AbstractBackgroundFoot progression deviations are a common and important problem for children with CP. Tibial and femoral derotational osteotomies (TDO, FDO) are used to treat foot progression deviations, but outcomes are unpredictable. The available evidence for the causal effects of TDO and FDO is limited and weak, and thus modeling approaches are needed.MethodsWe queried our clinical database for individuals with a diagnosis of cerebral palsy (CP) who were less than 18 years old and had baseline and follow up gait data collected within a 3-year time span. We then used the Bayesian Causal Forest (BCF) algorithm to estimate the causal treatment effects of TDO and FDO on foot progression deviations (separate models). We examined average treatment effects and heterogeneous treatment effects (HTEs) with respect to clinically relevant covariates.ResultsThe TDO and FDO models were able to accurately predict follow-up foot progression (r2 ∼0.7, RMSE ∼8°). The estimated causal effect of TDO was bimodal and exhibited significant heterogeneity with respect to baseline levels of foot progression and tibial torsion as well as changes in tibial torsion at follow-up. The estimated causal effect of FDO was unimodal and largely homogeneous with respect to baseline or change characteristics.ConclusionsThis study demonstrated the potential for causal machine-learning algorithms to impact treatment in children with CP. The causal model is accurate and appears sensible – though no gold-standard exists for validating the causal estimates. The model results can provide guidance for planning surgical corrections, and partly explain unsatisfactory outcomes observed in prior observational clinical studies.
Tibial Torsion in Cerebral Palsy: Validity and Reliability of Measurement