Bundle of care decreased ventilator-associated events-implications for ventilator-associated pneumonia prevention

Impact of a national collaborative project to improve the care of mechanically ventilated patients

This prospective quasi-experimental study from the NASAM (National Approach to Standardize and Improve Mechanical Ventilation) collaborative assessed the impact of evidence-based practices including subglottic suctioning, daily assessment for spontaneous awakening trial (SAT), spontaneous breathing trial (SBT), head of bed elevation, and avoidance of neuromuscular blockers unless otherwise indicated. The study outcomes included VAE (primary) and intensive care unit (ICU) mortality. Changes in daily care process measures and outcomes were evaluated using repeated measures mixed modeling. The results were reported as incident rate ratio (IRR) for each additional month with 95% confidence interval (CI). A comprehensive program that included education on evidence-based practices for optimal care of mechanically ventilated patients with real-time benchmarking of daily care process measures to drive improvement in forty-two ICUs from 26 hospitals in Saudi Arabia (>27,000 days of observation). Compliance with subglottic suctioning, SAT and SBT increased monthly during the project by 3.5%, 2.1% and 1.9%, respectively (IRR 1.035, 95%CI 1.007-1.064, p = 0.0148; 1.021, 95% CI 1.010-1.032, p = 0.0003; and 1.019, 95%CI 1.009-1.029, p = 0.0001, respectively). The use of neuromuscular blockers decreased monthly by 2.5% (IRR 0.975, 95%CI 0.953-0.998, p = 0.0341). The compliance with head of bed elevation was high at baseline and did not change over time. Based on data for 83153 ventilator days, VAE rate was 15.2/1000 ventilator day (95%CI 12.6-18.1) at baseline and did not change during the project (IRR 1.019, 95%CI 0.985-1.053, p = 0.2812). Based on data for 8523 patients; the mortality was 30.4% (95%CI 27.4-33.6) at baseline, and decreased monthly during the project by 1.6% (IRR 0.984, 95%CI 0.973-0.996, p = 0.0067). A national quality improvement collaborative was associated with improvements in daily care processes. These changes were associated with a reduction in mortality but not VAEs. Registration The study is registered in clinicaltrials.gov (NCT03790150).

Analysis of diagnostic criteria for ventilator-associated pneumonia: a cohort study

OBJECTIVES

to analyze the diagnostic criteria for ventilator-associated pneumonia recommended by the Brazilian Health Regulatory Agency and the National Healthcare Safety Network/Centers for Disease Control and Prevention, as well as its risk factors.

METHODS

retrospective cohort study carried out in an intensive care unit throughout 12 months, in 2017. Analyses included chi-square, simple linear regression, and Kappa statistical tests and were conducted using Stata 12 software.

RESULTS

the sample was 543 patients who were in the intensive care unit and under mechanical ventilation, of whom 330 (60.9%) were men and 213 (39.1%) were women. Variables such as gender, age, time under mechanical ventilation, and oral hygiene proved to be significant risk factors for the development of ventilator-associated pneumonia.

CONCLUSIONS

patients submitted to mechanical ventilation need to be constantly evaluated so the used diagnostic methods can be accurate and applied in an objective and standardized way in Brazilian hospitals.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.