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.

Optimizing human thermal comfort and mitigating the urban heat island effect on public open spaces in Rome, Italy through sustainable design strategies.

The climate affects how a city's outdoor spaces are utilized. It is more likely that people will use and appreciate public areas designed for pedestrian use, such as parks, squares, streets, and foot-cycle pathways, when they provide a comfortable and healthy environment. A predicted increase in global temperature has made the climate uncomfortable, especially during the summer when heat stress is strengthened and anticipated. This phenomenon is more severe in urban areas, often affected by the Urban Heat Island (UHI) effect. Since the spatial characteristics of a city influence its climate, urban design can be deployed to mitigate the combined effects of climate change and UHI. This research is conducted to study the UHI effect on thermal comfort in an urban open space in Rome (Italy) and aims at identifying and implementing a methodology that urban designers can follow to reduce the impact of urban heat islands and increase thermal comfort in urban outdoor space. This study is based on an urban design concept adopting the Sustainable Development Goals as guidelines; it investigates how UHI's effect affects the use of public space and examines the influence of urban microclimatic conditions on the thermal perception of users through PET, PMV and PPD values, that were assessed through simulations with ENVI-MET software. The study thus proposes a redesign for the site in Rome, with a masterplan based on sustainable design principles, aimed at improving the microclimatic conditions in the site. The design solution was then validated through ex post simulations.

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.

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.

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.

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.

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.