Dressing changes cause severe pain (ie, 8-10 on a 10-point scale) for approximately one-third (36%) of patients with open skin wounds. No tool exists that allows nurses to predict which patients are likely to experience severe pain during dressing changes. The aim of this study was to develop a clinical tool to predict severe pain during dressing changes using clinically accessible wound and pain predictors and to evaluate the diagnostic validity of this model. Using a cross-sectional design, a one-time study dressing change was conducted by the same wound care nurse on 445 subjects while concurrently measuring patient and wound predictors and pain intensity during the dressing change. Three predictors came out of the study as most useful for a clinical prediction tool: type of dressing, resting wound pain, and expected pain. Algorithms based on these predictors are presented, which can be applied in other settings to predict patients likely to experience severe pain during a dressing change. This is the first study to systematically examine a comprehensive set of wound and patient predictors for their individual and collective associations with pain during dressing changes using precisely defined and rigorously measured study variables. The ability to predict which patients are likely to have severe pain during dressing changes is critically needed so that they can be targeted for preventive pain control strategies.

Early and accurate detection of pathogen is an important key to prevent and treat pathogen originated health issues. Conventional diagnostic methods are relatively laborious, time-consuming, costly, and require sophisticated instruments. To overcome the limitations of traditional instruments, nanosensors have emerged as a promising alternative. Due to their easy fabrication, high surface-to-volume ratio, and great biocompatibility, nanosensors have been shown to be an ideal technique for sensing. Herein, we provide an overall impression of the application of different types of nanosensors in disease diagnosis, and in the monitoring of water and food quality along with their probable future orientations.