Background Intraoperative burst-suppression is associated with postoperative delirium. Whether this association is causal remains unclear. Therefore, the authors investigated whether burst-suppression during cardiopulmonary bypass (CPB) mediates the effects of known delirium risk factors on postoperative delirium. Methods This was a retrospective cohort observational substudy of the Minimizing ICU [intensive care unit] Neurological Dysfunction with Dexmedetomidine-induced Sleep (MINDDS) trial. The authors analyzed data from patients more than 60 yr old undergoing cardiac surgery (n = 159). Univariate and multivariable regression analyses were performed to assess for associations and enable causal inference. Delirium risk factors were evaluated using the abbreviated Montreal Cognitive Assessment and Patient-Reported Outcomes Measurement Information System questionnaires for applied cognition, physical function, global health, sleep, and pain. The authors also analyzed electroencephalogram data (n = 141). Results The incidence of delirium in patients with CPB burst-suppression was 25% (15 of 60) compared with 6% (5 of 81) in patients without CPB burst-suppression. In univariate analyses, age (odds ratio, 1.08 [95% CI, 1.03 to 1.14]; P = 0.002), lowest CPB temperature (odds ratio, 0.79 [0.66 to 0.94]; P = 0.010), alpha power (odds ratio, 0.65 [0.54 to 0.80]; P < 0.001), and physical function (odds ratio, 0.95 [0.91 to 0.98]; P = 0.007) were associated with CPB burst-suppression. In separate univariate analyses, age (odds ratio, 1.09 [1.02 to 1.16]; P = 0.009), abbreviated Montreal Cognitive Assessment (odds ratio, 0.80 [0.66 to 0.97]; P = 0.024), alpha power (odds ratio, 0.75 [0.59 to 0.96]; P = 0.025), and CPB burst-suppression (odds ratio, 3.79 [1.5 to 9.6]; P = 0.005) were associated with delirium. However, only physical function (odds ratio, 0.96 [0.91 to 0.99]; P = 0.044), lowest CPB temperature (odds ratio, 0.73 [0.58 to 0.88]; P = 0.003), and electroencephalogram alpha power (odds ratio, 0.61 [0.47 to 0.76]; P < 0.001) were retained as predictors in the burst-suppression multivariable model. Burst-suppression (odds ratio, 4.1 [1.5 to 13.7]; P = 0.012) and age (odds ratio, 1.07 [0.99 to 1.15]; P = 0.090) were retained as predictors in the delirium multivariable model. Delirium was associated with decreased electroencephalogram power from 6.8 to 24.4 Hertz. Conclusions The inference from the present study is that CPB burst-suppression mediates the effects of physical function, lowest CPB temperature, and electroencephalogram alpha power on delirium. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Study Objective:Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. Therefore, we hypothesize that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.Methods: Using a dataset including 8,682 polysomnographs, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals.Results: ECG in combination with the abdominal respiratory effort achieve the best performance for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence interval 0.599 -0.602); and 0.762 (0.760 -0.763) for discriminating awake vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is better for young participants and for those with a low apnea-hypopnea index, while it is robust for commonly used outpatient medications. Conclusions:Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large population. It opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible, such as in critically ill patients. Deep Network ArchitectureWe trained five deep neural networks based on the following input signals and their combinations: 1) ECG; 2) CHEST (chest respiratory effort); 3) ABD (abdominal respiratory effort); 4) ECG+CHEST; and 5) ECG+ABD. Each deep neural network contained a feed-forward convolutional neural network (CNN) which learned features pertaining to each epoch, and a recurrent neural network (RNN), in this case long-short term memory (LSTM), to learn temporal patterns among consecutive epochs.The CNN of the network is similar to that in Hannun et al. 20 . As shown in Figure 1A and Figure 1B, the network for a single type of input signal, i.e. ECG, CHEST or ABD, consists of a convolutional layer, several residual blocks and a final output block. For a network with both ECG and CHEST/ABD as input signals ( Figure 1C), we first fixed the weights of the layers up to the 9 th residual block (gray) for the ECG network and similarly fixed up to the 5 th residual block (gray) for the CHEST/ABD network, concatenated the outputs, and then fed this concatenation into a subnetwork containing five residual blocks and a final output block. The numbers of fixed layers were chosen so that the outputs of layers from different modalities have the same shape (after padding zeros), and were then concatenated.The LSTM of the network has the same structure for different input signals. It is a bi-directional LSTM, where the context cells from the forward and backward directions are concatenated. For the network

Objective: This study aims to identify blood biomarkers of postoperative delirium. Background: Phosphorylated tau at threonine 217 (Tau-PT217) and 181 (Tau-PT181) are new Alzheimer disease biomarkers. Postoperative delirium is associated with Alzheimer disease. We assessed associations between Tau-PT217 or Tau-PT181 and postoperative delirium. Methods: Of 491 patients (65 years old or older) who had a knee replacement, hip replacement, or laminectomy, 139 participants were eligible and included in the analysis. Presence and severity of postoperative delirium were assessed in the patients. Preoperative plasma concentrations of Tau-PT217 and Tau-PT181 were determined by a newly established Nanoneedle technology. Results: Of 139 participants (73 ± 6 years old, 55% female), 18 (13%) developed postoperative delirium. Participants who developed postoperative delirium had higher preoperative plasma concentrations of Tau-PT217 and Tau-PT181 than participants who did not. Preoperative plasma concentrations of Tau-PT217 or Tau-PT181 were independently associated with postoperative delirium after adjusting for age, education, and preoperative Mini-Mental State score [odds ratio (OR) per unit change in the biomarker: 2.05, 95% confidence interval (CI):1.61-2.62, P < 0.001 for Tau-PT217; and OR: 4.12; 95% CI: 2.55--6.67, P < 0.001 for Tau-PT181]. The areas under the receiver operating curve for predicting delirium were 0.969 (Tau-PT217) and 0.885 (Tau-PT181). The preoperative plasma concentrations of Tau-PT217 or Tau-PT181 were also associated with delirium severity [beta coefficient (β) per unit change in the biomarker: 0.14; 95% CI: 0.09-0.19, P < 0.001 for Tau-PT217; and β: 0.41; 95% CI: 0.12-0.70, P = 0.006 for Tau-PT181). Conclusions: Preoperative plasma concentrations of Tau-PT217 and Tau-PT181 were associated with postoperative delirium, with Tau-PT217 being a stronger indicator of postoperative delirium than Tau-PT181.

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Using positron emission tomography, we recently demonstrated elevated brain levels of the 18 kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HCs). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8 ± 12.5 y/o; 18 HC, 48.2 ± 12.8 y/o). We then trained random forest machine learning algorithms based on TSPO imaging features combining discovery and replication cohorts (totaling 25 cLBP, 42.4 ± 13.2 y/o; 27 HC, 48.9 ± 12.6 y/o), to explore whether image features other than the mean contain meaningful information that might contribute to the discrimination of cLBP patients and HC. Feature importance was ranked using SHapley Additive exPlanations values, and the classification performance (in terms of area under the curve values) of classifiers containing only the mean, other features, or all features was compared using the DeLong test. Both region-of-interest and voxelwise analyses replicated the original observation of thalamic TSPO signal elevations in cLBP patients compared to HC (P < 0.05). The random forest-based analyses revealed that although the mean is a discriminating feature, other features demonstrate similar level of importance, including the maximum, kurtosis, and entropy. Our observations suggest that thalamic neuroinflammatory signal is a reproducible and discriminating feature for cLBP, further supporting a role for glial activation in human cLBP, and the exploration of neuroinflammation as a therapeutic target for chronic pain. This work further shows that TSPO signal contains a richness of information that the simple mean might fail to capture completely.

We recently showed that patients with different chronic pain conditions (such as chronic low back pain, fibromyalgia, migraine, and Gulf War Illness) demonstrated elevated brain and/or spinal cord levels of the glial marker 18 kDa translocator protein, which suggests that neuroinflammation might be a pervasive phenomenon observable across multiple etiologically heterogeneous pain disorders. Interestingly, the spatial distribution of this neuroinflammatory signal appears to exhibit a degree of disease specificity (e.g. with respect to the involvement of the primary somatosensory cortex), suggesting that different pain conditions may exhibit distinct “neuroinflammatory signatures”. To further explore this hypothesis, we tested whether neuroinflammatory signal can characterize putative etiological subtypes of chronic low back pain patients based on clinical presentation. Specifically, we explored neuroinflammation in patients whose chronic low back pain either did or did not radiate to the leg (i.e. “radicular” vs. “axial” back pain). Fifty-four chronic low back pain patients, twenty-six with axial back pain (43.7 ± 16.6 y.o. [mean±SD]) and twenty-eight with radicular back pain (48.3 ± 13.2 y.o.), underwent PET/MRI with [11C]PBR28, a second-generation radioligand for the 18 kDa translocator protein. [11C]PBR28 signal was quantified using standardized uptake values ratio (validated against volume of distribution ratio; n = 23). Functional MRI data were collected simultaneously to the [11C]PBR28 data 1) to functionally localize the primary somatosensory cortex back and leg subregions and 2) to perform functional connectivity analyses (in order to investigate possible neurophysiological correlations of the neuroinflammatory signal). PET and functional MRI measures were compared across groups, cross-correlated with one another and with the severity of “fibromyalgianess” (i.e. the degree of pain centralization, or “nociplastic pain”). Furthermore, statistical mediation models were employed to explore possible causal relationships between these three variables. For the primary somatosensory cortex representation of back/leg, [11C]PBR28 PET signal and functional connectivity to the thalamus were: 1) higher in radicular compared to axial back pain patients, 2) positively correlated with each other and 3) positively correlated with fibromyalgianess scores, across groups. Finally, 4) fibromyalgianess mediated the association between [11C]PBR28 PET signal and primary somatosensory cortex-thalamus connectivity across groups. Our findings support the existence of “neuroinflammatory signatures” that are accompanied by neurophysiological changes, and correlate with clinical presentation (in particular, with the degree of nociplastic pain) in chronic pain patients. These signatures may contribute to the subtyping of distinct pain syndromes and also provide information about inter-individual variability in neuro-immune brain signals, within diagnostic groups, that could eventually serve as targets for mechanism-based precision medicine approaches.

Background Delirium is a distressing neurocognitive disorder recently linked to sleep disturbances. However, the longitudinal relationship between sleep and delirium remains unclear. This study assessed the associations of poor sleep burden, and its trajectory, with delirium risk during hospitalization. Methods 321,818 participants from the UK Biobank (mean age 58±8y[SD]; range 37-74y) reported (2006-2010) sleep traits (sleep duration, excessive daytime sleepiness, insomnia-type complaints, napping, and chronotype–a closely-related circadian measure for sleep timing), aggregated into a sleep burden score (0-9). New-onset delirium (n=4,775) was obtained from hospitalization records during 12y median follow-up. 42,291 (mean age 64±8; range 44-83y) had repeat sleep assessment on average 8y after their first. Results In the baseline cohort, Cox proportional hazards models showed that moderate (aggregate scores=4-5) and severe (scores=6-9) poor sleep burden groups were 18% (hazard ratio 1.18 [95% confidence interval 1.08-1.28], p&lt;0.001) and 57% (1.57 [1.38-1.80], p&lt;0.001), more likely to develop delirium respectively. The latter risk magnitude is equivalent to two additional cardiovascular risks. These findings appeared robust when restricted to postoperative delirium and after exclusion of underlying dementia. Higher sleep burden was also associated with delirium in the follow-up cohort. Worsening sleep burden (score increase ≥2 vs. no change) further increased the risk for delirium (1.79 [1.23-2.62], p=0.002) independent of their baseline sleep score and time-lag. The risk was highest in those under 65y at baseline (p for interaction &lt;0.001). Conclusion Poor sleep burden and worsening trajectory were associated with increased risk for delirium; promotion of sleep health may be important for those at higher risk.

BackgroundSurgery at night (incision time 17:00 to 07:00 hours) may lead to increased postoperative mortality and morbidity. Mechanisms explaining this association remain unclear.MethodsWe conducted a multicentre retrospective cohort study of adult patients undergoing non-cardiac surgery with general anaesthesia at two major, competing tertiary care hospital networks. In primary analysis, we imputed missing data and determined whether exposure to night surgery affects 30-day mortality using a mixed-effects model with individual anaesthesia and surgical providers as random effects. Secondary outcomes were 30-day morbidity and the mediating effect of blood transfusion rates and provider handovers on the effect of night surgery on outcomes. We further tested for effect modification by surgical setting.ResultsAmong 350 235 participants in the primary imputed cohort, the mortality rate was 0.9% (n=2804/322 327) after day and 3.4% (n=940/27 908) after night surgery. Night surgery was associated with an increased risk of mortality (ORadj 1.26, 95% CI 1.15 to 1.38, p<0.001). In secondary analyses, night surgery was associated with increased morbidity (ORadj 1.41, 95% CI 1.33 to 1.48, p<0.001). The proportion of patients receiving intraoperative blood transfusion and anaesthesia handovers were higher during night-time, mediating 9.4% (95% CI 4.7% to 14.2%, p<0.001) of the effect of night surgery on 30-day mortality and 8.4% (95% CI 6.7% to 10.1%, p<0.001) of its effect on morbidity. The primary association was modified by the surgical setting (p-for-interaction<0.001), towards a greater effect in patients undergoing ambulatory/same-day surgery (ORadj 1.81, 95% CI 1.39 to 2.35) compared with inpatients (ORadj 1.17, 95% CI 1.02 to 1.34).ConclusionsNight surgery was associated with an increased risk of postoperative mortality and morbidity. The effect was independent of case acuity and was mediated by potentially preventable factors: higher blood transfusion rates and more frequent provider handovers.