Objectives: To determine the normal range for carbon monoxide concentrations in the exhaled breath of subjects in the emergency department and to develop a protocol for the use of a breath analyser to detect abnormal carbon monoxide exposure. Methods: A hand held breath analyser was used to measure end expiratory carbon monoxide concentrations in 382 consenting subjects. Questionnaire data were collected to assess the effect of common sources of carbon monoxide exposure on breath carbon monoxide levels. Smokers were used as a carbon monoxide exposed group for comparison with non-smokers. Results: The range of carbon monoxide concentrations obtained in the non-smoking group was 0-6 ppm and in the smoking group was 1-68 ppm. Smokers had a mean breath carbon monoxide concentration of 16.4 ppm and non-smokers had a mean of 1.26 ppm (95% confidence interval (CI) for difference 13.6 to 16.8 ppm). Male sex and frequent motor vehicle use were associated with slightly higher carbon monoxide concentrations (by 0.40, 95% CI 0.18 to 0.63 ppm, and 0.38, 95% CI 0.13 to 0.63 ppm, respectively) in the non-smoking group. Mean breath carbon monoxide concentrations increased in direct proportion to the number of cigarettes smoked (p<0.001) and there was a negative correlation between carbon monoxide and time since last smoking a cigarette (p<0.001). Altogether 23% of smokers had breath carbon monoxide concentrations in the range 1-6 ppm. Conclusions: Breath analysis was rapid and results correlated well with carbon monoxide exposure. In this population subjects with breath carbon monoxide concentrations greater than 6 ppm should be assessed for the risk of carbon monoxide poisoning. However even carbon monoxide concentrations less than 6 ppm do not exclude carbon monoxide poisoning within the last 24 hours.
Valdecoxib was comparable with tramadol and was significantly better than placebo in treating acute ankle sprain, and it enabled more patients to resume normal walking on days 4 and 7. Both valdecoxib and tramadol were well tolerated.
The study examines the cause of fractures in infants under a year of age presenting to an Accident and Emergency (A&E) department in order to ascertain the proportion of non-accidental (NA) to accidental fractures. It also considers whether there were any child protection concerns in either group in the 4-5 years following the fracture. The study involved a retrospective review of notes of infants presenting with fractures over a 2-year period. Health visitors (HV) completed a questionnaire regarding the well-being of these children 4-5 years after the fracture occurred. The study identified 28 infants with fractures, seven NA and 21 accidental. Of the seven NA cases, five were diagnosed on presentation but two only on subsequent referral (2 days and 5 weeks later). On second presentation, one of these had more severe injuries. Six of the seven with NA fractures were male, five were less than 4 months of age and in four a skeletal survey revealed other fractures. Information from the HV questionnaire revealed that one of those considered to have had an accidental fracture in infancy was later put on the Social Services child protection register. It was concluded that fractures in infants under a year of age have a high risk of being NA and distinguishing these cases from accidental fractures can be difficult. The younger the infant, the greater the risk of a fracture being NA. A missed diagnosis may result in a child sustaining more severe injuries. A&E departments should have clear guidelines on the assessment of infants with fractures. Liaison with HVs and general practitioners is important to ensure primary care advice on accident prevention and to allow for surveillance regarding possible neglectful care.
BackgroundAnkle sprains are very common injuries. Although recovery can occur within weeks, around one-third of patients have longer-term problems.ObjectivesTo develop and externally validate a prognostic model for identifying people at increased risk of poor outcome after an acute ankle sprain.DesignDevelopment of a prognostic model in a clinical trial cohort data set and external validation in a prospective cohort study.SettingEmergency departments (EDs) in the UK.ParticipantsAdults with an acute ankle sprain (within 7 days of injury).Sample sizeThere were 584 clinical trial participants in the development data set and 682 recruited for the external validation study.PredictorsCandidate predictor variables were chosen based on availability in the clinical data set, clinical consensus, face validity, a systematic review of the literature, data quality and plausibility of predictiveness of the outcomes.Main outcome measuresModels were developed to predict two composite outcomes representing poor outcome. Outcome 1 was the presence of at least one of the following symptoms at 9 months after injury: persistent pain, functional difficulty or lack of confidence. Outcome 2 included the same symptoms as outcome 1, with the addition of recurrence of injury. Rates of poor outcome in the external data set were lower than in the development data set, 7% versus 20% for outcome 1 and 16% versus 24% for outcome 2.AnalysisMultiple imputation was used to handle missing data. Logistic regression models, together with multivariable fractional polynomials, were used to select variables and identify transformations of continuous predictors that best predicted the outcome based on a nominal alpha of 0.157, chosen to minimise overfitting. Predictive accuracy was evaluated by assessing model discrimination (c-statistic) and calibration (flexible calibration plot).Results(1) Performance of the prognostic models in development data set – the combinedc-statistic for the outcome 1 model across the 50 imputed data sets was 0.74 [95% confidence interval (CI) 0.70 to 0.79], with good model calibration across the imputed data sets. The combinedc-statistic for the outcome 2 model across the 50 imputed data sets was 0.70 (95% CI 0.65 to 0.74), with good model calibration across the imputed data sets. Updating these models, which used baseline data collected at the ED, with an additional variable at 4 weeks post injury (pain when bearing weight on the ankle) improved the discriminatory ability (c-statistic 0.77, 95% CI 0.73 to 0.82, for outcome 1 and 0.75, 95% CI 0.71 to 0.80, for outcome 2) and calibration of both models. (2) Performance of the models in the external data set – the combinedc-statistic for the outcome 1 model across the 50 imputed data sets was 0.73 (95% CI 0.66 to 0.79), with a calibration plot intercept of –0.91 (95% CI –0.98 to 0.44) and slope of 1.13 (95% CI 0.76 to 1.50). The combinedc-statistic for the outcome 2 model across the 50 imputed data sets was 0.63 (95% CI 0.58 to 0.69), with a calibration plot intercept of –0.25 (95% CI –0.27 to 0.11) and slope of 1.03 (95% CI 0.65 to 1.42). The updated models with the additional pain variable at 4 weeks had improved discriminatory ability over the baseline models but not better calibration.ConclusionsThe SPRAINED (Synthesising a clinical Prognostic Rule for Ankle Injuries in the Emergency Department) prognostic models performed reasonably well, and showed benefit compared with not using any model; therefore, the models may assist clinical decision-making when managing and advising ankle sprain patients in the ED setting. The models use predictors that are simple to obtain.LimitationsThe data used were from a randomised controlled trial and so were not originally intended to fulfil the aim of developing prognostic models. However, the data set was the best available, including data on the symptoms and clinical events of interest.Future workFurther model refinement, including recalibration or identifying additional predictors, may be required. The effect of implementing and using either model in clinical practice, in terms of acceptability and uptake by clinicians and on patient outcomes, should be investigated.Trial registrationCurrent Controlled Trials ISRCTN12726986.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 22, No. 64. See the NIHR Journals Library website for further project information. Funding was also recieved from the NIHR Collaboration for Leadership in Applied Health Research, Care Oxford at Oxford Health NHS Foundation Trust, NIHR Biomedical Research Centre, Oxford, and the NIHR Fellowship programme.
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