Validation of a Clinical Prediction Model for the Development of Neuromuscular Scoliosis. a Multinational Study

Publication date: Available online 20 November 2017
Source:Pediatric Neurology
Author(s): Carlo M. Bertoncelli, Domenico Bertoncelli, Leonard Elbaum, Michal Latalski, Paola Altamura, Charles Musoff, Virginie Rampal, Federico Solla
ObjectiveTo evaluate the performance of a clinical prediction model of neuromuscular scoliosis via external validation.Study DesignWe have analyzed a series of 120 patients (mean age ± SD, 15.7±1.8y; range: 12-18y) with cerebral palsy (CP), severe motor disorders, and cognitive impairment with and without neuromuscular scoliosis treated in 2 specialized units (70 patients from Nice, France and 50 from Lublin, Poland ) in a cross-sectional, double-blind study.Data on etiology, diagnosis, functional assessments, the type of spasticity, epilepsy, scoliosis, and clinical history were collected prospectively between 2005 and 2015.MethodFisher's exact test and multiple logistic regressions were used to identify influent factors for developing spinal deformity. Thus, we applied a predictive model based on a logistic regression algorithm in order to predict the probability of scoliosis onset for new patients.ResultsChildren with truncal tone disorders (p=<0.001) and poor motor function (p=<0.001) were four times more likely to develop neuromuscular scoliosis compared with similar group without these problems. Previous hip surgery (p=0.01), intractable epilepsy (p=0.03) and spasticity (p=0.04) were also associated with neuromuscular scoliosis. Multivariate logistic regression highlighted previous hip surgery (p=0.002≈0.005), intractable epilepsy (p=0.01≈0.04)and female gender (0.07) as influent factors in the two cohorts. Average accuracy, sensitivity, and specificity of the predictive model was 74%.ConclusionWe validated a prediction model of neuromuscular scoliosis. In CP subjects with the above predictors of scoliosis, the frequency of clinical examinations of spine and spinal X-ray should be increased to easily identify candidates to treatment.



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