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Purpose. The purpose of this study was to determine the incidence, risk factors, and impact of delirium on outcomes in ICU patients. In addition, the scoring systems were measured consecutively to characterize how these scores changed with time in patients with and without delirium. Material and Methods. A prospective cohort study enrolling 400 consecutive patients admitted to the ICU between 2018 and 2019 due to trauma or surgery. Patients were followed up for the development of delirium over ICU days using the Confusion Assessment Method (CAM) for the ICU and Intensive Care Delirium Screening Checklist (ICDSC). Cox model logistic regression analysis was used to explore delirium risk factors. Results. Delirium occurred in 108 (27%) patients during their ICU stay, and the median onset of delirium was 4 (IQR 3–4) days after admission. According to multivariate cox regression, the expected hazard for delirium was 1.523 times higher in patients who used mechanical ventilator as compared to those who did not (HR: 1.523, 95% CI: 1.197-2.388, P < 0.001 ). Conclusion. Our findings suggest that an important opportunity for improving the care of critically ill patients may be the determination of modifiable risk factors for delirium in the ICU. In addition, the scoring systems (APACHE IV, SOFA, and RASS) are useful for the prediction of delirium in critically ill patients.

Background. Standardization of clinical practices is an essential part of continuing education of newly registered nurses in the intensive care unit (ICU). The development of educational standards based on evidence can help improve the quality of educational programs and ultimately clinical skills and practices. Objectives. The objectives of the study were to develop a standardized learning curve of arterial blood gas (ABG) sampling competency, to design a checklist for the assessment of competency, to assess the relative importance of predictors and learning patterns of competency, and to determine how many times it is essential to reach a specific level of ABG sampling competency according to the learning curve. Design. A quasi-experimental, nonrandomized, single-group trial with time series design. Participants. All newly registered nurses in the ICU of a teaching hospital of Tehran University of Medical Sciences were selected from July 2016 to April 2018. Altogether, 65 nurses participated in the study; however, at the end, only nine nurses had dropped out due to shift displacement. Methods. At first, the primary checklist was prepared to assess the nurses’ ABG sampling practices and it was finalized after three sessions of the expert panel. The checklist had three domains, including presampling, during sampling, and postsampling of ABG competency. Then, 56 nurses practiced ABG sampling step by step under the supervision of three observers who controlled the processes and they filled the checklists. The endpoint was considered reaching a 95 score on the learning curve. The Poisson regression model was used in order to verify the effective factors of ABG sampling competency. The importance of variables in the prediction of practice scores had been calculated in a linear regression of R software by using the relaimpo package. Results. According to the results, in order to reach a skill level of 55, 65, 75, 85, and 95, nurses, respectively, would need average ABG practice times of 6, 6, 7, 7, and 7. In the linear regression model, demographic variables predict 47.65 percent of changes related to scores in practices but the extent of prediction of these variables totally decreased till 7 practice times, and in each practice, nurses who had the higher primary skill levels gained 1 to 2 skill scores more than those with low primary skills. Conclusions. Utilization of the learning curve could be helpful in the standardization of clinical practices in nursing training and optimization of the frequency of skills training, thus improving the training quality in this field. This trial is registered with NCT02830971.