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Appl Clin Inform 2024; 15(04): 700-708
DOI: 10.1055/s-0044-1787975
Research Article

External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care

Anya G. Barron
1   Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States
,
Ada M. Fenick
2   Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, United States
,
Kaitlin R. Maciejewski
3   Yale Center for Analytical Sciences, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States
,
Christy B. Turer
4   Departments of Pediatrics and Medicine, University of Texas Southwestern Medical Center and Children's Health, Dallas, Texas, United States
,
Mona Sharifi
2   Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, United States
› Author Affiliations
Funding This study was supported in part by Agency for Healthcare Research and Quality under Award Number grant K08HS024332, by the National Institute on Minority Health and Health Disparities of the National Institutes of Health (NIH) under Award Number R01MD014853, and by Clinical and Translational Science Awards Grant Number UL1TR001863 from the National Center for Advancing Translational Science, a component of the NIH.
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Abstract

Objectives The lack of feasible and meaningful measures of clinicians' behavior hinders efforts to assess and improve obesity management in pediatric primary care. In this study, we examined the external validity of a novel algorithm, previously validated in a single geographic region, using structured electronic health record (EHR) data to identify phenotypes of clinicians' attention to elevated body mass index (BMI) and weight-related comorbidities.

Methods We extracted structured EHR data for 300 randomly selected 6- to 12-year-old children with elevated BMI seen for well-child visits from June 2018 to May 2019 at pediatric primary care practices affiliated with Yale. Using diagnosis codes, laboratory orders, referrals, and medications adapted from the original algorithm, we categorized encounters as having evidence of attention to BMI only, weight-related comorbidities only, or both BMI and comorbidities. We evaluated the algorithm's sensitivity and specificity for detecting any attention to BMI and/or comorbidities using chart review as the reference standard.

Results The adapted algorithm yielded a sensitivity of 79.2% and specificity of 94.0% for identifying any attention to high BMI/comorbidities in clinical documentation. Of 86 encounters labeled as “no attention” by the algorithm, 83% had evidence of attention in free-text components of the progress note. The likelihood of classification as “any attention” by both chart review and the algorithm varied by BMI category and by clinician type (p < 0.001).

Conclusion The electronic phenotyping algorithm had high specificity for detecting attention to high BMI and/or comorbidities in structured EHR inputs. The algorithm's performance may be improved by incorporating unstructured data from clinical notes.

Keywords

data validation and verification - primary care - pediatrics - specific conditions - quality - EHR systems

Data Availability Statement

To protect the privacy of individuals included in the study, the data underlying this article cannot be shared publicly. The data can be shared on reasonable request to the corresponding author.


Protection of Human and Animal Subjects

This study was approved by the Yale Human Research Protection Program.


Supplementary Material



Publication History

Received: 15 November 2023

Accepted: 13 February 2024

Article published online:
28 August 2024

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