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

The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative / Monica E. CALKINS in Journal of Child Psychology and Psychiatry, 56-12 (December 2015)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 56-12 (December 2015) . - p.1356-1369 Titre : The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative Type de document : texte imprimé Auteurs : Monica E. CALKINS, Auteur ; Kathleen R. MERIKANGAS, Auteur ; Tyler M. MOORE, Auteur ; Marcy BURSTEIN, Auteur ; Meckenzie A. BEHR, Auteur ; Theodore D. SATTERTHWAITE, Auteur ; Kosha RUPAREL, Auteur ; Daniel H. WOLF, Auteur ; David R. ROALF, Auteur ; Frank D. MENTCH, Auteur ; Haijun QIU, Auteur ; Rosetta CHIAVACCI, Auteur ; John J. CONNOLLY, Auteur ; Patrick M.A. SLEIMAN, Auteur ; Ruben C. GUR, Auteur ; Hakon HAKONARSON, Auteur ; Raquel E. GUR, Auteur Article en page(s) : p.1356-1369 Langues : Anglais (eng) Mots-clés : Community cohort  children  adolescents  young adults  psychopathology  mood  anxiety  behavior  psychosis  comorbidity  structure  genomics  neuroimaging  neurocognition  public domain Index. décimale : PER Périodiques Résumé : Background An integrative multidisciplinary approach is required to elucidate the multiple factors that shape neurodevelopmental trajectories of mental disorders. The Philadelphia Neurodevelopmental Cohort (PNC), funded by the National Institute of Mental Health Grand Opportunity (GO) mechanism of the American Recovery and Reinvestment Act, was designed to characterize clinical and neurobehavioral phenotypes of genotyped youths. Data generated, which are recently available through the NIMH Database of Genotypes and Phenotypes (dbGaP), have garnered considerable interest. We provide an overview of PNC recruitment and clinical assessment methods to allow informed use and interpretation of the PNC resource by the scientific community. We also evaluate the structure of the assessment tools and their criterion validity. Methods Participants were recruited from a large pool of youths (n = 13,958) previously identified and genotyped at The Children's Hospital of Philadelphia. A comprehensive computerized tool for structured evaluation of psychopathology domains (GOASSESS) was constructed. We administered GOASSESS to all participants and used factor analysis to evaluate its structure. Results A total of 9,498 youths (aged 8–21; mean age = 14.2; European American = 55.8%; African American = 32.9%; Other = 11.4%) were enrolled. Factor analysis revealed a strong general psychopathology factor, and specific ‘anxious-misery’, ‘fear’, and ‘behavior’ factors. The ‘behavior’ factor had a small negative correlation (−0.21) with overall accuracy of neurocognitive performance, particularly in tests of executive and complex reasoning. Being female had a high association with the ‘anxious-misery’ and low association with the ‘behavior’ factors. The psychosis spectrum was also best characterized by a general factor and three specific factors: ideas about ‘special abilities/persecution,’ ‘unusual thoughts/perceptions’, and ‘negative/disorganized’ symptoms. Conclusions The PNC assessment mechanism yielded psychopathology data with strong factorial validity in a large diverse community cohort of genotyped youths. Factor scores should be useful for dimensional integration with other modalities (neuroimaging, genomics). Thus, PNC public domain resources can advance understanding of complex inter-relationships among genes, cognition, brain, and behavior involved in neurodevelopment of common mental disorders. En ligne : http://dx.doi.org/10.1111/jcpp.12416 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=273 [article] The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative [texte imprimé] / Monica E. CALKINS, Auteur ; Kathleen R. MERIKANGAS, Auteur ; Tyler M. MOORE, Auteur ; Marcy BURSTEIN, Auteur ; Meckenzie A. BEHR, Auteur ; Theodore D. SATTERTHWAITE, Auteur ; Kosha RUPAREL, Auteur ; Daniel H. WOLF, Auteur ; David R. ROALF, Auteur ; Frank D. MENTCH, Auteur ; Haijun QIU, Auteur ; Rosetta CHIAVACCI, Auteur ; John J. CONNOLLY, Auteur ; Patrick M.A. SLEIMAN, Auteur ; Ruben C. GUR, Auteur ; Hakon HAKONARSON, Auteur ; Raquel E. GUR, Auteur . - p.1356-1369. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 56-12 (December 2015) . - p.1356-1369 Mots-clés : Community cohort  children  adolescents  young adults  psychopathology  mood  anxiety  behavior  psychosis  comorbidity  structure  genomics  neuroimaging  neurocognition  public domain Index. décimale : PER Périodiques Résumé : Background An integrative multidisciplinary approach is required to elucidate the multiple factors that shape neurodevelopmental trajectories of mental disorders. The Philadelphia Neurodevelopmental Cohort (PNC), funded by the National Institute of Mental Health Grand Opportunity (GO) mechanism of the American Recovery and Reinvestment Act, was designed to characterize clinical and neurobehavioral phenotypes of genotyped youths. Data generated, which are recently available through the NIMH Database of Genotypes and Phenotypes (dbGaP), have garnered considerable interest. We provide an overview of PNC recruitment and clinical assessment methods to allow informed use and interpretation of the PNC resource by the scientific community. We also evaluate the structure of the assessment tools and their criterion validity. Methods Participants were recruited from a large pool of youths (n = 13,958) previously identified and genotyped at The Children's Hospital of Philadelphia. A comprehensive computerized tool for structured evaluation of psychopathology domains (GOASSESS) was constructed. We administered GOASSESS to all participants and used factor analysis to evaluate its structure. Results A total of 9,498 youths (aged 8–21; mean age = 14.2; European American = 55.8%; African American = 32.9%; Other = 11.4%) were enrolled. Factor analysis revealed a strong general psychopathology factor, and specific ‘anxious-misery’, ‘fear’, and ‘behavior’ factors. The ‘behavior’ factor had a small negative correlation (−0.21) with overall accuracy of neurocognitive performance, particularly in tests of executive and complex reasoning. Being female had a high association with the ‘anxious-misery’ and low association with the ‘behavior’ factors. The psychosis spectrum was also best characterized by a general factor and three specific factors: ideas about ‘special abilities/persecution,’ ‘unusual thoughts/perceptions’, and ‘negative/disorganized’ symptoms. Conclusions The PNC assessment mechanism yielded psychopathology data with strong factorial validity in a large diverse community cohort of genotyped youths. Factor scores should be useful for dimensional integration with other modalities (neuroimaging, genomics). Thus, PNC public domain resources can advance understanding of complex inter-relationships among genes, cognition, brain, and behavior involved in neurodevelopment of common mental disorders. En ligne : http://dx.doi.org/10.1111/jcpp.12416 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=273

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