Daily Ards Research Analysis

RATIONALE: Critically ill adults are at risk for a variety of distressing and consequential symptoms both during and after an ICU stay. Management of these symptoms can directly influence outcomes. OBJECTIVES: The objective was to update and expand the Society of Critical Care Medicine's 2018 Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. PANEL DESIGN: The interprofessional inclusive guidelines task force was composed of 24 individuals including nurses, physicians, pharmacists, physiotherapists, psychologists, and ICU survivors. The task force developed evidence-based recommendations using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Conflict-of-interest policies were strictly followed in all phases of the guidelines, including task force selection and voting. METHODS: The task force focused on five main content areas as they pertain to adult ICU patients: anxiety (new topic), agitation/sedation, delirium, immobility, and sleep disruption. Using the GRADE approach, we conducted a rigorous systematic review for each population, intervention, control, and outcome question to identify the best available evidence, statistically summarized the evidence, assessed the quality of evidence, and then performed the evidence-to-decision framework to formulate recommendations. RESULTS: The task force issued five statements related to the management of anxiety, agitation/sedation, delirium, immobility, and sleep disruption in adults admitted to the ICU. In adult patients admitted to the ICU, the task force issued conditional recommendations to use dexmedetomidine over propofol for sedation, provide enhanced mobilization/rehabilitation over usual mobilization/rehabilitation, and administer melatonin. The task force was unable to issue recommendations on the administration of benzodiazepines to treat anxiety, and the use of antipsychotics to treat delirium. CONCLUSIONS: The guidelines task force provided recommendations for pharmacologic management of agitation/sedation and sleep, and nonpharmacologic management of immobility in critically ill adults. These recommendations are intended for consideration along with the patient's clinical status.

BACKGROUND: The application of artificial intelligence (AI) in predicting the mortality of acute respiratory distress syndrome (ARDS) has garnered significant attention. However, there is still a lack of evidence-based support for its specific diagnostic performance. Thus, this systematic review and meta-analysis was conducted to evaluate the effectiveness of AI algorithms in predicting ARDS mortality. METHOD: We conducted a comprehensive electronic search across Web of Science, Embase, PubMed, Scopus, and EBSCO databases up to April 28, 2024. The QUADAS-2 tool was used to assess the risk of bias in the included articles. A bivariate mixed-effects model was applied for the meta-analysis. Sensitivity analysis, meta-regression analysis, and tests for heterogeneity were also performed. RESULTS: Eight studies were included in the analysis. The sensitivity, specificity, and summarized receiver operating characteristic (SROC) of the AI-based model in the validation set were 0.89 (95% CI 0.79-0.95), 0.72 (95% CI 0.65-0.78), and 0.84 (95% CI 0.80-0.87), respectively. For the logistic regression (LR) model, the sensitivity, specificity, and SROC were 0.78 (95% CI 0.74-0.82), 0.68 (95% CI 0.60-0.76), and 0.81 (95% CI 0.77-0.84). The AI model demonstrated superior predictive accuracy compared to the LR model. Notably, the predictive model performed better in patients with moderate to severe ARDS (SAUC: 0.84 [95% CI 0.80-0.87] vs. 0.81 [95% CI 0.77-0.84]). CONCLUSION: The AI algorithms showed superior performance in predicting the mortality of ARDS patients and demonstrated strong potential for clinical application. Additionally, we found that for ARDS, a highly heterogeneous condition, the accuracy of the model is influenced by the severity of the disease.

BACKGROUND: Protective lung strategies (PLS) are guidelines about recent clinical advances that deliver an air volume compatible with the patient's lung capacity and are used to treat acute respiratory distress syndrome. These mechanical ventilation guidelines are not implemented within intensive care units (ICUs) despite strong evidence-based recommendations and a dedicated professional staff. Nurses' familiarity with clinical guidelines can bridge the gap between actual and recommended practice. However, several barriers undermine this process. The objectives of this study were to identify those barriers and explore the knowledge, attitudes, and behavior of ICU nurses regarding the implementation of PLS. METHODS: This was a descriptive, cross-sectional study. The participants were nurses working in the six ICUs of a pediatric tertiary care hospital in Lahore, Pakistan. Using purposive sampling with random selection, the total sample size was 137 nurses. A summative rating scale was used to identify barriers to the implementation of PLS. RESULTS: Overall, the nurses' barrier score was high, with a mean of 66.77±5.36. Across all the barriers subscales, attitude was a much more significant barrier (35.74±3.57) to PLS than behavior (6.53±1.96), perceived knowledge (17.42±2.54), and organizational barriers (7.08±1.39). Knowledge-related barriers were also significantly high. Conclusion: This study identified important barriers to PLS implementation by nurses, including attitudes and knowledge deficits. Understanding those barriers and planning interventions to address them could help to increase adherence to low tidal volume ventilation and improve patient outcomes. Nurses' involvement in mechanical ventilation management could help to safely deliver air volumes compatible with recommendations.