Articles: mechanical-ventilation.
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J Clin Monit Comput · Dec 2022
Mechanical power of ventilation is associated with mortality in neurocritical patients: a cohort study.
This study aimed to determine the predictive relevance of mechanical power in the clinical outcomes (such as ICU mortality, hospital mortality, 90-day mortality, length of ICU stay, and number of ventilator-free days at day 28) of neurocritical patients. This is a retrospective cohort analysis of an open-access clinical database known as MIMIC-III. The study included patients who had sustained an acute brain injury and required invasive ventilation for at least 24 h. ⋯ Among these patients elevated MP was associated to higher ICU mortality (OR 1.11; 95% CI 1.06-1.17; p < 0.001), enhanced the risk of hospital mortality, prolonged ICU stay, and decreased the number of ventilator-free days. In the subgroup analysis, high MP was associated with ICU mortality regardless of ARDS (OR 1.01, 95% CI 1.00-1.02, p = 0.009; OR 1.01, 95% CI 1.00-1.02, p = 0.018, respectively) or obesity (OR 1.01, 95% CI 1.00-1.02, p = 0.012; OR 1.01, 95% CI 1.01-1.02, p < 0.001, respectively). In neurocritical care patients undergoing invasive ventilation, elevated MP is linked to higher ICU mortality and a variety of other clinical outcomes.
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Critical care nurse · Dec 2022
A Care Step Pathway for the Diagnosis and Treatment of COVID-19-Associated Invasive Fungal Infections in the Intensive Care Unit.
In March 2020, the World Health Organization declared COVID-19, caused by the SARS-CoV-2 virus, a pandemic. Patients with severe cases resulting in hospitalization and mechanical ventilation are at risk for COVID-19-associated pulmonary aspergillosis, an invasive fungal infection, and should be screened for aspergillosis if they have persistent hemodynamic instability and fever. Early detection and treatment of this fungal infection can significantly reduce morbidity and mortality in this population. ⋯ The Care Step Pathway is an effective educational tool to help intensive care unit clinicians consider fungal infection when caring for COVID-19 patients receiving mechanical ventilation in the intensive care unit, especially when the clinical course is deteriorating and antibiotics are ineffective.
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Journal of critical care · Dec 2022
Characterizing intensive care unit rounding teams using meta-data from the electronic health record.
Teamwork is an important determinant of outcomes in the intensive care unit (ICU), yet the nature of individual ICU teams remains poorly understood. We examined whether meta-data in the form of digital signatures in the electronic health record (EHR) could be used to identify and characterize ICU teams. ⋯ EHR meta-data can assist in the characterization of ICU teams, potentially providing novel insight into strategies to measure and improve team function in critical care.
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In this study, we aimed to evaluate and compare the clinical characteristics, laboratory findings, and outcomes of hospitalized patients with and without diabetes along with poorly vs. well-controlled diabetes. ⋯ Patients with diabetes and comorbidities, apart from the glycemic control, should receive intensive monitoring and disease management.
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J Clin Monit Comput · Dec 2022
A model-based approach to generating annotated pressure support waveforms.
Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. ⋯ Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher's exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.