In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Abstract Background Postoperative delirium (POD) is an acute form of brain dysfunction that can result in serious adverse consequences. There has been a link between cognitive dysfunction and poor sleep. The present study aimed to determine the association and prediction of subjective sleep quality and postoperative delirium during major non-cardiac surgery. Methods One hundred and thirty-four patients, aged 60 years or older, were scheduled for elective laparotomy or orthopaedic procedures. The Pittsburgh Sleep Quality Index (PSQI) and sleep log were used to assess perioperative subjective sleep quality in participants. Nursing Delirium Screening Checklist (NU-DESC) was used for screening, and the Confusion Assessment Method (CAM) was used to diagnose POD during the first seven days following surgery. The association between subjective sleep quality and POD was assessed using a multivariate logistic regression model. Thereafter, the prediction performance of subjective sleep quality was evaluated using a receiver operating characteristic (ROC) curve. Results All assessments were completed on 119 patients who had an average PSQI score of 7.0 ± 2.4 before surgery. 23 patients (19.3%) suffered from POD. The multivariate logistic regression analysis showed that the occurrence of POD was closely related to age, BMI, PSQI and operation time. After adjusting for related factors, there was a statistically significant association between PSQI and POD occurrence (OR = 1.422, 95%CI 1.079–1.873, per 1-point increase in PSQI). The ROC curve analysis showed that the optimal PSQI cutoff value was 8.0 for predicting POD, and the area under the ROC (AUROC) value of PSQI was 0.741 (95%CI 0.635 to 0.817). The AUROC of the model developed by the multivariate logistic regression analysis was 0.870 (95%CI 0.797 to 0.925). Conclusions The study found that preoperative subjective sleep quality was strongly associated with POD during major non-cardiac surgery. Additionally, PSQI combined with age, BMI, and operation time improved POD prediction.

Volatile anesthetics such as sevoflurane have been widely used in clinical practice, but how it causes loss of consciousness, anesthesia maintenance and consciousness recovery remained largely unknown. Here we show that the loss of consciousness induced by sevoflurane was closely related to the paraventricular thalamus (PVT). Both bed nucleus of stria terminalis (BNST) GABAergic and glutamatergic neurons receive PVT glutamatergic projections. Chemogenetic inhibition of PVT glutamatergic neurons or PVT-BNST neural circuit facilitated induction and delayed recovery of sevoflurane anesthesia, while optogenetic activation of PVT glutamatergic neurons or PVTvglut2+-BNST neural circuit produced opposite effects. Additionally, transient stimulation of PVT glutamatergic neurons or PVTvglut2+-BNST neural circuit was efficient to induce behavioral arousal during continuous steady-state general anesthesia with sevoflurane and promote cortical activation during sevoflurane-induced burst-suppression condition. Taken together, our results provide compelling evidence that PVT glutamatergic neurons regulate states of consciousness during sevoflurane anesthesia through directly projecting to BNST. And this specific cell-type neural circuit can be potential targets that modulate consciousness and mitigate general anesthesia related side effects.

IMPORTANCE Delirium is a common postoperative complication in older patients that often goes undetected and might lead to worse outcomes. The 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) might be a practical tool for routine clinical diagnosis of delirium. OBJECTIVE To assess the 3D-CAM for detecting postoperative delirium compared with the longform CAM used for research purposes. DESIGN, SETTING, AND PARTICIPANTS This cohort study of older patients enrolled in ongoing clinical trials between 2015 and 2018 was conducted at a single tertiary US hospital. Included participantswere aged 60 years or older undergoing major elective surgical procedures that required at least a 2-day hospital stay. Data were analyzed between February and April 2019. EXPOSURES Surgical procedures of at least 2 hours in length requiring general anesthesia with planned extubation. MAIN OUTCOMES AND MEASURES Patients were concurrently assessed for delirium using the 3D-CAM assessment and the long-form CAM, scored based on a standardized cognitive assessment. Agreement between these 2methods was tested using Cohen. with repeated measures, a generalized linear mixed-effects model, and Bland-Altman analysis. RESULTS Sixteen raters conducted 471 concurrent CAM and 3D-CAM interviews including 299 patients (mean [SD] age, 69 [6.5] years), the majority of whom were men (152 [50.8%]), were White (263 [88.0%]), and had noncardiac operations (211 [ 70.6%]). Both instruments had good intraclass correlation (0.84 for the CAM and 0.98 for the 3D-CAM). Cohen. demonstrated good overall agreement between the CAM and 3D-CAM (kappa = 0.71; 95% CI, 0.58 to 0.83). According to the mixedeffects model, there was statistically significant disagreement between the 3D-CAM and CAM (estimated difference in fixed effect, -0.68; 95% CI, -1.32 to -0.05; P =.04). Bland-Altman analysis showed the probability of a delirium diagnosis with the 3D-CAMwas more than twice the probability of a delirium diagnosis with the CAM (probability ratio, 2.78; 95% CI, 2.44 to 3.23). CONCLUSIONS AND RELEVANCE The 3D-CAM instrument demonstrated agreement with the longform CAM and might provide a pragmatic and sensitive clinical tool for detecting postoperative delirium, with the caveat that the 3D-CAM might overdiagnose delirium.

Method comparison studies are essential for development in medical and clinical fields. These studies often compare a cheaper, faster, or less invasive measuring method with a widely used one to see if they have sufficient agreement for interchangeable use. In the clinical and medical context, the response measurement is usually impacted not only by the measuring method but by the rater as well. This paper proposes a model-based approach to assess agreement of two measuring methods for paired repeated binary measurements under the scenario when the agreement between two measuring methods and the agreement among raters are required to be studied in a unified framework. Based upon the generalized linear mixed models (GLMM), the decision on the adequacy of interchangeable use is made by testing the equality of fixed effects of methods. Approaches for assessing method agreement, such as the Bland-Altman diagram and Cohen's kappa, are also developed for repeated binary measurements based upon the latent variables in GLMMs. We assess our novel model-based approach by simulation studies and a real clinical research application, in which patients are evaluated repeatedly for delirium with two validated screening methods: the Confusion Assessment Method and the 3-Minute Diagnostic Interview for Confusion Assessment Method. Both the simulation studies and the real data analyses demonstrate that our new approach can effectively assess method agreement.

OBJECTIVES/SPECIFIC AIMS: (1) Assess if the total duration of EEG suppression during a protocolized exposure to general anesthesia predicts cognitive performance in multiple cognitive domains immediately following emergence from anesthesia. (2) Assess if the total duration of EEG suppression in the same individuals predicts the rate of cognitive recovery in a three-hour period following emergence from anesthesia. METHODS/STUDY POPULATION: This was a non-specified substudy of NCT01911195, a multicenter investigation taking place at the University of Michigan, University of Pennsylvania, and Washington University in St. Louis. 30 healthy volunteers aged 20-40 years were recruited to receive general anesthesia. Participants in the anesthesia arm were anesthetized for three hours at isoflurane levels compatible with surgery (1.3 MAC). Multichannel sensor nets were used for EEG acquisition during the anesthetic exposure. EEG suppression was detected through automated voltage-thresholded classification of 2-second signal epochs, with concordance assessed across sensors. Following return of responsiveness to verbal commands, participants completed up to three hours of serial cognitive tests assessing executive function, reaction time, cognitive throughput, and working memory. Non-linear mixed effects models will be used to estimate the initial cognitive deficit and the rate of cognitive recovery following anesthetic exposure; these measures of cognitive function will be assessed in relation to total duration of suppression during anesthesia. RESULTS/ANTICIPATED RESULTS: Participants displayed wide variability in the total amount of suppression during anesthesia, with a median of 31.2 minutes and range from 0 minutes to 115.2 minutes. Initial analyses suggest that greater duration of burst suppression had a weak relationship with participants’ initial cognitive deficits upon return of responsiveness from anesthesia. Model generation of rate of recovery following anesthetic exposure is pending, but we anticipate this will also have a weak relationship with burst suppression. DISCUSSION/SIGNIFICANCE OF IMPACT: In healthy adults receiving a standardized exposure to anesthesia without surgery, burst suppression appears to be a poor predictor of post-anesthesia cognitive task performance. This suggests that burst suppression may have limited utility as a predictive marker of post-operative cognitive functioning, particularly in young adults without significant illness.

Background: Burst suppression occurs in the EEG during coma and under general anaesthesia. It has been assumed that burst suppression represents a deeper state of anaesthesia from which it is more difficult to recover. This has not been directly demonstrated, however. Here, we test this hypothesis directly by assessing relationships between EEG suppression in human volunteers and recovery of consciousness. Methods: We recorded the EEG of 27 healthy humans (nine women/18 men) anaesthetised with isoflurane 1.3 minimum alveolar concentration (MAC) for 3 h. Periods of EEG suppression and non-suppression were separated using principal component analysis of the spectrogram. After emergence, participants completed the digit symbol substitution test and the psychomotor vigilance test. Results: Volunteers demonstrated marked variability in multiple features of the suppressed EEG. In order to test the hypothesis that, for an individual subject, inclusion of features of suppression would improve accuracy of a model built to predict time of emergence, two types of models were constructed: one with a suppression-related feature included and one without. Contrary to our hypothesis, Akaike information criterion demonstrated that the addition of a suppression-related feature did not improve the ability of the model to predict time to emergence. Furthermore, the amounts of EEG suppression and decrements in cognitive task performance relative to pre-anaesthesia baseline were not significantly correlated. Conclusions: These findings suggest that, in contrast to current assumptions, EEG suppression in and of itself is not an important determinant of recovery time or the degree of cognitive impairment upon emergence from anaesthesia in healthy adults.

Method comparison studies are essential for development in medical and clinical fields. These studies often compare a cheaper, faster, or less invasive measuring method with a widely used one to see if they have sufficient agreement for interchangeable use. In the clinical and medical context, the response measurement is usually impacted not only by the measuring method but by the rater as well. This paper proposes a model-based approach to assess agreement of two measuring methods for paired repeated binary measurements under the scenario when the agreement between two measuring methods and the agreement among raters are required to be studied in a unified framework. Based upon the generalized linear mixed models (GLMM), the decision on the adequacy of interchangeable use is made by testing the equality of fixed effects of methods. Approaches for assessing method agreement, such as the Bland-Altman diagram and Cohenu0027s kappa, are also developed for repeated binary measurements based upon the latent variables in GLMMs. We assess our novel model-based approach by simulation studies and a real clinical research application, in which patients are evaluated repeatedly for delirium with two validated screening methods: the Confusion Assessment Method and the 3-Minute Diagnostic Interview for Confusion Assessment Method. Both the simulation studies and the real data analyses demonstrate that our new approach can effectively assess method agreement.

A basic problem in modern multivariate analysis is testing the equality of two mean vectors in settings where the dimension p increases with the sample size n. This paper proposes a robust two-sample test for high-dimensional data against sparse and strong alternatives, in which the mean vectors of the populations differ in only a few dimensions, but the magnitude of the differences is large. The test is based on trimmed means and robust precision matrix estimators. The asymptotic joint distribution of the trimmed means is established, and the proposed test statistic is shown to have a Gumbel distribution in the limit. Simulation studies suggest that the numerical performance of the proposed test is comparable to that of non-robust tests for uncontaminated data. For cell-wise contaminated data, it outperforms non-robust tests. An illustration involves biomarker identification in an Alzheimer’s disease dataset.