3 recherche sur le mot-clé 'developmental milestones'

Utilization of the Maternal and Child Health Handbook in Early Identification of Autism Spectrum Disorder and Other Neurodevelopmental Disorders / Tomoya HIROTA in Autism Research, 14-3 (March 2021)  

Public ISBD [article]  inAutism Research > 14-3 (March 2021) . - p.551-559 Titre : Utilization of the Maternal and Child Health Handbook in Early Identification of Autism Spectrum Disorder and Other Neurodevelopmental Disorders Type de document : Texte imprimé et/ou numérique Auteurs : Tomoya HIROTA, Auteur ; Somer L. BISHOP, Auteur ; Masaki ADACHI, Auteur ; Amy SHUI, Auteur ; Michio TAKAHASHI, Auteur ; Hiroyuki MORI, Auteur ; Kazuhiko NAKAMURA, Auteur Article en page(s) : p.551-559 Langues : Anglais (eng) Mots-clés : developmental milestones  early identification  neurodevelopmental disorders  parental concerns  universal healthcare Index. décimale : PER Périodiques Résumé : There is relatively little information about prospectively reported developmental milestones from caregivers of children who go on to be diagnosed with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). The current study examined rates of early skill attainment in 5-year-old children who participated in a comprehensive in-person assessment for NDDs in Hirosaki in Japan. Developmental milestone data were extracted from their Maternal and Child Health Handbook (MCHH), a booklet distributed to all pregnant women as part of universal health care. Seven hundred and twenty children underwent the assessment, among whom 455 received one or more NDD diagnoses (ASD: n = 124, non-ASD NDD: n = 331). Developmental skills were organized into four domains (motor, social interaction, communication, self-help), and the cumulative number of potential delays in each domain was calculated for each participant within three different age ranges (by 12?months, by 24?months, and by 36?months). Even by age 12?months, children with ASD/NDDs showed more potential delays across domains compared to those who received no NDD diagnosis. However, differences between those with ASD and those with non-ASD NDDs were not apparent until 24?months for social interaction and communication, and 36?months for self-help. These findings provide insights into specific behaviors that could be used to screen for ASD and other NDDs. In addition, the present study indicates the potential utility of the MCHH as a broadband screening tool to educate parents about what to look for in charting their child's early development. LAY SUMMARY: The present study examined prospectively charted developmental milestones from home-based records used as part of universal health care in 720 5-year-old children from Hirosaki, Japan. All children participated in a comprehensive evaluation to determine if they met criteria for a neurodevelopmental disorder (NDD), including autism spectrum disorder (ASD). Compared to those who received no NDD diagnosis, children with NDDs exhibited higher rates of potential delays across developmental domains, including social interaction, communication, and self-help. For some children, these delays were apparent before the age of 12?months. Differences between diagnostic groups became even more pronounced by 24 and 36?months, well before the average age of diagnosis. This suggests that home-based records can be useful tools to educate caregivers about what to look for in charting their child's early development and could assist with early screening efforts. En ligne : http://dx.doi.org/10.1002/aur.2442 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443 [article] Utilization of the Maternal and Child Health Handbook in Early Identification of Autism Spectrum Disorder and Other Neurodevelopmental Disorders [Texte imprimé et/ou numérique] / Tomoya HIROTA, Auteur ; Somer L. BISHOP, Auteur ; Masaki ADACHI, Auteur ; Amy SHUI, Auteur ; Michio TAKAHASHI, Auteur ; Hiroyuki MORI, Auteur ; Kazuhiko NAKAMURA, Auteur . - p.551-559. Langues : Anglais (eng) in Autism Research > 14-3 (March 2021) . - p.551-559 Mots-clés : developmental milestones  early identification  neurodevelopmental disorders  parental concerns  universal healthcare Index. décimale : PER Périodiques Résumé : There is relatively little information about prospectively reported developmental milestones from caregivers of children who go on to be diagnosed with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). The current study examined rates of early skill attainment in 5-year-old children who participated in a comprehensive in-person assessment for NDDs in Hirosaki in Japan. Developmental milestone data were extracted from their Maternal and Child Health Handbook (MCHH), a booklet distributed to all pregnant women as part of universal health care. Seven hundred and twenty children underwent the assessment, among whom 455 received one or more NDD diagnoses (ASD: n = 124, non-ASD NDD: n = 331). Developmental skills were organized into four domains (motor, social interaction, communication, self-help), and the cumulative number of potential delays in each domain was calculated for each participant within three different age ranges (by 12?months, by 24?months, and by 36?months). Even by age 12?months, children with ASD/NDDs showed more potential delays across domains compared to those who received no NDD diagnosis. However, differences between those with ASD and those with non-ASD NDDs were not apparent until 24?months for social interaction and communication, and 36?months for self-help. These findings provide insights into specific behaviors that could be used to screen for ASD and other NDDs. In addition, the present study indicates the potential utility of the MCHH as a broadband screening tool to educate parents about what to look for in charting their child's early development. LAY SUMMARY: The present study examined prospectively charted developmental milestones from home-based records used as part of universal health care in 720 5-year-old children from Hirosaki, Japan. All children participated in a comprehensive evaluation to determine if they met criteria for a neurodevelopmental disorder (NDD), including autism spectrum disorder (ASD). Compared to those who received no NDD diagnosis, children with NDDs exhibited higher rates of potential delays across developmental domains, including social interaction, communication, and self-help. For some children, these delays were apparent before the age of 12?months. Differences between diagnostic groups became even more pronounced by 24 and 36?months, well before the average age of diagnosis. This suggests that home-based records can be useful tools to educate caregivers about what to look for in charting their child's early development and could assist with early screening efforts. En ligne : http://dx.doi.org/10.1002/aur.2442 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=443

Age of First Words Predicts Cognitive Ability and Adaptive Skills in Children with ASD / Jessica MAYO in Journal of Autism and Developmental Disorders, 43-2 (February 2013)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 43-2 (February 2013) . - p.253-264 Titre : Age of First Words Predicts Cognitive Ability and Adaptive Skills in Children with ASD Type de document : Texte imprimé et/ou numérique Auteurs : Jessica MAYO, Auteur ; Colby CHLEBOWSKI, Auteur ; Deborah A. FEIN, Auteur ; Inge-Marie EIGSTI, Auteur Année de publication : 2013 Article en page(s) : p.253-264 Langues : (Eng) Mots-clés : Autism  Autism spectrum disorders  Language acquisition  Language delay  Developmental milestones  Prognosis Index. décimale : PER Périodiques Résumé : Acquiring useful language by age 5 has been identified as a strong predictor of positive outcomes in individuals with Autism Spectrum Disorders (ASD). This study examined the relationship between age of language acquisition and later functioning in children with ASD (n = 119). First word acquisition at a range of ages was probed for its relationship to cognitive ability and adaptive behaviors at 52 months. Results indicated that although producing first words predicted better outcome at every age examined, producing first words by 24 months was a particularly strong predictor of better outcomes. This finding suggests that the historic criterion for positive prognosis (i.e., 'useful language by age 5') can be updated to a more specific criterion with an earlier developmental time point. En ligne : http://dx.doi.org/10.1007/s10803-012-1558-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=187 [article] Age of First Words Predicts Cognitive Ability and Adaptive Skills in Children with ASD [Texte imprimé et/ou numérique] / Jessica MAYO, Auteur ; Colby CHLEBOWSKI, Auteur ; Deborah A. FEIN, Auteur ; Inge-Marie EIGSTI, Auteur . - 2013 . - p.253-264. Langues : (Eng) in Journal of Autism and Developmental Disorders > 43-2 (February 2013) . - p.253-264 Mots-clés : Autism  Autism spectrum disorders  Language acquisition  Language delay  Developmental milestones  Prognosis Index. décimale : PER Périodiques Résumé : Acquiring useful language by age 5 has been identified as a strong predictor of positive outcomes in individuals with Autism Spectrum Disorders (ASD). This study examined the relationship between age of language acquisition and later functioning in children with ASD (n = 119). First word acquisition at a range of ages was probed for its relationship to cognitive ability and adaptive behaviors at 52 months. Results indicated that although producing first words predicted better outcome at every age examined, producing first words by 24 months was a particularly strong predictor of better outcomes. This finding suggests that the historic criterion for positive prognosis (i.e., 'useful language by age 5') can be updated to a more specific criterion with an earlier developmental time point. En ligne : http://dx.doi.org/10.1007/s10803-012-1558-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=187

Predicting autism traits from baby wellness records: A machine learning approach / Joshua GUEDALIA ; Keren ILAN ; Meirav SHAHAM ; Galit SHEFER ; Roe COHEN ; Yuval TAMIR ; Lidia V. GABIS in Autism, 28-12 (December 2024)  

Public ISBD [article]  inAutism > 28-12 (December 2024) . - p.3063-3077 Titre : Predicting autism traits from baby wellness records: A machine learning approach Type de document : Texte imprimé et/ou numérique Auteurs : Joshua GUEDALIA, Auteur ; Keren ILAN, Auteur ; Meirav SHAHAM, Auteur ; Galit SHEFER, Auteur ; Roe COHEN, Auteur ; Yuval TAMIR, Auteur ; Lidia V. GABIS, Auteur Article en page(s) : p.3063-3077 Langues : Anglais (eng) Mots-clés : autism spectrum conditions  developmental milestones  electronic health records  machine learning  screening Index. décimale : PER Périodiques Résumé : Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24?months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 (SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys' and girls' models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24?months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Lay abstract Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24?months, mother?s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life. En ligne : https://dx.doi.org/10.1177/13623613241253311 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=543 [article] Predicting autism traits from baby wellness records: A machine learning approach [Texte imprimé et/ou numérique] / Joshua GUEDALIA, Auteur ; Keren ILAN, Auteur ; Meirav SHAHAM, Auteur ; Galit SHEFER, Auteur ; Roe COHEN, Auteur ; Yuval TAMIR, Auteur ; Lidia V. GABIS, Auteur . - p.3063-3077. Langues : Anglais (eng) in Autism > 28-12 (December 2024) . - p.3063-3077 Mots-clés : autism spectrum conditions  developmental milestones  electronic health records  machine learning  screening Index. décimale : PER Périodiques Résumé : Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24?months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with a threefold cross-validation and SHAPley additive explanation tool quantified feature importance. The model had an average area under the curve of 0.81 (SD = 0.004). The high-likelihood group detected by the model had a 0.073 autism spectrum condition incidence rate; 3.42-fold more than in the entire cohort (0.02). Sex-specific models had higher specificity (0.81 boys and 0.79 girls) than sensitivity (0.64 boys and 0.66 girls). The common predictors were more parental concerns, older mothers, never nursing, lower initial and higher last weight percentiles, and several delayed milestones. SHAPley additive explanation tool results show common, important predictors in the full sample and separate boys' and girls' models. These included birth, growth, familial, postnatal parameters and delayed language, fine motor, and social milestones from 12 to 24?months. Machine learning algorithms can help detect increased autism signs by relying on the multidimensional data routinely recorded during the first 2 years. Lay abstract Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from routinely collected health information. This study tested a model that provides a likelihood score for autism diagnosis from baby wellness visit records collected during the first 2 years of life. It included records of 591,989 non-autistic children and 12,846 children with autism. The model identified two-thirds of the autism spectrum condition group (boys 63% and girls 66%). Sex-specific models had several predictive features in common. These included language development, fine motor skills, and social milestones from visits at 12-24?months, mother?s age, and lower initial growth but higher last growth measurements. Parental concerns about development or hearing impairment were other predictors. The models differed in other growth measurements and birth parameters. These models can support the detection of early signs of autism in girls and boys by using information routinely recorded during the first 2 years of life. En ligne : https://dx.doi.org/10.1177/13623613241253311 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=543