All indices, RE, SE, and BIS, distinguished excellently between conscious and unconscious states during propofol, sevoflurane, and thiopental anesthesia. During burst suppression, Entropy parameters RE and SE, but not BIS, behave monotonously. During regaining of consciousness after a thiopental or propofol bolus, RE and SE values recovered significantly closer to their baseline values than did BIS. Response entropy indicates emergence from anesthesia earlier than SE or BIS.

Entropy monitoring assisted titration of propofol, especially during the last part of the procedures, as indicated by higher entropy values, decreased consumption of propofol, and shorter recovery times in the entropy group.

Chronic pain has a significant impact on quality of life. Measurement of health-related quality of life (HRQoL) is essential in the assessment of pain management outcomes, but different instruments have produced varying results. We assessed the validity of 2 HRQoL instruments, EuroQol 5 dimensions questionnaire (EQ-5D) and 15-dimensional health-related quality of life measure (15D), in patients with challenging chronic pain. Three hundred ninety-one chronic noncancer pain patients referred to tertiary pain clinics completed EQ-5D, 15D, and a broad set of questionnaires mapping socioeconomic factors, self-rated health, pain intensity and interference, depression, pain acceptance, pain-related anxiety, and sleep. The 2 HRQoL instruments were compared with each other, and head-to-head comparisons were made with self-rated health and the symptom-specific questionnaires. 15D and EQ-5D showed moderate agreement (ρ = 0.66), but there were also considerable differences between the instruments. 15D correlated better with self-rated health than EQ-5D (ρ = -0.62 vs -0.45, P < 0.001). The EQ-5D appeared less sensitive than 15D especially in those patients with chronic pain who had a better health status. The principal component constructed from measures of pain intensity and interference, anxiety, pain acceptance, depression, and sleep had higher standardized beta coefficients with 15D than with EQ-5D (P = 0.038). The principal component explained more variance in the 15D (R = 0.65) than in the EQ-5D (R = 0.43). The study identified differences in the pain-related variables between the EQ-5D and the 15D. In patients with chronic pain, both instruments are valid, but 15D appears somewhat more sensitive than EQ-5D.

Patients with chronic pain have complex pain profiles and associated problems. Subgroup analysis can help identify key problems. We used a data-based approach to define pain phenotypes and their most relevant associated problems in 320 patients undergoing tertiary pain management. Unsupervised machine learning analysis of parameters "pain intensity", "number of pain areas", "pain duration", "activity pain interference" and "affective pain interference", implemented as emergent selforganizing maps, identified three patient phenotype clusters. Supervised analyses, implemented as different types of decision rules, identified "affective pain interference" and the "number of pain areas" as most relevant for cluster assignment. These appeared 698 and 637 times, respectively, in 1000 cross-validation runs among the most relevant characteristics in an item categorization approach in a computed ABC analysis. Cluster assignment was achieved with a median balanced accuracy of 79.9%, a sensitivity of 74.1%, and a specificity of 87.7%. In addition, among 59 demographic, pain etiology, comorbidity, lifestyle, psychological, and treatment-related variables, sleep problems appeared 638 and 439 times among the most important characteristics in 1000 cross-validation runs where patients were assigned to the two extreme pain phenotype clusters. Also important were the parameters "fear of pain", "self-rated poor health", and "systolic blood pressure". Decision trees trained with this information assigned patients to the extreme pain-phenotype with an accuracy of 67%. Machine learning suggested sleep problems as key factors in the most difficult pain presentations, therefore deserving priority in the treatment of chronic pain.In this study, we used a data-driven approach in a cohort of patients with persistent pain of various causes to identify the most important attributes or problems specific to different pain phenotypes. In addition to pain phenotype-related factors, 59 further variables, representing factors recognized to be, and others possibly, associated with pain phenotypes were identified, including demographic factors, pain etiology, comorbidities, lifestyle factors, psychological variables, and treatment-related factors. These data were analyzed using unsupervised and supervised machine learning methods with the aim of (i) identifying and interpreting patterns arising from different pain phenotypic parameters and (ii) selecting among the further parameters those that were informative in associating a patient with a particular pain phenotype. Methods Study setting and designThis was an observational cohort study in chronic pain patients treated in three multidisciplinary tertiary pain clinics in Finland. A multi-center "KROKIETA" study was used to collect patient data on a broad scale, including socioeconomic factors, lifestyle factors, psychological variables, previous treatments, and biochemical indicators [98]. The study adheres to the STROBE guidelines

Our results show that burst suppression caused by the different anesthetics can be reliably detected with our segmentation and classification methods. The analysis of normal and pathological EEG, however, should include information of the anesthetic used. Knowledge of the normal variation of the EEG is necessary in order to detect the abnormal BSP of, for instance, seizure patients.