Melissa A. Miller

A bundled approach to intensive care unit (ICU) care known as "Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mobility" (ABCDE) has been proposed, with evidence supporting individual interventions as well as the bundle as a whole. Few data exist on the bundle's implementation and efficacy in community practice. To evaluate self-reported rates of implementation of ABCDE components and their association with outcomes in a state-wide quality improvement collaborative. A written survey was administered to representatives attending the 2012 annual meeting of the Michigan Health and Hospital Association's Keystone ICU collaborative. Respondents reported on their practices regarding spontaneous awakening trials, delirium assessments, and early mobility. There were 212 respondents, a 76% response rate. Wide variation in focus was noted across the assessed components of ABCDE. Only 12% reported having implemented routine spontaneous awakening trials and delirium assessments as well as early mobility, 36% reported not having early mobility as an active goal in their units (nonmovers), and 52% reported attempts at early mobility without both routine sedation interruption and delirium screening implementation. In adjusted models, those who implemented exercise with sedation interruption and delirium screening were 3.5 (95% confidence interval, 1.4-8.6) times more likely to achieve higher levels of exercise in ventilated patients than those who implemented exercise without both sedation interruption and delirium screening. There is incomplete penetrance of aspects of ABCDE across ICUs in this highly motivated statewide quality improvement collaborative. Yet, implementation of exercise in the context of both sedation interruption and delirium screening was associated with improved self-reported mobility outcomes. Effective knowledge translation and implementation strategies may offer substantial benefits to ICU patients.

Introduction: In critical care observational studies, when clinicians administer different treatments to sicker patients, any treatment comparisons will be confounded by differences in severity of illness between patients. We sought to investigate the extent that observational studies assessing treatments are at risk of incorrectly concluding such treatments are ineffective or even harmful due to inadequate risk adjustment. Methods: We performed Monte Carlo simulations of observational studies evaluating the effect of a hypothetical treatment on mortality in critically ill patients. We set the treatment to have either no association with mortality or to have a truly beneficial effect, but more often administered to sicker patients. We varied the strength of the treatment’s true effect, strength of confounding, study size, patient population, and accuracy of the severity of illness risk-adjustment (area under the receiver operator characteristics curve, AUROC). We measured rates in which studies made inaccurate conclusions about the treatment’s true effect due to confounding, and the measured odds ratios for mortality for such false associations. Results: Simulated observational studies employing adequate risk-adjustment were generally able to measure a treatment’s true effect. As risk-adjustment worsened, rates of studies incorrectly concluding the treatment provided no benefit or harm increased, especially when sample size was large (n = 10,000). Even in scenarios of only low confounding, studies using the lower accuracy risk-adjustors (AUROC < 0.66) falsely concluded that a beneficial treatment was harmful. Measured odds ratios for mortality of 1.4 or higher were possible when the treatment’s true beneficial effect was an odds ratio for mortality of 0.6 or 0.8. Conclusions: Large observational studies confounded by severity of illness have a high likelihood of obtaining incorrect results even after employing conventionally “acceptable” levels of risk-adjustment, with large effect sizes that may be construed as true associations. Reporting the AUROC of the risk-adjustment used in the analysis may facilitate an evaluation of a study’s risk for confounding.