Articles: mechanical-ventilation.
-
Frontiers in medicine · Jan 2021
Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database.
Background: Mechanically ventilated patients in the intensive care unit (ICU) have high mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA), widely used in the general ICU population. We aimed to establish prediction scores on mechanically ventilated patients with the combination of these disease severity scores and other features available on the first day of admission. ⋯ The top five predictors were age, respiratory dysfunction, SAPS II score, maximum hemoglobin, and minimum lactate. Conclusion: The current study indicates that models with the risk of factors on the first day could be successfully established for predicting mortality in ventilated patients. The XGBoost model performs best among the seven machine learning models.
-
The main objective of this study was to evaluate trends in set tidal volumes across all adult ICUs at a large academic medical center over 6 years, with a focus on adherence to lung-protective ventilation (≤ 8-cc/kg ideal body weight). A secondary objective was to survey providers on their perceptions of lung-protective ventilation and barriers to its implementation.
-
Case 1: A 65-year-old man with novel coronavirus infection (COVID-19) complicated with acute respiratory failure. On admission, the patient was started on favipiravir and corticosteroid. However, due to a lack of significant improvement, he was introduced to mechanical ventilation and extracorporeal membrane oxygenation (ECMO). ⋯ Due to progressive respiratory failure, the patient underwent mechanical ventilation and ECMO. The patient recovered without complications. We successfully treated these severe cases with a multimodal combination of pharmacological and non-pharmacological supportive therapy.
-
Frontiers in medicine · Jan 2021
Identifying Clinical Phenotypes in Moderate to Severe Acute Respiratory Distress Syndrome Related to COVID-19: The COVADIS Study.
Objectives: Different phenotypes have been identified in acute respiratory distress syndrome (ARDS). Existence of several phenotypes in coronavirus disease (COVID-19) related acute respiratory distress syndrome is unknown. We sought to identify different phenotypes of patients with moderate to severe ARDS related to COVID-19. ⋯ Conclusions: In COVID-19 patients with moderate to severe ARDS, we identified three clinical phenotypes. One of these included older people with comorbidities who had a fulminant course of disease with poor prognosis. Requirement of different treatments and ventilatory strategies for each phenotype needs further investigation.