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

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.

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy.

Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to investigate this field and this population in particular. The aim of this study is to develop and test a predictive learning model to identify factors associated with ASD in adolescents with CP. This was a multicenter controlled cohort study of 102 adolescents with CP (61 males, 41 females; mean age ± SD [standard deviation] = 16.6 ± 1.2 years; range: 12-18 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected between 2005 and 2015. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with ASD. A predictive learning model was implemented to identify factors associated with ASD. The guidelines of the "transparent reporting of a multivariable prediction model for individual prognosis or diagnosis" (TRIPOD) statement were followed. Type of spasticity (hemiplegia > diplegia > tri/quadriplegia; OR [odds ratio] = 1.76, SE [standard error] = 0.2785, p = 0.04), communication disorders (OR = 7.442, SE = 0.59, p < 0.001), intellectual disability (OR = 2.27, SE = 0.43, p = 0.05), feeding abilities (OR = 0.35, SE = 0.35, p = 0.002), and motor function (OR = 0.59, SE = 0.22, p = 0.01) were significantly associated with ASD. The best average prediction model score for accuracy, specificity, and sensitivity was 75%. Motor skills, feeding abilities, type of spasticity, intellectual disability, and communication disorders were associated with ASD. The prediction model was able to adequately identify adolescents at risk of ASD.

PredictMed-epilepsy: A multi-agent based system for epilepsy detection and prediction in neuropediatrics.

BACKGROUND AND OBJECTIVE: Epileptic seizures are associated with a higher incidence of Developmental Disabilities and Cerebral Palsy. Early evaluation and management of epilepsy is strongly recommended. We propose and discuss an application to predict epilespy (PredictMed-Epilepsy) and seizures via a deep-learning module (PredictMed-Seizures) encompassed within a multi-agent based healthcare system (PredictMed-MHS); this system is meant, in perspective, to be integrated into a clinical decision support system (PredictMed-CDSS). PredictMed-Epilespy, in particular, aims to identify factors associated with epilepsy in children with Developmental Disabilities and Cerebral Palsy by using a prediction-learning model named PredictMed. PredictMed-epilespy methods: We performed a longitudinal, multicenter, double-blinded, descriptive study of one hundred and two children with Developmental Disabilities and Cerebral Palsy (58 males, 44 females; 65 inpatients, 37 outpatients; 72 had epilepsy - 22 of intractable epilepsy, age: 16.6±1.2y, range: 12-18y). Data from 2005 to 2021 on Cerebral Palsy etiology, diagnosis, type of epilepsy and spasticity, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected. The machine-learning model PredictMed was exploited to identify factors associated with epilepsy. The guidelines of the "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" Statement (TRIPOD) were followed. PredictMed-epilepsy results: Cerebral Palsy etiology [(prenatal > perinatal > postnatal causes) p=0.036], scoliosis (p=0.048), communication (p=0.018) and feeding disorders (p=0.002), poor motor function (p<0.001), intellectual disabilities (p=0.007), and type of spasticity [(quadriplegia/triplegia > diplegia > hemiplegia), p=0.002)] were associated with having epilepsy. The prediction model scored an average of 82% of accuracy, sensitivity, and specificity. Thus, PredictMed defined the computational phenotype of children with Developmental Disabilities/Cerebral Palsy at risk of epilepsy. Novel contribution of the work: We have been developing and we have prototypically implemented a Multi-Agent Systems (MAS) that encapsulates the PredictMed-Epilepsy module. More specifically, we have implemented the Patient Observing MAS (PoMAS), which, as a novelty w.r.t. the existing literature, includes a complex event processing module that provides real-time detention of short- and long-term events related to the patient's condition.

Identifying Postural Instability in Children with Cerebral Palsy Using a Predictive Model: A Longitudinal Multicenter Study.

Insufficient postural control and trunk instability are serious concerns in children with cerebral palsy (CP). We implemented a predictive model to identify factors associated with postural impairments such as spastic or hypotonic truncal tone (TT) in children with CP. We conducted a longitudinal, double-blinded, multicenter, descriptive study of 102 teenagers with CP with cognitive impairment and severe motor disorders with and without truncal tone impairments treated in two specialized hospitals (60 inpatients and 42 outpatients; 60 males, mean age 16.5 ± 1.2 years, range 12 to 18 yrs). Clinical and functional data were collected between 2006 and 2021. TT-PredictMed, a multiple logistic regression prediction model, was developed to identify factors associated with hypotonic or spastic TT following the guidelines of "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis". Predictors of hypotonic TT were hip dysplasia (p = 0.01), type of etiology (postnatal > perinatal > prenatal causes; p = 0.05), male gender, and poor manual (p = 0.01) and gross motor function (p = 0.05). Predictors of spastic TT were neuromuscular scoliosis (p = 0.03), type of etiology (prenatal > perinatal > postnatal causes; p < 0.001), spasticity (quadri/triplegia > diplegia > hemiplegia; p = 0.05), presence of dystonia (p = 0.001), and epilepsy (refractory > controlled, p = 0.009). The predictive model's average accuracy, sensitivity, and specificity reached 82%. The model's accuracy aligns with recent studies on applying machine learning models in the clinical field.

Prediction Model for Identifying Computational Phenotypes of Children with Cerebral Palsy Needing Neurotoxin Treatments.

Factors associated with neurotoxin treatments in children with cerebral palsy (CP) are poorly studied. We developed and externally validated a prediction model to identify the prognostic phenotype of children with CP who require neurotoxin injections. We conducted a longitudinal, international, multicenter, double-blind descriptive study of 165 children with CP (mean age 16.5 ± 1.2 years, range 12−18 years) with and without neurotoxin treatments. We collected functional and clinical data from 2005 to 2020, entered them into the BTX-PredictMed machine-learning model, and followed the guidelines, "Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis". In the univariate analysis, neuromuscular scoliosis (p = 0.0014), equines foot (p < 0.001) and type of etiology (prenatal > peri/postnatal causes, p = 0.05) were linked with neurotoxin treatments. In the multivariate analysis, upper limbs (p < 0.001) and trunk muscle tone disorders (p = 0.02), the presence of spasticity (p = 0.01), dystonia (p = 0.004), and hip dysplasia (p = 0.005) were strongly associated with neurotoxin injections; and the average accuracy, sensitivity, and specificity was 75%. These results have helped us identify, with good accuracy, the clinical features of prognostic phenotypes of subjects likely to require neurotoxin injections.

Prediction Model for Identifying Factors Associated with Epilepsy in Children with Cerebral Palsy.

(1) Background: Cerebral palsy (CP) is associated with a higher incidence of epileptic seizures. This study uses a prediction model to identify the factors associated with epilepsy in children with CP. (2) Methods: This is a retrospective longitudinal study of the clinical characteristics of 102 children with CP. In the study, there were 58 males and 44 females, 65 inpatients and 37 outpatients, 72 had epilepsy, and 22 had intractable epilepsy. The mean age was 16.6 ± 1.2 years, and the age range for this study was 12−18 years. Data were collected on the CP etiology, diagnosis, type of epilepsy and spasticity, clinical history, communication abilities, behaviors, intellectual disability, motor function, and feeding abilities from 2005 to 2020. A prediction model, Epi-PredictMed, was implemented to forecast the factors associated with epilepsy. We used the guidelines of "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" (TRIPOD). (3) Results: CP etiology [(prenatal > perinatal > postnatal causes) p = 0.036], scoliosis (p = 0.048), communication (p = 0.018), feeding disorders (p = 0.002), poor motor function (p < 0.001), intellectual disabilities (p = 0.007), and the type of spasticity [(quadriplegia/triplegia > diplegia > hemiplegia), p = 0.002)] were associated with having epilepsy. The model scored an average of 82% for accuracy, sensitivity, and specificity. (4) Conclusion: Prenatal CP etiology, spasticity, scoliosis, severe intellectual disabilities, poor motor skills, and communication and feeding disorders were associated with epilepsy in children with CP. To implement preventive and/or management measures, caregivers and families of children with CP and epilepsy should be aware of the likelihood that these children will develop these conditions.

Predicting osteoarthritis in adults using statistical data mining and machine learning.

Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of OA in subjects aged 20-50 years and compare the performance of different machine learning models. Methods: We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20-50 years (n = 19,133), with or without OA. The supervised machine learning model 'Deep PredictMed' based on logistic regression, deep neural network (DNN), and support vector machine was used for identifying demographic and personal characteristics that are associated with OA. Finally, we compared the performance of the different models. Results: Being a female (p < 0.001), older age (p < 0.001), a smoker (p < 0.001), higher body mass index (p < 0.001), high blood pressure (p < 0.001), race/ethnicity (lowest risk among Mexican Americans, p = 0.01), and physical and mental limitations (p < 0.001) were associated with having OA. Best predictive performance yielded a 75% area under the receiver operating characteristic curve. Conclusion: Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20-50 years. The best predictive performance was achieved using DNN algorithms.

Predicting Hip Dysplasia in Teenagers with Cerebral Palsy in order to Optimize Prevention and Rehabilitation. A Longitudinal Descriptive Study.

OBJECTIVE: To develop a predictive model of neuromuscular hip dysplasia (NHD) in teenagers with cerebral palsy (CP) to optimize rehabilitation. DESIGN: A longitudinal, multicenter, double-blinded, descriptive study of one hundred and two teenagers with CP (age 16.5 ± 1.2 years, range 12-18 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, and functional assessments were collected from 2005 to 2017 and entered in the prediction model "PredictMed." RESULTS: Poor walking abilities [p < .001; Odd Ratio (OR) Infinity], scoliosis (p 0.01; OR 3.22), trunk muscles' tone disorder (p 0.002; OR 4.81), spasticity (p 0.006; OR 6.6), poor motor function (p 0.02; OR 5.5), and epilepsy (p 0.03; OR 2.6) were predictors of NHD development. The accuracy of the model was 77%. CONCLUSION: Trunk muscles' tone disorder, severe scoliosis, epilepsy, and spasticity were predictors of NHD in teenagers with CP. Based on the results we have developed appropriate preventative rehabilitation interventions.

Predictive Model for Gastrostomy Placement in Adolescents With Developmental Disabilities and Cerebral Palsy.

BACKGROUND: Factors associated with gastrostomy placement in adolescents with developmental disabilities (DDs) and cerebral palsy (CP) are poorly investigated. We aimed to develop and validate a machine learning (ML) model for gastrostomy placement in adolescents with DDs and CP. METHODS: We performed a multinational, double-blinded, case-control study including 130 adolescents with severe DD and CP (72 males, 58 females; mean age 16 ± 2 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, and functional assessments such as the Eating and Drinking Ability Classification System, Manual Ability Classification System, and Gross Motor Function Classification System were collected between 2005 and 2015. Analysis included Fisher exact test, multiple logistic regressions, and a supervised ML model, named PredictMed, to identify factors associated with gastrostomy placement. "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" guidelines were followed. RESULTS: Poor motor function (P < 0.001), trunk muscle tone disorder (P < 0.001), male gender (P < 0.01), epilepsy (P = 0.01), and severe neuromuscular scoliosis (P = 0.04) were factors linked with gastrostomy placement in univariate analysis. Epilepsy (P = 0.03), poor motor function (P = 0.04), and male gender (P = 0.04) were associated with gastrostomy placement in multivariate analysis with 95% accuracy. CONCLUSION: Epilepsy, poor motor function, trunk muscles tone disorder, and male gender were accurate, sensitive, and specific factors associated with gastrostomy need.

PredictMed: A logistic regression-based model to predict health conditions in cerebral palsy.

Logistic regression-based predictive models are widely used in the healthcare field but just recently are used to predict comorbidities in children with cerebral palsy. This article presents a logistic regression approach to predict health conditions in children with cerebral palsy and a few examples from recent research. The model named PredictMed was trained, tested, and validated for predicting the development of scoliosis, intellectual disabilities, autistic features, and in the present study, feeding disorders needing gastrostomy. This was a multinational, cross-sectional descriptive study. Data of 130 children (aged 12-18 years) with cerebral palsy were collected between June 2005 and June 2015. The logistic regression-based model uses an algorithm implemented in R programming language. After splitting the patients in training and testing sets, logistic regressions are performed on every possible subset (tuple) of independent variables. The tuple that shows the best predictive performance in terms of accuracy, sensitivity, and specificity is chosen as a set of independent variables in another logistic regression to calculate the probability to develop the specific health condition (e.g. the need for gastrostomy). The average of accuracy, sensitivity, and specificity score was 90%. Our model represents a novelty in the field of some cerebral palsy-related health outcomes treatment, and it should significantly help doctors' decision-making process regarding patient prognosis.

Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model.

BACKGROUND: Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy. METHODS: This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as "mild," "moderate," "severe," or "profound" based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the "Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement" were followed. RESULTS: Poor manual abilities (P ≤ .001), gross motor function (P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%. CONCLUSION: Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

Why a P-Value is Not Enough.

BACKGROUND: All doctors know that P-value<0.05 is "the Graal," but publications require further parameters [odds ratios, confidence interval (CI), etc.] to better analyze scientific data. AIM: The aim of this study was to present P-values, CI, and common effect-sizes (Cohen d, odds ratio, and various coefficients) in a simple way. DESCRIPTION: The P-value is the probability, when the null hypothesis is true (eg, no difference or no association), of obtaining a result equal to or more extreme than what we actually observed. Simplistically, P-value quantifies the probability that the result is due to chance. It does not measure how big the association or the difference is. The CI on a value describes the probability that the true value is within a given range. A 95% CI means that the CI covers the true value in 95 of 100 performed studies. The test is significant if the CI does not include the null hypothesized difference or association (eg, 0 for difference). The effect-sizes are quantitative measures of the strength of a difference or association. If the P-value is <0.05 but the effect size is very low, the test is statistically significant but probably, clinically not so. CONCLUSIONS: Scientific publications require more parameters than a P-value. Statistical results should also include effect sizes and CIs to allow for a more complete, honest, and useful interpretation of scientific findings.

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

BACKGROUND: The objective of this study was to evaluate the performance of a clinical prediction model of neuromuscular scoliosis via external validation. METHODS: We analyzed a series of 120 patients (mean age ± standard deviation, 15.7 ± 1.8 years; range: 12 to 18 years) with cerebral palsy, severe motor disorders, and cognitive impairment with and without neuromuscular scoliosis treated in two specialized units (70 patients from Nice, France, and 50 patients from Lublin, Poland) in a cross-sectional, double-blind study. Data on etiology, diagnosis, functional assessments, type of spasticity, epilepsy, scoliosis, and clinical history were collected prospectively between 2005 and 2015. Fisher's exact test and multiple logistic regressions were used to identify influential factors for developing spinal deformity. Thus, we applied a predictive model based on a logistic regression algorithm to predict the probability of scoliosis onset for new patients. RESULTS: Children with truncal tone disorders (P = 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 were 74%. CONCLUSIONS: We validated a prediction model of neuromuscular scoliosis. In cerebral palsy subjects with the previouslymentioned predictors of scoliosis, the frequency of clinical examinations of spine and spinal x-ray should be increased to easily identify candidates for treatment.

Risk Factors for Developing Scoliosis in Cerebral Palsy: A Cross-Sectional Descriptive Study.

This study aims to identify the risk factors leading to the development of severe scoliosis among children with cerebral palsy. A cross-sectional descriptive study of 70 children (aged 12-18 years) with severe spastic and/or dystonic cerebral palsy treated in a single specialist unit is described. Statistical analysis included Fisher exact test and logistic regression analysis to identify risk factors. Severe scoliosis is more likely to occur in patients with intractable epilepsy ( P = .008), poor gross motor functional assessment scores ( P = .018), limb spasticity ( P = .045), a history of previous hip surgery ( P = .048), and nonambulatory patients ( P = .013). Logistic regression model confirms the major risk factors are previous hip surgery ( P = .001), moderate to severe epilepsy ( P = .007), and female gender ( P = .03). History of previous hip surgery, intractable epilepsy, and female gender are predictors of developing severe scoliosis in children with cerebral palsy. This knowledge should aid in the early diagnosis of scoliosis and timely referral to specialist services.