patients with hypoxemic respiratory failure, aiming to: 1) characterize incidence and clustering of high respiratory drive by developing algorithms, 2) apply artificial intelligence in respiratory signals to identify potentially harmful patient-ventilator interactions, 3) characterize cognitive and emotional sequelae in critically ill COVID-19 survivors after ICU discharge and 4) identify sets of genes and transcriptomic signatures whose quantified expression predisposed to asynchronies and cognitive impairment in critically ill COVID-19 patients.
Study Type
OBSERVATIONAL
Enrollment
126
Candelaria De Haro
Sabadell, Barcelona, Spain
RECRUITINGFundació Althaia
Manresa, Spain
RECRUITINGHospital Universitario Central de Asturias
Oviedo, Spain
RECRUITINGRespiratory drive
To characterize the high respiratory drive phenomena in critically ill COVID-19 patients undergoing mechanical ventilation.
Time frame: From day 1 at ICU until the day were the criteria of PaFi > 300 is met, up to 30 days
Cluster of high respiratory drive
To describe the incidence and clustering of high respiratory drive throughout mechanical ventilation period by the development of specific algorithms.
Time frame: From day 1 of mechanical ventilation until the day of mechanical ventilation discontinuation, up to 30 days
Artificial intelligence algorithms
To apply artificial intelligence (machine learning, deep learning, pattern/image recognition and entropy) in physiologic respiratory signals to identify potentially harmful patient-ventilator interactions.
Time frame: From day 1 of mechanical ventilation until the day of mechanical ventilation discontinuation, up to 30 days
Neurocognitive disorders
To characterize cognitive and emotional sequelae in critically ill COVID-19 survivors at 1 month and 1 year after ICU discharge.
Time frame: 1 month after ICU discharge and 1 year after ICU discharge
Gene expression
Application of massive sequencing of gene expression and circulating micro-RNA in blood samples to identify sets of genes and c-miRNA whose quantified expression is related to ventilatory asynchronies and cognitive and emotional impairment in critically ill COVID-19 patients.
Time frame: day 1 of ICU admission
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