Prescriptive analytics is a concept combining statistical and computer sciences to prescribe an optimal course of action, based on predictions of possible future events. In this simulation study we investigate using prescriptive analytics to recommend optimal in-brace corrections for braced Adolescent Idiopathic Scoliosis (AIS) patients. The objectives were to estimate the efficacy of these recommendations, ultimately working toward improved brace design protocols.
Data was obtained for 90 AIS patients who had finished brace treatment at our center (60 full-time and 30 nighttime braces). Rates of ≥6 degree progression were 53% for daytime and 30% for nighttime braces. A modeling technique previously developed by our group was used to predict these patients’ likely treatment outcomes given a range of in-brace corrections – the model was blinded to the true outcomes during this process. Each patient’s ‘recommended’ correction was identified as the least aggressive correction resulting in a desirable predicted outcome.
The efficacy of these recommendations was estimated using a technique called “clinical trial simulation” (CTS). This technique used a statistical model to predict progression rate under the model-recommended treatment, and compared it to the true progression rate, observed retrospectively, under the actual treatment. Significance was calculated using a permutation test.
Model-recommended corrections ranged from 20%-58% for daytime and 65%-130% for nighttime braces, roughly corresponding with previous literature. Interestingly, in 37% of cases the recommended correction was less than that which had actually been applied, suggesting some opportunity for less aggressive (more comfortable) braces without compromising treatment outcome.
The CTS estimated 26% fewer progressive cases using the model-recommended in-brace correction, over the actual correction observed retrospectively in the charts (p=0.05). The patients whose correction decreased under the model’s recommendation did not show an increased progression rate.
Optimal correction may be less than the maximum achievable correction. The preliminary results suggest that considering model-generated recommendations during brace fitting could improve outcomes. Future work will expand the system to recommend wear-times as well as corrections, improving its clinical relevance. We envision this pilot demonstration to promote development of model-based decision support in scoliosis treatment, and prompt discussion on its future role.
Overall progression rates from the patient charts were compared to (predicted) progression rates under the recommended in-brace corrections; the difference in progression rate was measured. Progression was defined as a >5° increase in Cobb angle by the end of treatment . A permutation test calculated the confidence interval of the change in progression rate, based on the prediction model’s negative/positive predictive values.
Computer-generated in-brace correction recommendations ranged from 20%-58% for full-time braces, and 65%-130% for nighttime. In 37% of cases the recommended in-brace correction was lower than the actual correction which had been applied clinically, as recorded in the patient charts.
The CTS estimated 26% fewer progressions under the recommended in-brace correction, compared to the actual correction in patients’ charts. The 95% confidence interval ranged from 48% fewer to 4% more progressions. The estimated decrease in progression rate was statistically significant at p=0.05.
The CTS estimated that the computer-recommended in-brace corrections could reduce progressive cases by 26%. However it is unclear whether the recommended corrections would actually be achievable in practice: overall the recommendations agree with literature and corrections observed at our clinic, but some individual patients with stiff curves may not be capable of large corrections. Thus, what is perhaps most interesting is that many recommended corrections were lower than that actually applied. This may suggest some potential to build less aggressive (more comfortable) braces without compromising treatment outcome.
This study was performed using data from one center, and its limitations may affect the results’ generalization to other centers. Our patient sample was somewhat small, and involves two different brace types built by two orthotists. The patients’ compliance with brace-wear was unknown. Our approach may have unpredictable results at other centers, or on different patient groups.
The results of this pilot demonstration are promising, but the work is in its early stages with a significant amount of work to be done. The system will be expanded to recommend wear-times as well as corrections, producing a range of correction/wear-time combinations likely to result in success. This will be useful in cases where the “optimal” correction is not actually achievable. Also, this preliminary work used a clinical trial simulation; ultimately a prospective clinical trial will be necessary to prove clinical value.
This preliminary study suggests that computer-generated recommendations may improve treatment outcomes, and may safely reduce aggressiveness of treatment in some cases.
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