Open Access

BACKGROUND: Intraoperative electroencephalography (EEG) signatures related to the development of postoperative delirium (POD) in older patients are frequently studied. However, a broad analysis of the EEG dynamics including preoperative, postinduction, intraoperative and postoperative scenarios and its correlation to POD development is still lacking. We explored the relationship between perioperative EEG spectra-derived parameters and POD development, aiming to ascertain the diagnostic utility of these parameters to detect patients developing POD. METHODS: Patients aged ≥65 years undergoing elective surgeries that were expected to last more than 60 minutes were included in this prospective, observational single center study (Biomarker Development for Postoperative Cognitive Impairment [BioCog] study). Frontal EEGs were recorded, starting before induction of anesthesia and lasting until recovery of consciousness. EEG data were analyzed based on raw EEG files and downloaded excel data files. We performed multitaper spectral analyses of relevant EEG epochs and further used multitaper spectral estimate to calculate a corresponding spectral parameter. POD assessments were performed twice daily up to the seventh postoperative day. Our primary aim was to analyze the relation between the perioperative spectral edge frequency (SEF) and the development of POD. RESULTS: Of the 237 included patients, 41 (17%) patients developed POD. The preoperative EEG in POD patients was associated with lower values in both SEF (POD 13.1 ± 4.6 Hz versus no postoperative delirium [NoPOD] 17.4 ± 6.9 Hz; P = .002) and corresponding γ-band power (POD −24.33 ± 2.8 dB versus NoPOD −17.9 ± 4.81 dB), as well as reduced postinduction absolute α-band power (POD −7.37 ± 4.52 dB versus NoPOD −5 ± 5.03 dB). The ratio of SEF from the preoperative to postinduction state (SEF ratio) was ~1 in POD patients, whereas NoPOD patients showed a SEF ratio >1, thus indicating a slowing of EEG with loss of unconscious. Preoperative SEF, preoperative γ-band power, and SEF ratio were independently associated with POD (P = .025; odds ratio [OR] = 0.892, 95% confidence interval [CI], 0.808–0.986; P = .029; OR = 0.568, 95% CI, 0.342–0.944; and P = .009; OR = 0.108, 95% CI, 0.021–0.568, respectively). CONCLUSIONS: Lower preoperative SEF, absence of slowing in EEG while transitioning from preoperative state to unconscious state, and lower EEG power in relevant frequency bands in both these states are related to POD development. These findings may suggest an underlying pathophysiology and might be used as EEG-based marker for early identification of patients at risk to develop POD.

Background Beta-blocker (BB) therapy plays a central role in the treatment of cardiovascular diseases. An increasing number of patients with cardiovascular diseases undergoe noncardiac surgery, where opioids are an integral part of the anesthesiological management. There is evidence to suggest that short-term intravenous BB therapy may influence perioperative opioid requirements due to an assumed cross-talk between G-protein coupled beta-adrenergic and opioid receptors. Whether chronic BB therapy could also have an influence on perioperative opioid requirements is unclear. Methods A post hoc analysis of prospectively collected data from a multicenter observational (BioCog) study was performed. Inclusion criteria consisted of elderly patients (≥ 65 years) undergoing elective noncardiac surgery as well as total intravenous general anesthesia without the use of regional anesthesia and duration of anesthesia ≥ 60 min. Two groups were defined: patients with and without BB in their regular preopreative medication. The administered opioids were converted to their respective morphine equivalent doses. Multiple regression analysis was performed using the morphine-index to identify independent predictors. Results A total of 747 patients were included in the BioCog study in the study center Berlin. 106 patients fulfilled the inclusion criteria. Of these, 37 were on chronic BB. The latter were preoperatively significantly more likely to have arterial hypertension (94.6%), chronic renal failure (27%) and hyperlipoproteinemia (51.4%) compared to patients without BB. Both groups did not differ in terms of cumulative perioperative morphine equivalent dose (230.9 (BB group) vs. 214.8 mg (Non-BB group)). Predictive factors for increased morphine-index were older age, male sex, longer duration of anesthesia and surgery of the trunk. In a model with logarithmised morphine index, only gender (female) and duration of anesthesia remained predictive factors. Conclusions Chronic BB therapy was not associated with a reduced perioperative opioid consumption.

Background Postoperative delirium (POD) is a frequent and serious complication after surgery. Evidence of a relationship between anticholinergic medication and the development of delirium is inconclusive, but studies on POD are rare. Objectives The objective of this study was to evaluate the anticholinergic load of preoperative medication in older adult patients and its association with the development of POD. Methods This investigation was part of the European BioCog project (http://www.biocog.eu), a prospective multicenter observational study in older adult surgical patients (ClinicalTrials.gov identifier: NCT02265263, 15 October 2014). Patients with a Mini–Mental State Examination score ≤ 23 points were excluded. POD was assessed up to 7 days after surgery using the Nursing Delirium Screening Scale, Confusion Assessment Method and a patient chart review. The preoperative anticholinergic load was calculated using the Anticholinergic Drug Scale (ADS), the Anticholinergic Risk Scale (ARS) and the Anticholinergic Cognitive Burden Scale (ACBS), and associations with POD were analyzed using logistic regression analysis adjusting for age, comorbidities, duration of anesthesia and number of drugs used. Results In total, 837 participants were included for analysis, and 165 patients (19.7%) fulfilled the criteria of POD. After adjusting for confounders, we found no association between preoperative anticholinergic load and the development of POD (ADS [points] odds ratio [OR] 0.928; 95% confidence interval [CI] 0.749–1.150; ARS [points] OR 0.832; 95% CI 0.564–1.227; ACBS [points] OR 1.045; 95% CI 0.842–1.296). Conclusion This study found no association between the anticholinergic load of drugs used preoperatively and the development of POD in older adult patients without severe preexisting cognitive impairment. Future analyses should examine the influence of intra- and postoperative administration of anticholinergic drugs as well as dosages of and interactions between medications.

Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals. Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question. Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.