Prognostic models for identifying risk of poor outcome in people with acute ankle sprains: the SPRAINED development and external validation study
Authors: Keene David J , Schlüssel Michael M , Thompson Jacqueline , Hagan Daryl A , Williams Mark A , Byrne Christopher , Goodacre Steve , Cooke Matthew , Gwilym Stephen , Hormbrey Philip , Bostock Jennifer , Haywood Kirstie , Wilson David , Collins Gary S , Lamb Sarah E 
Published: November 2018

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 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.

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 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.