A5008464879- Sepsis Diagnosis and Treatment
- Intensive Care Unit Cognitive Disorders
- COVID-19 Clinical Research Studies
- Respiratory Support and Mechanisms
- Machine Learning in Healthcare
- COVID-19 diagnosis using AI
- Renal function and acid-base balance
- Sports, Gender, and Society
- Sports and Physical Education Studies
- Cardiac Arrest and Resuscitation
- Digital Games and Media
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Phonocardiography and Auscultation Techniques
- Pressure Ulcer Prevention and Management
- Nosocomial Infections in ICU
- Viral Infections and Immunology Research
- Thermal Regulation in Medicine
- Hemodynamic Monitoring and Therapy
- Emergency and Acute Care Studies
Hospital Universitari Joan XXIII de Tarragona
2020-2024
Universitat Rovira i Virgili
2024
Institut d'Investigació Sanitària Pere Virgili
2021-2024
The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to decision-making that improves prognostic outcomes.Prospective, multicenter, observational study critically ill confirmed disease acute respiratory failure admitted from 63 ICUs Spain. objective was utilize an unsupervised clustering analysis derive analyze patient's risk. Patient features including demographics data...
Some unanswered questions persist regarding the effectiveness of corticosteroids for severe coronavirus disease 2019 (COVID-19) patients. We aimed to assess clinical effect on intensive care unit (ICU) mortality among mechanically ventilated COVID-19-associated acute respiratory distress syndrome (ARDS)
Abstract While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal lactate. A better understanding this discordance may help differentiate sepsis phenotypes and offer clues to pathophysiology. Three intensive care unit datasets were utilized. Adult in the highest quartile illness severity scores identified. Logistic regression, random forests, partial least square models built for each data set. Features differentiating...
Invasive Mechanical Ventilation (IMV) in Intensive Care Units (ICU) significantly increases the risk of Ventilator-Induced Lung Injury (VILI), necessitating careful management mechanical power (MP). This study aims to develop a real-time predictive model MP utilizing Artificial Intelligence mitigate VILI.
Abstract Background Over the past decade, numerous studies on potential factors contributing to ventilation-induced lung injury have been carried out. Mechanical power has pointed out as parameter that encloses all injury-contributing factors. However, conducted date provide data regarding mechanical during early hours of ventilation may not correspond real scenario. Methods Retrospective observational study at a single center in Spain. Patients admitted intensive care unit, > o = 18...
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI
Abstract Background Over the past decade, numerous studies on potential factors contributing to ventilation-induced lung injury have been carried out. Mechanical power has pointed out as parameter that encloses all injury-contributing factors. However, conducted date provide data regarding mechanical during early hours of ventilation may not accurately reflect impact throughout period ventilatory support intensive care unit mortality. Methods Retrospective observational study at a single...
Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study critically ill confirmed disease acute respiratory failure admitted from 63 Units(ICU) Spain. objective was analyze patient’s risk utilize Machine Learning(ML) derive phenotypes. Patient...
Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to decision-making that improves prognostic outcomes. objective was analyze patient's risk utilize Machine Learning(ML) derive phenotypes.Methods: Prospective, multicenter, observational study critically ill confirmed disease acute respiratory failure admitted from 63 Units(ICU) Spain. Patient features...
Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study critically ill confirmed disease acute respiratory failure admitted from 63 Units(ICU) Spain. objective was utilize an unsupervised clustering analysis derive analyze patient’s risk. Patient...
Abstract Background : The steroids are currently used as standard treatment for severe COVID-19. However, the evidence is weak. Our aim to determine if use of corticosteroids was associated with Intensive Care Unit (ICU) mortality among whole population and pre-specified clinical phenotypes. Methods A secondary analysis derived from multicenter, observational study adult critically ill patients confirmed COVID-19 disease admitted 63 ICUs in Spain. Three phenotypes were by non-supervised...