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Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.

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Title: Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.
Authors: Nguyen, T. Q., Kerley, C. I., Key, A. P., Maxwell‐Horn, A. C., Wells, Q. S., Neul, J. L., Cutting, L. E., Landman, B. A.
Source: Journal of Intellectual Disability Research. May2024, Vol. 68 Issue 5, p491-511. 21p.
Subjects: Congenital heart disease, Risk assessment, Down syndrome, Research funding, Academic medical centers, Prediction models, Heart failure, Descriptive statistics, Intellectual disabilities, Longitudinal method, Electronic health records, Medical records, Acquisition of data, Phenotypes, Comorbidity, Disease risk factors, Disease complications
Abstract: Background: Individuals with Down syndrome (DS) have a heightened risk for various co‐occurring health conditions, including congenital heart disease (CHD). In this two‐part study, electronic medical records (EMRs) were leveraged to examine co‐occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). Methods: De‐identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one‐by‐two sex and age matched with samples of case–controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome‐disease association study (PheDAS) strategy was employed to reveal co‐occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case–controls. Data were included in predictive models and assessed which model‐based health conditions, when more prevalent, would increase the likelihood of surgical intervention. Results: In Study 1, relative to case–controls and those with other IDDs, co‐occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co‐occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non‐rheumatic aortic valve disorders and heart failure (circulatory); acid–base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. Conclusions: Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision‐care development. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intellectual Disability Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.
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  Data: <searchLink fieldCode="AR" term="%22Nguyen%2C+T%2E+Q%2E%22">Nguyen, T. Q.</searchLink><br /><searchLink fieldCode="AR" term="%22Kerley%2C+C%2E+I%2E%22">Kerley, C. I.</searchLink><br /><searchLink fieldCode="AR" term="%22Key%2C+A%2E+P%2E%22">Key, A. P.</searchLink><br /><searchLink fieldCode="AR" term="%22Maxwell‐Horn%2C+A%2E+C%2E%22">Maxwell‐Horn, A. C.</searchLink><br /><searchLink fieldCode="AR" term="%22Wells%2C+Q%2E+S%2E%22">Wells, Q. S.</searchLink><br /><searchLink fieldCode="AR" term="%22Neul%2C+J%2E+L%2E%22">Neul, J. L.</searchLink><br /><searchLink fieldCode="AR" term="%22Cutting%2C+L%2E+E%2E%22">Cutting, L. E.</searchLink><br /><searchLink fieldCode="AR" term="%22Landman%2C+B%2E+A%2E%22">Landman, B. A.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intellectual+Disability+Research%22">Journal of Intellectual Disability Research</searchLink>. May2024, Vol. 68 Issue 5, p491-511. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Congenital+heart+disease%22">Congenital heart disease</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Down+syndrome%22">Down syndrome</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+medical+centers%22">Academic medical centers</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Heart+failure%22">Heart failure</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+disabilities%22">Intellectual disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+health+records%22">Electronic health records</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+records%22">Medical records</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Phenotypes%22">Phenotypes</searchLink><br /><searchLink fieldCode="DE" term="%22Comorbidity%22">Comorbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+complications%22">Disease complications</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Individuals with Down syndrome (DS) have a heightened risk for various co‐occurring health conditions, including congenital heart disease (CHD). In this two‐part study, electronic medical records (EMRs) were leveraged to examine co‐occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). Methods: De‐identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one‐by‐two sex and age matched with samples of case–controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome‐disease association study (PheDAS) strategy was employed to reveal co‐occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case–controls. Data were included in predictive models and assessed which model‐based health conditions, when more prevalent, would increase the likelihood of surgical intervention. Results: In Study 1, relative to case–controls and those with other IDDs, co‐occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co‐occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non‐rheumatic aortic valve disorders and heart failure (circulatory); acid–base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. Conclusions: Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision‐care development. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Intellectual Disability Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jir.13124
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 491
    Subjects:
      – SubjectFull: Congenital heart disease
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Down syndrome
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Academic medical centers
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Heart failure
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Intellectual disabilities
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Electronic health records
        Type: general
      – SubjectFull: Medical records
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Phenotypes
        Type: general
      – SubjectFull: Comorbidity
        Type: general
      – SubjectFull: Disease risk factors
        Type: general
      – SubjectFull: Disease complications
        Type: general
    Titles:
      – TitleFull: Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.
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              M: 05
              Text: May2024
              Type: published
              Y: 2024
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