Background: Recent limited evidence suggests that the use of processed electroencephalographic (EEG) monitor to guide anesthetic management may influence postoperative cognitive outcomes, however, the mechanism is unclear.Methods: This exploratory single center, randomized clinical trial included patients who were ≥ 65 years of age undergoing elective non-cardiac surgery. The study aimed to determine whether monitoring the brain using a processed EEG monitor reduced EEG suppression and subsequent postoperative delirium. The interventional group received processed EEG-guided anesthetic management to keep the Patient State Index (PSI) above 35 computed by the SEDline Brain Function Monitor while the standard care group was also monitored but the EEG data were blinded from the clinicians. The primary outcome was intraoperative EEG suppression. A secondary outcome was incident postoperative delirium during the first three days after surgery.Results: All outcomes were analyzed using the intention-to-treat paradigm. 204 patients with a mean age of 72 ± 5 years were studied. Minutes of EEG suppression adjusted by the length of surgery was found to be less for the interventional group than the standard care group (median, interquartile range 1.4% (5.0%) and 2.5% (10.4%); Hodges-Lehmann estimated median difference (95% CI) of −0.8% (−2.1%, −0.000009%). The effect of the intervention on EEG suppression differed for those with and without preoperative cognitive impairment (interaction P=0.01), with

Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparisons. The R package ADDT provides the functionalities of performing the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods for analyzing ADDT data, and then estimating the TI for polymeric materials. In this chapter, we provide a detailed introduction to the ADDT package. We provide a step-by-step illustration for the use of functions in the package. Publicly available datasets are used for illustrations.

Accelerated destructive degradation test (ADDT) is a technique that is commonly used by industries to access material's long-term properties. In many applications, the accelerating variable is usually the temperature. In such cases, a thermal index (TI) is used to indicate the strength of the material. For example, a TI of 200 • C may be interpreted as the material can be expected to maintain a specific property at a temperature of 200 • C for 100,000 hours. A material with a higher TI possesses a stronger resistance to thermal damage. In literature, there are three methods available to estimate the TI based on ADDT data, which are the traditional method based on the least-squares approach, the parametric method, and the semiparametric method. In this chapter, we provide a comprehensive review of the three methods and illustrate how the TI can be estimated based on different models. We also conduct comprehensive simulation studies to show the properties of different methods. We provide thorough discussions on the pros and cons of each method. The comparisons and discussion in this chapter can be useful for practitioners and future industrial standards.

No abstract

No abstract