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Prognostic models for identifying risk of poor outcome in people with acute ankle sprains : the SPRAINED development and external validation study
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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 L., Wilson, David, Collins, Gary S. and Lamb, S. E. (Sallie E.) (2018) Prognostic models for identifying risk of poor outcome in people with acute ankle sprains : the SPRAINED development and external validation study. Health Technology Assessment, 22 (64). pp. 1-112. doi:10.3310/hta22640
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Official URL: http://dx.doi.org/10.3310/hta22640
Abstract
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.
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > RD Surgery | |||||||||
| Divisions: | Faculty of Medicine > Warwick Medical School | |||||||||
| Library of Congress Subject Headings (LCSH): | Sprains, Ankle -- Diseases | |||||||||
| Journal or Publication Title: | Health Technology Assessment | |||||||||
| Publisher: | NIHR Health Technology Assessment programme | |||||||||
| ISSN: | 1366-5278 | |||||||||
| Official Date: | November 2018 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 7 January 2019 | |||||||||
| Volume: | 22 | |||||||||
| Number: | 64 | |||||||||
| Page Range: | pp. 1-112 | |||||||||
| DOI: | 10.3310/hta22640 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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