124 recherche sur le mot-clé 'phenotype'

Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records / Mengge ZHAO in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records Type de document : texte imprimé Auteurs : Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records [texte imprimé] / Mengge ZHAO, Auteur ; James HAVRILLA, Auteur ; Jacqueline PENG, Auteur ; Madison DRYE, Auteur ; Maddie FECHER, Auteur ; Whitney GUTHRIE, Auteur ; Birkan TUNC, Auteur ; Robert SCHULTZ, Auteur ; Kai WANG, Auteur ; Yunyun ZHOU, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Autism Spectrum Disorder/diagnosis/genetics  Electronic Health Records  Humans  Natural Language Processing  Phenotype  Vocabulary  Autism  Autism spectrum disorder  Electronic health record  Natural language processing  Phenotype ontology  Terminology set Index. décimale : PER Périodiques Résumé : BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives. METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method. RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders. CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making. En ligne : https://dx.doi.org/10.1186/s11689-022-09442-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

Exploring the Relationship Between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis / Michael L. CUCCARO in Journal of Autism and Developmental Disorders, 42-8 (August 2012)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 42-8 (August 2012) . - p.1630-1641 Titre : Exploring the Relationship Between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis Type de document : texte imprimé Auteurs : Michael L. CUCCARO, Auteur ; Roberto TUCHMAN, Auteur ; Kara L. HAMILTON-NELSON, Auteur ; Harry H. WRIGHT, Auteur ; Ruth K. ABRAMSON, Auteur ; Jonathan L. HAINES, Auteur ; John R. GILBERT, Auteur ; Margaret A.O. PERICAK-VANCE, Auteur Année de publication : 2012 Article en page(s) : p.1630-1641 Langues : Anglais (eng) Mots-clés : Autism spectrum disorders  Epilepsy  Latent class cluster analysis  Phenotype Index. décimale : PER Périodiques Résumé : Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster solution with one cluster showing a high rate of epilepsy (29%), earlier age at first recognition, and high rates of repetitive object use and unusual sensory interests. We also conducted LCCA on an ASD-epilepsy subset from the overall dataset (N = 64) which yielded three clusters, the largest of which had impairments in language and motor development; the remaining clusters, while not as developmentally impaired were characterized by different levels of repetitive and sensory behaviors. En ligne : http://dx.doi.org/10.1007/s10803-011-1402-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=178 [article] Exploring the Relationship Between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis [texte imprimé] / Michael L. CUCCARO, Auteur ; Roberto TUCHMAN, Auteur ; Kara L. HAMILTON-NELSON, Auteur ; Harry H. WRIGHT, Auteur ; Ruth K. ABRAMSON, Auteur ; Jonathan L. HAINES, Auteur ; John R. GILBERT, Auteur ; Margaret A.O. PERICAK-VANCE, Auteur . - 2012 . - p.1630-1641. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 42-8 (August 2012) . - p.1630-1641 Mots-clés : Autism spectrum disorders  Epilepsy  Latent class cluster analysis  Phenotype Index. décimale : PER Périodiques Résumé : Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster solution with one cluster showing a high rate of epilepsy (29%), earlier age at first recognition, and high rates of repetitive object use and unusual sensory interests. We also conducted LCCA on an ASD-epilepsy subset from the overall dataset (N = 64) which yielded three clusters, the largest of which had impairments in language and motor development; the remaining clusters, while not as developmentally impaired were characterized by different levels of repetitive and sensory behaviors. En ligne : http://dx.doi.org/10.1007/s10803-011-1402-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=178

The Study to Explore Early Development (SEED): A Multisite Epidemiologic Study of Autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) Network / Diana SCHENDEL in Journal of Autism and Developmental Disorders, 42-10 (October 2012)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 42-10 (October 2012) . - p.2121-2140 Titre : The Study to Explore Early Development (SEED): A Multisite Epidemiologic Study of Autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) Network Type de document : texte imprimé Auteurs : Diana SCHENDEL, Auteur ; Carolyn G. DIGUISEPPI, Auteur ; Lisa A. CROEN, Auteur ; M. Daniele FALLIN, Auteur ; Phil REED, Auteur ; Laura A. SCHIEVE, Auteur ; Lisa D. WIGGINS, Auteur ; Julie L. DANIELS, Auteur ; Judith K. GRETHER, Auteur ; Susan E. LEVY, Auteur ; Lisa MILLER, Auteur ; Craig J. NEWSCHAFFER, Auteur ; Jennifer A. PINTO-MARTIN, Auteur ; Cordelia ROBINSON, Auteur ; Gayle C. WINDHAM, Auteur ; Aimee A. ALEXANDER, Auteur ; Arthur S. AYLSWORTH, Auteur ; Pilar BERNAL, Auteur ; Joseph D. BONNER, Auteur ; Lisa BLASKEY, Auteur ; Chyrise BRADLEY, Auteur ; Jack COLLINS, Auteur ; Casara J. FERRETTI, Auteur ; Homayoon FARZADEGAN, Auteur ; Ellen GIARELLI, Auteur ; Marques HARVEY, Auteur ; Susan HEPBURN, Auteur ; Matthew HERR, Auteur ; Kristina KAPARICH, Auteur ; Rebecca LANDA, Auteur ; Li-Ching LEE, Auteur ; Brooke LEVENSELLER, Auteur ; Stacey MEYERER, Auteur ; Mohammad H. RAHBAR, Auteur ; Andria RATCHFORD, Auteur ; Ann REYNOLDS, Auteur ; Steven A. ROSENBERG, Auteur ; Julie RUSYNIAK, Auteur ; Stuart K. SHAPIRA, Auteur ; Karen S. SMITH, Auteur ; Margaret SOUDERS, Auteur ; Patrick Aaron THOMPSON, Auteur ; Lisa YOUNG, Auteur ; Marshalyn YEARGIN-ALLSOPP, Auteur Année de publication : 2012 Article en page(s) : p.2121-2140 Langues : Anglais (eng) Mots-clés : Autism  Epidemiology  Study methods  Risk factors  Phenotype Index. décimale : PER Périodiques Résumé : The Study to Explore Early Development (SEED), a multisite investigation addressing knowledge gaps in autism phenotype and etiology, aims to: (1) characterize the autism behavioral phenotype and associated developmental, medical, and behavioral conditions and (2) investigate genetic and environmental risks with emphasis on immunologic, hormonal, gastrointestinal, and sociodemographic characteristics. SEED uses a case–control design with population-based ascertainment of children aged 2–5 years with an autism spectrum disorder (ASD) and children in two control groups—one from the general population and one with non-ASD developmental problems. Data from parent-completed questionnaires, interviews, clinical evaluations, biospecimen sampling, and medical record abstraction focus on the prenatal and early postnatal periods. SEED is a valuable resource for testing hypotheses regarding ASD characteristics and causes. En ligne : http://dx.doi.org/10.1007/s10803-012-1461-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=180 [article] The Study to Explore Early Development (SEED): A Multisite Epidemiologic Study of Autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) Network [texte imprimé] / Diana SCHENDEL, Auteur ; Carolyn G. DIGUISEPPI, Auteur ; Lisa A. CROEN, Auteur ; M. Daniele FALLIN, Auteur ; Phil REED, Auteur ; Laura A. SCHIEVE, Auteur ; Lisa D. WIGGINS, Auteur ; Julie L. DANIELS, Auteur ; Judith K. GRETHER, Auteur ; Susan E. LEVY, Auteur ; Lisa MILLER, Auteur ; Craig J. NEWSCHAFFER, Auteur ; Jennifer A. PINTO-MARTIN, Auteur ; Cordelia ROBINSON, Auteur ; Gayle C. WINDHAM, Auteur ; Aimee A. ALEXANDER, Auteur ; Arthur S. AYLSWORTH, Auteur ; Pilar BERNAL, Auteur ; Joseph D. BONNER, Auteur ; Lisa BLASKEY, Auteur ; Chyrise BRADLEY, Auteur ; Jack COLLINS, Auteur ; Casara J. FERRETTI, Auteur ; Homayoon FARZADEGAN, Auteur ; Ellen GIARELLI, Auteur ; Marques HARVEY, Auteur ; Susan HEPBURN, Auteur ; Matthew HERR, Auteur ; Kristina KAPARICH, Auteur ; Rebecca LANDA, Auteur ; Li-Ching LEE, Auteur ; Brooke LEVENSELLER, Auteur ; Stacey MEYERER, Auteur ; Mohammad H. RAHBAR, Auteur ; Andria RATCHFORD, Auteur ; Ann REYNOLDS, Auteur ; Steven A. ROSENBERG, Auteur ; Julie RUSYNIAK, Auteur ; Stuart K. SHAPIRA, Auteur ; Karen S. SMITH, Auteur ; Margaret SOUDERS, Auteur ; Patrick Aaron THOMPSON, Auteur ; Lisa YOUNG, Auteur ; Marshalyn YEARGIN-ALLSOPP, Auteur . - 2012 . - p.2121-2140. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 42-10 (October 2012) . - p.2121-2140 Mots-clés : Autism  Epidemiology  Study methods  Risk factors  Phenotype Index. décimale : PER Périodiques Résumé : The Study to Explore Early Development (SEED), a multisite investigation addressing knowledge gaps in autism phenotype and etiology, aims to: (1) characterize the autism behavioral phenotype and associated developmental, medical, and behavioral conditions and (2) investigate genetic and environmental risks with emphasis on immunologic, hormonal, gastrointestinal, and sociodemographic characteristics. SEED uses a case–control design with population-based ascertainment of children aged 2–5 years with an autism spectrum disorder (ASD) and children in two control groups—one from the general population and one with non-ASD developmental problems. Data from parent-completed questionnaires, interviews, clinical evaluations, biospecimen sampling, and medical record abstraction focus on the prenatal and early postnatal periods. SEED is a valuable resource for testing hypotheses regarding ASD characteristics and causes. En ligne : http://dx.doi.org/10.1007/s10803-012-1461-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=180

Age differences in broader autism phenotype traits from young adulthood to older adulthood / William J. CHOPIK in Autism Research, 14-7 (July 2021)  

Public ISBD [article]  inAutism Research > 14-7 (July 2021) . - p.1456-1471 Titre : Age differences in broader autism phenotype traits from young adulthood to older adulthood Type de document : texte imprimé Auteurs : William J. CHOPIK, Auteur ; Jeewon OH, Auteur ; Amy K. NUTTALL, Auteur ; Katharine N. THAKKAR, Auteur ; Brooke R. INGERSOLL, Auteur Article en page(s) : p.1456-1471 Langues : Anglais (eng) Mots-clés : Adult  Aged  Autism Spectrum Disorder  Autistic Disorder  Cross-Sectional Studies  Female  Humans  Male  Middle Aged  Phenotype  Surveys and Questionnaires  Young Adult  age differences  autism spectrum disorders  broader autism phenotype  lifespan development  personality Index. décimale : PER Périodiques Résumé : Much of past research has been dedicated to refining the operationalization and correlates of the broader autism phenotype (BAP) and less on how the BAP differs by socio-demographic characteristics, like age-particularly after midlife. This gap is important because other nonclinical trait-like characteristics (e.g., personality) have shown considerable age differences, leading to work assessing the malleability of psychological characteristics and improving outcomes for individuals and their significant others. In the current study, we examined cross-sectional age differences in the BAP in a large sample of adults ranging in age from 18 to 85. We recruited a sample of 2966 adults ranging in age from 18 to 85 (M(age) = 36.53, SD = 12.61; 58.9% Female; 1.1% with an ASD diagnosis) recruited from an online survey service. We found that total BAP scores were higher in younger adults and lower among older adults. These differences were particularly true for pragmatic language difficulties, with this component of the BAP showing the most dramatic age differences. Aloofness showed similar negative associations with age, albeit much smaller. Rigidity was not significantly associated with age. The results are consistent with other research showing an abatement of symptoms among individuals with autism spectrum disorders (ASDs) across early life and theories predicting changes in other psychological characteristics (e.g., personality). The results are discussed in the context of the malleability of ASD and BAP traits across life, the clinical implications of these changes, and the origins and consequences for lifespan differences in BAP. LAY SUMMARY: Little is known about how subclinical autistic-like traits among middle-aged and older adults compare to younger adults. We found that these subclinical traits were highest in young adults and lowest in older adults. Knowing how these traits differ by age can provide researchers and clinicians with a sense of how much these traits might change across life, if the traits might be sensitive to interventions, and when in development it might be best to intervene. En ligne : http://dx.doi.org/10.1002/aur.2504 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=449 [article] Age differences in broader autism phenotype traits from young adulthood to older adulthood [texte imprimé] / William J. CHOPIK, Auteur ; Jeewon OH, Auteur ; Amy K. NUTTALL, Auteur ; Katharine N. THAKKAR, Auteur ; Brooke R. INGERSOLL, Auteur . - p.1456-1471. Langues : Anglais (eng) in Autism Research > 14-7 (July 2021) . - p.1456-1471 Mots-clés : Adult  Aged  Autism Spectrum Disorder  Autistic Disorder  Cross-Sectional Studies  Female  Humans  Male  Middle Aged  Phenotype  Surveys and Questionnaires  Young Adult  age differences  autism spectrum disorders  broader autism phenotype  lifespan development  personality Index. décimale : PER Périodiques Résumé : Much of past research has been dedicated to refining the operationalization and correlates of the broader autism phenotype (BAP) and less on how the BAP differs by socio-demographic characteristics, like age-particularly after midlife. This gap is important because other nonclinical trait-like characteristics (e.g., personality) have shown considerable age differences, leading to work assessing the malleability of psychological characteristics and improving outcomes for individuals and their significant others. In the current study, we examined cross-sectional age differences in the BAP in a large sample of adults ranging in age from 18 to 85. We recruited a sample of 2966 adults ranging in age from 18 to 85 (M(age) = 36.53, SD = 12.61; 58.9% Female; 1.1% with an ASD diagnosis) recruited from an online survey service. We found that total BAP scores were higher in younger adults and lower among older adults. These differences were particularly true for pragmatic language difficulties, with this component of the BAP showing the most dramatic age differences. Aloofness showed similar negative associations with age, albeit much smaller. Rigidity was not significantly associated with age. The results are consistent with other research showing an abatement of symptoms among individuals with autism spectrum disorders (ASDs) across early life and theories predicting changes in other psychological characteristics (e.g., personality). The results are discussed in the context of the malleability of ASD and BAP traits across life, the clinical implications of these changes, and the origins and consequences for lifespan differences in BAP. LAY SUMMARY: Little is known about how subclinical autistic-like traits among middle-aged and older adults compare to younger adults. We found that these subclinical traits were highest in young adults and lowest in older adults. Knowing how these traits differ by age can provide researchers and clinicians with a sense of how much these traits might change across life, if the traits might be sensitive to interventions, and when in development it might be best to intervene. En ligne : http://dx.doi.org/10.1002/aur.2504 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=449

An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities / Isabella SLABY in Journal of Neurodevelopmental Disorders, 14 (2022)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 14 (2022) Titre : An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities Type de document : texte imprimé Auteurs : Isabella SLABY, Auteur ; Heather S. HAIN, Auteur ; Debra ABRAMS, Auteur ; Frank D. MENTCH, Auteur ; Joseph T. GLESSNER, Auteur ; Patrick M.A. SLEIMAN, Auteur ; Hakon HAKONARSON, Auteur Langues : Anglais (eng) Mots-clés : Algorithms  Attention Deficit Disorder with  Hyperactivity/complications/diagnosis/epidemiology  Case-Control Studies  Child  Comorbidity  Electronic Health Records  Humans  Phenotype  Prospective Studies  Retrospective Studies Index. décimale : PER Périodiques Résumé : BACKGROUND: In over half of pediatric cases, ADHD presents with comorbidities, and often, it is unclear whether the symptoms causing impairment are due to the comorbidity or the underlying ADHD. Comorbid conditions increase the likelihood for a more severe and persistent course and complicate treatment decisions. Therefore, it is highly important to establish an algorithm that identifies ADHD and comorbidities in order to improve research on ADHD using biorepository and other electronic record data. METHODS: It is feasible to accurately distinguish between ADHD in isolation from ADHD with comorbidities using an electronic algorithm designed to include other psychiatric disorders. We sought to develop an EHR phenotype algorithm to discriminate cases with ADHD in isolation from cases with ADHD with comorbidities more effectively for efficient future searches in large biorepositories. We developed a multi-source algorithm allowing for a more complete view of the patient's EHR, leveraging the biobank of the Center for Applied Genomics (CAG) at Children's Hospital of Philadelphia (CHOP). We mined EHRs from 2009 to 2016 using International Statistical Classification of Diseases and Related Health Problems (ICD) codes, medication history and keywords specific to ADHD, and comorbid psychiatric disorders to facilitate genotype-phenotype correlation efforts. Chart abstractions and behavioral surveys added evidence in support of the psychiatric diagnoses. Most notably, the algorithm did not exclude other psychiatric disorders, as is the case in many previous algorithms. Controls lacked psychiatric and other neurological disorders. Participants enrolled in various CAG studies at CHOP and completed a broad informed consent, including consent for prospective analyses of EHRs. We created and validated an EHR-based algorithm to classify ADHD and comorbid psychiatric status in a pediatric healthcare network to be used in future genetic analyses and discovery-based studies. RESULTS: In this retrospective case-control study that included data from 51,293 subjects, 5840 ADHD cases were discovered of which 46.1% had ADHD alone and 53.9% had ADHD with psychiatric comorbidities. Our primary study outcome was to examine whether the algorithm could identify and distinguish ADHD exclusive cases from ADHD comorbid cases. The results indicate ICD codes coupled with medication searches revealed the most cases. We discovered ADHD-related keywords did not increase yield. However, we found including ADHD-specific medications increased our number of cases by 21%. Positive predictive values (PPVs) were 95% for ADHD cases and 93% for controls. CONCLUSION: We established a new algorithm and demonstrated the feasibility of the electronic algorithm approach to accurately diagnose ADHD and comorbid conditions, verifying the efficiency of our large biorepository for further genetic discovery-based analyses. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02286817 . First posted on 10 November 2014. CLINICALTRIALS: gov, NCT02777931 . First posted on 19 May 2016. CLINICALTRIALS: gov, NCT03006367 . First posted on 30 December 2016. CLINICALTRIALS: gov, NCT02895906 . First posted on 12 September 2016. En ligne : https://dx.doi.org/10.1186/s11689-022-09447-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574 [article] An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities [texte imprimé] / Isabella SLABY, Auteur ; Heather S. HAIN, Auteur ; Debra ABRAMS, Auteur ; Frank D. MENTCH, Auteur ; Joseph T. GLESSNER, Auteur ; Patrick M.A. SLEIMAN, Auteur ; Hakon HAKONARSON, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 14 (2022) Mots-clés : Algorithms  Attention Deficit Disorder with  Hyperactivity/complications/diagnosis/epidemiology  Case-Control Studies  Child  Comorbidity  Electronic Health Records  Humans  Phenotype  Prospective Studies  Retrospective Studies Index. décimale : PER Périodiques Résumé : BACKGROUND: In over half of pediatric cases, ADHD presents with comorbidities, and often, it is unclear whether the symptoms causing impairment are due to the comorbidity or the underlying ADHD. Comorbid conditions increase the likelihood for a more severe and persistent course and complicate treatment decisions. Therefore, it is highly important to establish an algorithm that identifies ADHD and comorbidities in order to improve research on ADHD using biorepository and other electronic record data. METHODS: It is feasible to accurately distinguish between ADHD in isolation from ADHD with comorbidities using an electronic algorithm designed to include other psychiatric disorders. We sought to develop an EHR phenotype algorithm to discriminate cases with ADHD in isolation from cases with ADHD with comorbidities more effectively for efficient future searches in large biorepositories. We developed a multi-source algorithm allowing for a more complete view of the patient's EHR, leveraging the biobank of the Center for Applied Genomics (CAG) at Children's Hospital of Philadelphia (CHOP). We mined EHRs from 2009 to 2016 using International Statistical Classification of Diseases and Related Health Problems (ICD) codes, medication history and keywords specific to ADHD, and comorbid psychiatric disorders to facilitate genotype-phenotype correlation efforts. Chart abstractions and behavioral surveys added evidence in support of the psychiatric diagnoses. Most notably, the algorithm did not exclude other psychiatric disorders, as is the case in many previous algorithms. Controls lacked psychiatric and other neurological disorders. Participants enrolled in various CAG studies at CHOP and completed a broad informed consent, including consent for prospective analyses of EHRs. We created and validated an EHR-based algorithm to classify ADHD and comorbid psychiatric status in a pediatric healthcare network to be used in future genetic analyses and discovery-based studies. RESULTS: In this retrospective case-control study that included data from 51,293 subjects, 5840 ADHD cases were discovered of which 46.1% had ADHD alone and 53.9% had ADHD with psychiatric comorbidities. Our primary study outcome was to examine whether the algorithm could identify and distinguish ADHD exclusive cases from ADHD comorbid cases. The results indicate ICD codes coupled with medication searches revealed the most cases. We discovered ADHD-related keywords did not increase yield. However, we found including ADHD-specific medications increased our number of cases by 21%. Positive predictive values (PPVs) were 95% for ADHD cases and 93% for controls. CONCLUSION: We established a new algorithm and demonstrated the feasibility of the electronic algorithm approach to accurately diagnose ADHD and comorbid conditions, verifying the efficiency of our large biorepository for further genetic discovery-based analyses. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02286817 . First posted on 10 November 2014. CLINICALTRIALS: gov, NCT02777931 . First posted on 19 May 2016. CLINICALTRIALS: gov, NCT03006367 . First posted on 30 December 2016. CLINICALTRIALS: gov, NCT02895906 . First posted on 12 September 2016. En ligne : https://dx.doi.org/10.1186/s11689-022-09447-9 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=574

Associations between genotype, phenotype and behaviours measured by the Rett syndrome behaviour questionnaire in Rett syndrome / Jenny DOWNS in Journal of Neurodevelopmental Disorders, 16 (2024)  

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Autism-like phenotype across the lifespan of Shank3B-mutant mice of both sexes / Jakub SZABÓ in Journal of Neurodevelopmental Disorders, 17 (2025)  

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Brief Report: Macrocephaly Phenotype and Psychiatric Comorbidity in a Clinical Sample of Mexican Children and Adolescents with Autism Spectrum Disorders / Lilia ALBORES-GALLO in Journal of Autism and Developmental Disorders, 47-9 (September 2017)  

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Cerebellar demyelination and neurodegeneration associated with mTORC1 hyperactivity may contribute to the developmental onset of autism-like neurobehavioral phenotype in a rat model / Viera KUTNA in Autism Research, 15-5 (May 2022)  

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Characterisation of the clinical phenotype in Phelan-McDermid syndrome / Mónica BURDEUS-OLAVARRIETA in Journal of Neurodevelopmental Disorders, 13 (2021)  

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