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Electronic phenotypes to distinguish clinician attention to high body mass index, hypertension, lipid disorders, fatty liver and diabetes in pediatric primary care: Diagnostic accuracy of electronic phenotypes compared to masked comprehensive chart review.
Turer, Christy B; Park, Jenny J; Gupta, Olga T; Ramirez, Charina; Basit, Mujeeb A; Heitjan, Daniel F; Barlow, Sarah E.
Affiliation
  • Turer CB; Department of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
  • Park JJ; Department of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
  • Gupta OT; Department of Population & Data Sciences, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
  • Ramirez C; Children's Health System of Dallas, Dallas, Texas, USA.
  • Basit MA; Department of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
  • Heitjan DF; Department of Population & Data Sciences, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
  • Barlow SE; Department of Statistical Science, Southern Methodist University (SMU), Dallas, Texas, USA.
Pediatr Obes ; 18(10): e13066, 2023 10.
Article in En | MEDLINE | ID: mdl-37458161
ABSTRACT
BACKGROUND/

OBJECTIVES:

Electronic phenotyping is a method of using electronic-health-record (EHR) data to automate identifying a patient/population with a characteristic of interest. This study determines validity of using EHR data of children with overweight/obesity to electronically phenotype evidence of clinician 'attention' to high body mass index (BMI) and each of four distinct comorbidities.

METHODS:

We built five electronic phenotypes classifying 2-18-year-old children with overweight/obesity (n = 17,397) by electronic/health-record evidence of distinct attention to high body mass index, hypertension, lipid disorders, fatty liver, and prediabetes/diabetes. We reviewed, selected and cross-checked random charts to define items clinicians select in EHRs to build problem lists, and to order medications, laboratory tests and referrals to electronically classify attention to overweight/obesity and each comorbidity. Operating characteristics of each clinician-attention phenotype were determined by comparing comprehensive chart review by reviewers masked to electronic classification who adjudicated evidence of clinician attention to high BMI and each comorbidity.

RESULTS:

In a random sample of 817 visit-records reviewed/coded, specificity of each electronic phenotype is 99%-100% (with PPVs ranging from 96.8% for prediabetes/diabetes to 100% for dyslipidemia and hypertension). Sensitivities of the attention classifications range from 69% for hypertension (NPV, 98.9%) to 84.7% for high-BMI attention (NPV, 92.3%).

CONCLUSIONS:

Electronic phenotypes for clinician attention to overweight/obesity and distinct comorbidities are highly specific, with moderate (BMI) to modest (each comorbidity) sensitivity. The high specificity supports using phenotypes to identify children with prior high-BMI/comorbidity attention.
Subject(s)
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prediabetic State / Diabetes Mellitus / Fatty Liver / Hypertension Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Pediatr Obes Year: 2023 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Prediabetic State / Diabetes Mellitus / Fatty Liver / Hypertension Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Pediatr Obes Year: 2023 Document type: Article Affiliation country: United States