Objectives To develop and validate a delirium prediction model for adult intensive care patients and determine its additional value compared with prediction by caregivers.Design Observational multicentre study.Setting Five intensive care units in the Netherlands (two university hospitals and three university affiliated teaching hospitals).Participants 3056 intensive care patients aged 18 years or over.Main outcome measure Development of delirium (defined as at least one positive delirium screening) during patients' stay in intensive care. ResultsThe model was developed using 1613 consecutive intensive care patients in one hospital and temporally validated using 549 patients from the same hospital. For external validation, data were collected from 894 patients in four other hospitals. The prediction (PRE-DELIRIC) model contains 10 risk factors-age, APACHE-II score, admission group, coma, infection, metabolic acidosis, use of sedatives and morphine, urea concentration, and urgent admission. The model had an area under the receiver operating characteristics curve of 0.87 (95% confidence interval 0.85 to 0.89) and 0.86 after bootstrapping. Temporal validation and external validation resulted in areas under the curve of 0.89 (0.86 to 0.92) and 0.84 (0.82 to 0.87). The pooled area under the receiver operating characteristics curve (n=3056) was 0.85 (0.84 to 0.87). The area under the curve for nurses ' and physicians' predictions (n=124) was significantly lower at 0.59 (0.49 to 0.70) for both. ConclusionThe PRE-DELIRIC model for intensive care patients consists of 10 risk factors that are readily available within 24 hours after intensive care admission and has a high predictive value. Clinical prediction by nurses and physicians performed significantly worse. The model allows for early prediction of delirium and initiation of preventive measures. Trial registration Clinical trials NCT00604773 (development study) and NCT00961389 (validation study). IntroductionDelirium, characterised by an acute onset of fluctuating changes in mental status and changed levels of consciousness and inattentiveness, 1 has a high incidence rate in critically ill patients. [2][3][4] It is a serious disorder associated with prolonged stays in intensive care units and hospitals, higher costs, and increased morbidity and mortality. Several tools are available for assessing delirium in intensive care patients, of which the confusion assessment method-intensive care unit (CAM-ICU) has the highest sensitivity and specificity. 6 7 Screening intensive care patients is important, [8][9][10] so that timely treatment can be provided. However, preventive measures for delirium may also reduce its incidence, severity, and duration, as determined in other groups of patients.11 12 General preventive measures in all intensive care patients are time consuming and may expose a substantial number of patients to unnecessary risks such as the adverse Methods Study designThis was an observational multicentre study in which we firstly developed the PREdi...

ICBT appears to be effective in reducing severe fatigue and related symptoms and meets the current need for easy accessible and more efficient evidence-based treatment options for severely fatigued survivors of breast cancer. Cancer 2017;123:3825-34. © 2017 American Cancer Society.

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Methods: A prospective multicenter cohort study was performed in eight intensive care units (ICUs) in six countries. The 10 predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 hours after ICU admission. The confusion assessment method for the Intensive Care Unit (CAM-ICU) was used to identify ICU delirium. CAM-ICU screening compliance and inter-rater reliability measurements were used to secure the quality of the data. Permanent repository linkResults: 2,852 adult ICU patients were screened of which 1,824 (64%) were eligible for the study. Main reasons for exclusion were length of stay <1day (19.1%) and sustained coma (4.1%). CAM-ICU compliance was mean (SD) 82±16% and inter-rater reliability 0.87±0.17. The median delirium incidence was 22.5% (IQR 12.8%-36.6%). Although the incidence of all ten predictors differed significantly between centers, the area under the receiver operating characteristic (AUROC) curve of the 8 participating centers remained good: 0.77 (95%CI:0.74-0.79). The linear predictor and intercept of the prediction rule were adjusted and resulted in improved recalibration of the PRE-DELIRIC model. Conclusions:In this multinational study we recalibrated the PRE-DELIRIC-model. Despite differences in the incidence of predictors between the centers in the different countries the performance of the PRE-DELIRICmodel remained good. Following validation of the PRE-DELIRIC model it may facilitate implementation of strategies to prevent delirium and aid improvements in delirium management of ICU patients.4

Objective. The treatment of patients with fibromyalgia (FM), a high-prevalence chronic pain condition with a high impact on both patients and society, poses a great challenge to clinicians due to a lack of effective treatments. In view of the large individual variability in outcome, selecting patients at risk of long-term dysfunction and offering tailored treatment may be promising for beneficial treatment effects. Methods. High-risk patients were selected and classified into 2 groups (pain-persistence and pain-avoidance groups) and subsequently randomized in groups to either a treatment condition (TC) or a waiting list control condition (WLC). Treatment consisted of 16 sessions of cognitive-behavioral therapy (CBT) and exercise training in groups, tailored to the patient's specific cognitive-behavioral pattern, delivered within 10 weeks. Physical and psychological functioning and impact of FM were assessed at baseline, posttreatment, and 6-month followup. Treatment effects were evaluated using a linear mixed model. Results. The treatment effects were significant for all primary outcomes, showing significant differences in physical (pain, fatigue, and functional disability) and psychological (negative mood and anxiety) functioning, and impact of FM for the TC in comparison with the WLC. Effect sizes in the TC were overall large, and reliable change indices indicated a clinically relevant improvement among the TC. Conclusion. The presented results demonstrate for the first time that tailored CBT and exercise training for high-risk patients with FM is effective in improving short-and long-term physical and psychological functioning, indicating that tailoring treatment is likely to promote beneficial outcomes in FM and reduce the burden for patients and society.