Prognostic models for identifying risk of poor outcome in people with acute ankle sprains: the SPRAINED development and external validation study

BACKGROUND: Ankle sprains are very common injuries. Although recovery can occur within weeks, around one-third of patients have longer-term problems. OBJECTIVES: To develop and externally validate a prognostic model for identifying people at increased risk of poor outcome after an acute ankle sprain. DESIGN: Development of a prognostic model in a clinical trial cohort data set and external validation in a prospective cohort study. SETTING: Emergency departments (EDs) in the UK. PARTICIPANTS: Adults with an acute ankle sprain (within 7 days of injury). SAMPLE SIZE: There were 584 clinical trial participants in the development data set and 682 recruited for the external validation study. PREDICTORS: Candidate 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 MEASURES: Models 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. ANALYSIS: Multiple 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 combined c-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 combined c-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 combined c-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 combined c-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. CONCLUSIONS: The 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. LIMITATIONS: The 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 WORK: Further 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 REGISTRATION: Current Controlled Trials ISRCTN12726986. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health 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

Spatio-temporal Models of Lymphangiogenesis in Wound Healing

Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis), very few have been proposed for the regeneration of the lymphatic network. Lymphangiogenesis is a markedly different process from angiogenesis, occurring at different times and in response to different chemical stimuli. Two main hypotheses have been proposed: 1) lymphatic capillaries sprout from existing interrupted ones at the edge of the wound in analogy to the blood angiogenesis case; 2) lymphatic endothelial cells first pool in the wound region following the lymph flow and then, once sufficiently populated, start to form a network. Here we present two PDE models describing lymphangiogenesis according to these two different hypotheses. Further, we include the effect of advection due to interstitial flow and lymph flow coming from open capillaries. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from biological data. The models are then solved numerically and the results are compared with the available biological literature.Comment: 29 pages, 9 Figures, 6 Tables (39 figure files in total

Primary staging and follow-up in melanoma patients – monocenter evaluation of methods, costs and patient survival

In a German cohort of 661 melanoma patients the performance, costs and survival benefits of staging methods (history and physical examination; chest X-ray; ultrasonography of the abdomen; high resolution sonography of the peripheral lymph nodes) were assessed at initial staging and during follow-up of stage I/II+III disease. At initial staging, 74% (23 out of 31) of synchronous metastases were first detected by physical examination followed by sonography of the lymph nodes revealing 16% (5 out of 31). Other imaging methods were less efficient (Chest X-ray: one out of 31; sonography of abdomen: two out of 31). Nearly 24% of all 127 first recurrences and 18% of 73 second recurrences developed in patients not participating in the follow-up programme. In follow-up patients detection of first or second recurrence were attributed to history and physical examination on a routine visit in 47 and 52% recurrences, respectively, and to routine imaging procedures in 21 and 17% of cases, respectively. Lymph node sonography was the most successful technical staging procedure indicating 13% of first relapses, but comprised 24% of total costs of follow-up in stage I/II. Routine imaging comprised nearly 50% of total costs for follow-up in stage I/II and in stage III. The mode of detecting a relapse (‘patient vs. doctor-diagnosed’ or ‘symptomatic vs asymptomatic’) did not significantly influence patients overall survival. Taken together, imaging procedures for routine follow-up in stage I/II and stage III melanoma patients were inefficient and not cost-efficient

Breath analysis to detect recent exposure to carbon monoxide

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