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Aims. The purpose of this study was: (1) to determine the combination of risk factors which best predicts the risk of developing pressure ulcers among inpatients in an acute care university hospital; (2) to determine the appropriate weight for each risk factor; and (3) to derive a concise and easy-to-use risk assessment tool for daily use by nursing staff. Background. Efficient application of preventive measures against pressure ulcers requires the identification of patients at risk. Adequate risk assessment tools are still needed because the predictive value of existing tools is sometimes unsatisfactory. Design. Survey. Methods. A sample of 34,238 cases admitted to Essen University Clinics from April 2003 and discharged up to and including March 2004, was enrolled into the study. Nursing staff recorded data on pressure ulcer status and potential risk factors on admission. Predictors were identified and weighted by multivariate logistic regression. We derived a risk assessment scale from the final logistic regression model by assigning point values to each predictor according to its individual weight. Results. The period prevalence rate of pressure ulcers was 1·8% (625 cases). The analysis identified 12 predictors for developing pressure ulcers. With the optimum cut-off point sensitivity and specificity were 83·4 and 83·1%, respectively, with a positive predictive value of 8·4% and a negative predictive value of 99·6%. The diagnostic probabilities of the derived scale were similar to those of the original regression model. Conclusions. The predictors mostly correspond to those used in established scales, although the use of weighted factors is a partly novel approach. Both the final regression model and the derived scale show good prognostic validity. Relevance to clinical practice. The derived risk assessment scale is an easy-to-understand, easy-to-use tool with good prognostic validity and can assist in effective application of preventive measures against pressure ulcer. [ABSTRACT FROM AUTHOR]

Remote ischemic preconditioning (RIPC) with transient upper limb ischemia reduces myocardial injury in patients undergoing on-pump coronary artery bypass grafting (CABG) with cross-clamp fibrillation or blood cardioplegia for myocardial protection. Whether or not such protection is still operative when standard crystalloid cardioplegic arrest is used is uncertain. Fifty-three consecutive, non-diabetic patients with triple-vessel disease and 64 +/- 12 years of age (mean +/- SD), who underwent elective CABG surgery with crystalloid (Bretschneider) cardioplegic arrest, were allocated in a prospective, randomized, single-blinded protocol to receive either a RIPC protocol (3 cycles of 5 min transient left upper arm ischemia induced by inflating a blood pressure cuff to 200 mmHg with 5 min of reperfusion) or control, respectively, after induction of anesthesia. Cardiac troponin I (cTnI) concentration was measured preoperatively and over 72 h postoperatively, and the area under the curve (AUC) was calculated. Peak postoperative cTnI concentration was significantly reduced from 13.7 +/- 7.7 ng/mL in controls to 8.9 +/- 4.4 ng/mL in RIPC (P = 0.008). Mean cTnI concentration was significantly lower at 6, 12, 24, and 48 h after surgery (ANOVA; P < 0.0001) in the RIPC patients (N = 27) than in controls (N = 26), resulting in a 44.5% reduction of cTnI (AUC at 72 h). RIPC by repetitive inflation of a cuff around the left upper arm before surgery enhances myocardial protection in patients undergoing CABG surgery with antegrade cold crystalloid cardioplegia.

Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in clustering procedures. Statistical methods are required to distinguish between real and random clusters. Several methods for assessing cluster stability have been published, including resampling methods such as the bootstrap. We propose a new resampling method based on continuous weights to assess the stability of clusters in hierarchical clustering. While in bootstrapping approximately one third of the original items is lost, continuous weights avoid zero elements and instead allow non integer diagonal elements, which leads to retention of the full dimensionality of space, i.e. each variable of the original data set is represented in the resampling sample., Results: Comparison of continuous weights and bootstrapping using real datasets and simulation studies reveals the advantage of continuous weights especially when the dataset has only few observations, few differentially expressed genes and the fold change of differentially expressed genes is low., Conclusion: We recommend the use of continuous weights in small as well as in large datasets, because according to our results they produce at least the same results as conventional bootstrapping and in some cases they surpass it.