Deconstructing delirium in the post anaesthesia care unit.

The course of neuro-cognitive recovery following anaesthesia and surgery is distinctive and poorly understood. Our objective was to identify patterns of neuro-cognitive recovery of the domains routinely assessed for delirium diagnosis in the post anaesthesia care unit (PACU) and to compare them to the cognitive recovery patterns observed in other studies; thereby aiding in the identification of pathological (high risk) patterns of recovery in the PACU. We also compared which of the currently available tests (3D-CAM, CAM-ICU, and NuDESC) is the best to use in PACU. This was a post hoc secondary analysis of data from the Alpha Max study which involved 200 patients aged over 60 years, scheduled for elective surgery under general anaesthesia lasting more than 2 h. These patients were assessed for delirium at 30 min following arrival in the PACU, if they were adequately arousable (Richmond Agitation Sedation Score ≥ -2). All tests for delirium diagnosis (3D-CAM, CAM-ICU, and NuDESC) and the sub-domains assessed were compared to understand temporal recovery of neurocognitive domains. These data were also analysed to determine the best predictor of PACU delirium. We found the incidence of PACU delirium was 35% (3D-CAM). Individual cognitive domains were affected differently. Few individuals had vigilance deficits (6.5%, n = 10 CAM-ICU) or disorganized thinking (19% CAM-ICU, 27.5% 3D-CAM), in contrast attention deficits were common (72%, n = 144) and most of these patients (89.5%, n = 129) were not sedated (RASS ≥ -2). CAM-ICU (27%) and NuDESC (52.8%) detected fewer cases of PACU delirium compared to 3D-CAM. In conclusion, return of neurocognitive function is a stepwise process; Vigilance and Disorganized Thinking are the earliest cognitive functions to return to baseline and lingering deficits in these domains could indicate an abnormal cognitive recovery. Attention deficits are relatively common at 30 min in the PACU even in individuals who appear to be awake. The 3D CAM is a robust test to check for delirium in the PACU.

An Introduction to Causal Diagrams for Anesthesiology Research.

Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects. We use the topical question of whether anesthetic technique (total intravenous anesthesia vs. volatile) affects outcome after cancer surgery as a basis for a series of example directed acyclic graphs, which demonstrate how variables can be chosen to statistically control confounding and other sources of bias. We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem.This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies-without the expense and artificiality of randomized controlled trials.

Transient electroencephalographic alpha power loss during maintenance of general anaesthesia.

BACKGROUND: EEG activity in the extended alpha frequency range (7-17 Hz) during maintenance of general anaesthesia is primarily determined by effect-site concentrations of the hypnotic and analgesic drugs used. Intermittent alpha loss during surgery, unexplained by changes in anaesthetic or opioid concentrations, could represent arousal of the cortex as a result of increased surgical stimulation. METHODS: A generalised linear model was fitted to alpha power recorded from patients undergoing general anaesthesia from induction until waking using three explanatory variables: age-adjusted volatile anaesthetic effect-site concentration, and estimated effect-site propofol and opioid concentrations. Model residuals were decomposed into uncorrelated white noise and a fluctuating auto-correlated trend. Deviations of this local trend were classified as 'unexpected alpha dropout events'. To investigate whether these alpha dropouts might be explained by the effect of noxious stimulation, we related their occurrence to whether a patient was undergoing surgery involving the body cavity or not. RESULTS: Alpha power dropouts occurred in 73 of the 237 patients included in the final analysis (31%, median amplitude of -3.5 dB, duration=103 s). They showed a bimodal or broadly skewed distribution, being more probable soon after initial incision (32%), dropping to around 10% at 1 h, and then again increasing to >30% in operations lasting >3 h. Multivariate analysis showed that alpha dropouts were significantly associated with body cavity surgery (P=0.003) and with longer operations (P<0.001). CONCLUSIONS: A loss of alpha power, unexplained by changes in anaesthetic or opioid concentrations, is suggestive of thalamocortical depolarisation induced by body cavity noxious stimuli, and could provide a measure of nociception during surgery.

Inattentive Delirium vs. Disorganized Thinking: A New Axis to Subcategorize PACU Delirium.

Background: Assessment of patients for delirium in the Post Anesthesia Care Unit (PACU) is confounded by the residual effects of the varied anesthetic and analgesic regimens employed during surgery and by the physiological consequences of surgery such as pain. Nevertheless, delirium diagnosed at this early stage has been associated with adverse clinical outcomes. The last decade has seen the emergence of the confusion assessment method-intensive care unit (CAM-ICU) score as a quick practical method of detecting delirium in clinical situations. Nonetheless, this tool has not been specifically designed for use in this immediate postoperative setting. Methods: Patients enrolled in a larger observational study were administered the CAM-ICU delirium screening tool 15 min after the latter of return of responsiveness to command or arrival in the post-anesthesia care unit. Numerical pain rating scores were also recorded. In addition, we reviewed additional behavioral observations suggestive of disordered thinking, such as hallucinations, a non-reactive eyes-open state, or an inability to state a pain score. Results: Two-hundred and twenty-nine patients underwent CAM-ICU testing in PACU. 33 patients (14%) were diagnosed with delirium according to CAM-ICU criteria; 25 of these were inattentive with low arousal, seven were inattentive with high arousal, and one was inattentive and calm and with disordered thinking. Using our extended criteria an additional eleven patients showed signs of disordered thinking. CAM-ICU delirium was associated with increased length of operation (p = 0.028), but a positive CAM-PACU designation was associated with both increased operation length and age (p = 0.003 and 0.010 respectively). Two of the CAM-ICU positive patients with inattention and high arousal reported high pain scores and were not classified as CAM-PACU positive. Conclusion: Disordered thinking is correlated with older patients and longer operations. The sensitivity of the existing CAM-ICU score in diagnosing delirium or disordered thinking in PACU patients is improved by the inclusion of a few extra criteria, namely: patients having perceptual hallucinations, in an unreactive eyes-open state, or who cannot state a pain score. We present this alternative screening tool for use in the post-anesthetic period, which we have named CAM-PACU.