PredictMed: A Machine Learning Model for Identifying Risk Factors of Neuromuscular Hip Dysplasia: A Multicenter Descriptive Study

Abstract

Neuromuscular hip dysplasia (NHD) is a common and severe problem in patients with cerebral palsy (CP). Previous studies have so far identified only spasticity (SP) and high levels of Gross Motor Function Classification System as factors associated with NHD. The aim of this study is to develop a machine learning model to identify additional risk factors of NHD. This was a cross-sectional multicenter descriptive study of 102 teenagers with CP (60 males, 42 females; 60 inpatients, 42 outpatients; mean age 16.5 ± 1.2 years, range 12–18 years). Data on etiology, diagnosis, SP, epilepsy (E), clinical history, and functional assessments were collected between 2007 and 2017. Hip dysplasia was defined as femoral head lateral migration percentage > 33% on pelvic radiogram. A logistic regression-prediction model named PredictMed was developed to identify risk factors of NHD. Twenty-eight (27%) teenagers with CP had NHD, of which 18 (67%) had dislocated hips. Logistic regression model identified poor walking abilities (p < 0.001; odds ratio [OR] infinity; 95% confidence interval [CI] infinity), scoliosis (p = 0.01; OR 3.22; 95% CI 1.30–7.92), trunk muscles' tone disorder (p = 0.002; OR 4.81; 95% CI 1.75–13.25), SP (p = 0.006; OR 6.6; 95% CI 1.46–30.23), poor motor function (p = 0.02; OR 5.5; 95% CI 1.2–25.2), and E (p = 0.03; OR 2.6; standard error 0.44) as risk factors of NHD. The accuracy of the model was 77%. PredictMed identified trunk muscles' tone disorder, severe scoliosis, E, and SP as risk factors of NHD in teenagers with CP.

Keywords

machine learning - prediction model - cerebral palsy - neuromuscular hip dysplasia

Publikationsverlauf

Eingereicht: 27. Mai 2020

Angenommen: 24. September 2020

Artikel online veröffentlicht:
22. Dezember 2020

• References

• 1 Evans PM, Evans SJ, Alberman E. Cerebral palsy: why we must plan for survival. Arch Dis Child 1990; 65 (12) 1329-1333
• 2 Hägglund G, Lauge-Pedersen H, Wagner P. Characteristics of children with hip displacement in cerebral palsy. BMC Musculoskelet Disord 2007; 8: 101
• 3 Chang CH, Wang YC, Ho PC. et al. Determinants of hip displacement in children with cerebral palsy. Clin Orthop Relat Res 2015; 473 (11) 3675-3681
• 4 Terjesen T. The natural history of hip development in cerebral palsy. Dev Med Child Neurol 2012; 54 (10) 951-957
• 5 Huser A, Mo M, Hosseinzadeh P. Hip surveillance in children with cerebral palsy. Orthop Clin North Am 2018; 49 (02) 181-190
• 6 O'Sullivan R, Walsh M, Hewart P, Jenkinson A, Ross LA, O'Brien T. Factors associated with internal hip rotation gait in patients with cerebral palsy. J Pediatr Orthop 2006; 26 (04) 537-541
• 7 Winter RB, Pinto WC. Pelvic obliquity. Its causes and its treatment. Spine 1986; 11 (03) 225-234
• 8 Bertoncelli CM, Bertoncelli D, Elbaum L. et al. Validation of a clinical prediction model for the development of neuromuscular scoliosis. A multinational study. Pediatr Neurol 2018; 79: 14-20
• 9 Hermanson M, Hägglund G, Riad J, Rodby-Bousquet E, Wagner P. Prediction of hip displacement in children with cerebral palsy: development of the CPUP hip score. Bone Joint J 2015; 97-B (10) 1441-1444
• 10 Miller SD, Juricic M, Hesketh K. et al. Prevention of hip displacement in children with cerebral palsy: a systematic review. Dev Med Child Neurol 2017; 59 (11) 1130-1138
• 11 Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 2009; 45 (1, Suppl): S199-S209
• 12 Gmelig Meyling C, Ketelaar M, Kuijper MA, Voorman J, Buizer AI. Effects of postural management on hip migration in children with cerebral palsy: a systematic review. Pediatr Phys Ther 2018; 30 (02) 82-91
• 13 Hastie T, Tibshirani R, Friedman J. Boosting and additive trees. In: The Elements of Statistical Learning. 2nd ed.. New York: Springer-Verlag; 2009
• 14 Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015; 350: g7594
• 15 Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 2009; 45 (1, Suppl): S199-S209
• 16 Vapnik V. The Nature of Statistical Learning Theory. New York, NY: Springer Science & Business Media; 2013
• 17 Hareb F, Bertoncelli CM, Rosello O, Rampal V, Solla F. Botulinum toxin in children with cerebral palsy: an update. Neuropediatrics 2020; 51 (01) 1-5
• 18 Bertoncelli CM, Solla F, Loughenbury PR, Tsirikos AI, Bertoncelli D, Rampal V. Risk factors for developing scoliosis in cerebral palsy: a cross sectional descriptive study. J Child Neurol 2017; 32 (07) 657-662
• 19 Bertoncelli CM, Altamura P, Vieira ER. et al. Predictive model for gastrostomy placement in adolescents with developmental disabilities and cerebral palsy. Nutr Clin Pract 2020; 35 (01) 149-156
• 20 Bertoncelli CM, Altamura P, Vieira ER, Bertoncelli D, Thummler S, Solla F. Identifying factors associated with intellectual disabilities in teenagers with cerebral palsy using a predictive learning model. J Child Neurol 2019; 34 (04) 221-229
• 21 Gainsborough M, Surman G, Maestri G, Colver A, Cans C. Validity and reliability of the guidelines of the surveillance of cerebral palsy in Europe for the classification of cerebral palsy. Dev Med Child Neurol 2008; 50 (11) 828-831
• 22 Sæther R, Jørgensen L. Intra- and inter-observer reliability of the Trunk Impairment Scale for children with cerebral palsy. Res Dev Disabil 2011; 32 (02) 727-739
• 23 Dequeker G, Van Campenhout A, Feys H, Molenaers G. Evolution of self-care and functional mobility after single-event multilevel surgery in children and adolescents with spastic diplegic cerebral palsy. Dev Med Child Neurol 2018; 60 (05) 505-512
• 24 Dingemans SA, Kleipool SC, Mulders MAM. et al. Normative data for the Lower Extremity Functional Scale (LEFS). Acta Orthop 2017; 88 (04) 422-426
• 25 Holmes C, Brock K, Morgan P. Postural asymmetry in non-ambulant adults with cerebral palsy: a scoping review. Disabil Rehabil 2018; 41 (09) 1079-1088
• 26 McCarthy JJ, D'Andrea LP, Betz RR, Clements DH. Scoliosis in the child with cerebral palsy. J Am Acad Orthop Surg 2006; 14 (06) 367-375
• 27 Berg AT. Defining intractable epilepsy. Adv Neurol 2006; 97: 5-10
• 28 Sinha S, Siddiqui KA. Definition of intractable epilepsy. Neurosciences (Riyadh) 2011; 16 (01) 3-9
• 29 Berg AT. Identification of pharmacoresistant epilepsy. Neurol Clin 2009; 27 (04) 1003-1013
• 30 Harris-Hayes M, Steger-May K, van Dillen LR. et al. Reduced hip adduction is associated with improved function after movement-pattern training in young people with chronic hip joint pain. J Orthop Sports Phys Ther 2018; 48 (04) 316-324
• 31 Willoughby K, Jachno K, Ang SG, Thomason P, Graham HK. The impact of complementary and alternative medicine on hip development in children with cerebral palsy. Dev Med Child Neurol 2013; 55 (05) 472-479
• 32 Solla F, Tran A, Bertoncelli D, Musoff C, Bertoncelli CM. Why a p-value is not enough. Clin Spine Surg 2018; 31 (09) 385-388
• 33 Sullivan KM, Dean A, Soe MM. OpenEpi: a web-based epidemiologic and statistical calculator for public health. Public Health Rep 2009; 124 (03) 471-474
• 34 Wen Z, Zeng N, Wang N. Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. NESUG Proceedings: Health Care and Life Sciences, Baltimore, Maryland. 2010: 1-9
• 35 Robert JT. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. Sausalito, CA: University Science Books; 1999: 128-129
• 36 Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989; 129 (01) 125-137
• 37 Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol 1993; 138 (11) 923-936
• 38 Wordie SJ, Robb JE, Hägglund G, Bugler KE, Gaston MS. Hip displacement and dislocation in a total population of children with cerebral palsy in Scotland. Bone Joint J 2020; 102-B [phone]
• 39 Pruszczynski B, Sees J, Miller F. Risk factors for hip displacement in children with cerebral palsy: systematic review. J Pediatr Orthop 2016; 36 (08) 829-833