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

Carlo M. Bertoncelli; Paola Altamura; Domenico Bertoncelli; Virginie Rampal; Edgar Ramos Vieira; Federico Solla

doi:10.1055/s-0040-1721703

Neuropediatrics, Inhaltsverzeichnis

Neuropediatrics 2021; 52(05): 343-350
DOI: 10.1055/s-0040-1721703

Original Article

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

Autor*innen

Carlo M. Bertoncelli

¹Department of Physical Therapy, Nicole Wertheim College of Nursing & Health Sciences, Florida International University, Miami, Florida, United States

²E.E.A.P. H. Germain, Children Hospital, PredictMed Lab, Nice, France
Paola Altamura

³Department of Medicinal Chemistry and Pharmaceutical Technology, University of Chieti, Chieti, Italy
Domenico Bertoncelli

⁴Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
Virginie Rampal

⁵Department of Pediatric Orthopaedic Surgery, Lenval Children's University Hospital of Nice, Nice, France
Edgar Ramos Vieira

¹Department of Physical Therapy, Nicole Wertheim College of Nursing & Health Sciences, Florida International University, Miami, Florida, United States
Federico Solla

⁵Department of Pediatric Orthopaedic Surgery, Lenval Children's University Hospital of Nice, Nice, France

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

Volltext

Referenzen

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