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respiratory distress syndrome experienced a higher rate of propofol-associated hypertriglyceridemia than noncoronavirus disease 2019 acute respiratory distress syndrome patients, even after accounting for differences in propofol administration.

Patients with coronavirus disease 2019 acute respiratory distress syndrome experienced a higher rate of propofol-associated hypertriglyceridemia than noncoronavirus disease 2019 acute respiratory distress syndrome patients, even after accounting for differences in propofol administration.

The purpose of this scoping review is to provide a synthesis of the available literature on implementation science in critical care settings. Specifically, we aimed to identify the evidence-based practices selected for implementation, the frequency and type of implementation strategies used to foster change, and the process and clinical outcomes associated with implementation.

A librarian-assisted search was performed using three electronic databases.

Articles that reported outcomes aimed at disseminating, implementing, or sustaining an evidence-based intervention or practice, used established implementation strategies, and were conducted in a critical care unit were included.

Two reviewers independently screened titles, abstracts, and full text of articles to determine eligibility. Data extraction was performed using customized fields established a priori within a systematic review software system.

Of 1,707 citations, 82 met eligibility criteria. Studies included prospective research investigationsthe most effective mechanisms to integrate and sustain these practices across diverse critical care settings and teams.

The field of critical care has experienced slow but steady gains in the number of investigations specifically guided by implementation science. However, given the exponential growth of evidence-based practices and guidelines in this same period, much work remains to critically evaluate the most effective mechanisms to integrate and sustain these practices across diverse critical care settings and teams.

To develop an algorithm that predicts an individualized risk of severe coronavirus disease 2019 illness (i.e., ICU admission or death) upon testing positive for coronavirus disease 2019.

A retrospective cohort study.

Cleveland Clinic Health System.

Those hospitalized with coronavirus disease 2019 between March 8, 2020, and July 13, 2020.

A temporal coronavirus disease 2019 test positive cut point of June 1 was used to separate the development from validation cohorts. RIN1 in vitro Fine and Gray competing risk regression modeling was performed.

The development set contained 4,520 patients who tested positive for coronavirus disease 2019 between March 8, 2020, and May 31, 2020. The validation set contained 3,150 patients who tested positive between June 1 and July 13. Approximately 9% of patients were admitted to the ICU or died of coronavirus disease 2019 within 2 weeks of testing positive. A prediction cut point of 15% was proposed. Those who exceed the cutoff have a 21% chance of future severe coronavirus disease 2019, whereas those who do not have a 96% chance of avoiding the severe coronavirus disease 2019. In addition, application of this decision rule identifies 89% of the population at the very low risk of severe coronavirus disease 2019 (< 4%).

We have developed and internally validated an algorithm to assess whether someone is at high risk of admission to the ICU or dying from coronavirus disease 2019, should he or she test positive for coronavirus disease 2019. This risk should be a factor in determining resource allocation, protection from less safe working conditions, and prioritization for vaccination.

We have developed and internally validated an algorithm to assess whether someone is at high risk of admission to the ICU or dying from coronavirus disease 2019, should he or she test positive for coronavirus disease 2019. This risk should be a factor in determining resource allocation, protection from less safe working conditions, and prioritization for vaccination.

Elucidate how the degree of ventilator-induced lung injury due to atelectrauma that is produced in the injured lung during mechanical ventilation is determined by both the timing and magnitude of the airway pressure profile.

A computational model of the injured lung provides a platform for exploring how mechanical ventilation parameters potentially modulate atelectrauma and volutrauma. This model incorporates the time dependence of lung recruitment and derecruitment, and the time-constant of lung emptying during expiration as determined by overall compliance and resistance of the respiratory system.

Computational model.

Simulated scenarios representing patients with both normal and acutely injured lungs.

Protective low-tidal volume ventilation (Low-Vt) of the simulated injured lung avoided atelectrauma through the elevation of positive end-expiratory pressure while maintaining fixed tidal volume and driving pressure. In contrast, airway pressure release ventilation avoided atelectrauma by incorporat, respectively. can be based on exhalation flow values, which may provide a patient-specific approach to protective ventilation.

Summarize performance and development of ICU delirium-prediction models published within the past 5 years.

Systematic electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and Cumulative Index to Nursing and Allied Health Literature to identify peer-reviewed studies.

Eligible studies were published in English during the past 5 years that specifically addressed the development, validation, or recalibration of delirium-prediction models in adult ICU populations.

Screened citations were extracted independently by three investigators with a 42% overlap to verify consistency using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

Eighteen studies featuring 23 distinct prediction models were included. Model performance varied greatly, as assessed by area under the receiver operating characteristic curve (0.62-0.94), specificity (0.50-0.97), and sensitivity (0.45-0.96). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians.