Common peripheral nerve entrapments in the upper limb.

Entrapment of peripheral nerves can occur as they travel through restrictive spaces. This nerve compression can result in a constellation of signs and symptoms, which are often called syndromes. Patients initially report pain, paraesthesia and numbness, followed by weakness and clumsiness and, ultimately, muscle wasting. The specific region of paraesthesia and pain and the specific muscle weakness is determined by the peripheral nerve involved and the location of the entrapment. Diagnosis is mainly based on history and examination. Further investigations are available for atypical presentations. Each syndrome has its own set of risk factors, but repetitive action and muscle overuse are commonly associated with most syndromes. The treatment is activity modification followed by steroid injection and finally surgical decompression for ongoing persistent symptoms or severe initial presentation. This article outlines the history, examination, possible investigations and management for common peripheral nerve entrapments of the median, ulnar and radial nerves.

Comparison of the International Crowding Measure in Emergency Departments (ICMED) and the National Emergency Department Overcrowding Score (NEDOCS) to measure emergency department crowding: pilot study.

INTRODUCTION: There is uncertainty about the best way to measure emergency department crowding. We have previously developed a consensus-based measure of crowding, the International Crowding Measure in Emergency Departments (ICMED). We aimed to obtain pilot data to evaluate the ability of a shortened form of the ICMED, the sICMED, to predict senior emergency department clinicians' concerns about crowding and danger compared with a very well-studied measure of emergency department crowding, the National Emergency Department Overcrowding Score (NEDOCS). METHODS: We collected real-time observations of the sICMED and NEDOCS and compared these with clinicians' perceptions of crowding and danger on a visual analogue scale. Data were collected in four emergency departments in the East of England. Associations were explored using simple regression, random intercept models and models accounting for correlation between adjacent time points. RESULTS: We conducted 82 h of observation in 10 observation sets. Naive modelling suggested strong associations between sICMED and NEDOCS and clinician perceptions of crowding and danger. Further modelling showed that, due to clustering, the association between sICMED and danger persisted, but the association between these two measures and perception of crowding was no longer statistically significant. CONCLUSIONS: Both sICMED and NEDOCS can be collected easily in a variety of English hospitals. Further studies are required but initial results suggest both scores may have potential use for assessing crowding variation at long timescales, but are less sensitive to hour-by-hour variation. Correlation in time is an important methodological consideration which, if ignored, may lead to erroneous conclusions. Future studies should account for such correlation in both design and analysis.

Initial validation of the International Crowding Measure in Emergency Departments (ICMED) to measure emergency department crowding.

INTRODUCTION: Emergency department (ED) crowding is recognised as a major public health problem. While there is agreement that ED crowding harms patients, there is less agreement about the best way to measure ED crowding. We have previously derived an eight-point measure of ED crowding by a formal consensus process, the International Crowding Measure in Emergency Departments (ICMED). We aimed to test the feasibility of collecting this measure in real time and to partially validate this measure. METHODS: We conducted a cross-sectional study in four EDs in England. We conducted independent observations of the measure and compared these with senior clinician's perceptions of crowding and safety. RESULTS: We obtained 84 measurements spread evenly across the four EDs. The measure was feasible to collect in real time except for the 'Left Before Being Seen' variable. Increasing numbers of violations of the measure were associated with increasing clinician concerns. The area under the receiver operating characteristic curve was 0.80 (95% CI 0.72 to 0.90) for predicting crowding and 0.74 (95% CI 0.60 to 0.89) for predicting danger. The optimal number of violations for predicting crowding was three, with a sensitivity of 91.2 (95% CI 85.1 to 97.2) and a specificity of 100.0 (92.9-100). The measure predicted clinician concerns better than individual variables such as occupancy. DISCUSSION: The ICMED can easily be collected in multiple EDs with different information technology systems. The ICMED seems to predict clinician's concerns about crowding and safety well, but future work is required to validate this before it can be advocated for widespread use.