Evidence map›Paper›PMID 37458161›Full record

ReviewPediatric obesity2023

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.

Christy B Turer, Jenny J Park, Olga T Gupta, Charina Ramirez, Mujeeb A Basit, Daniel F Heitjan, Sarah E Barlow

Abstract readReview
In one paragraph

Review in Pediatric obesity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Christy B TurerDepartment of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.ORCID 0000-0001-6333-5535
Jenny J ParkDepartment of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Olga T GuptaDepartment of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Charina RamirezDepartment of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Mujeeb A BasitDepartment of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Daniel F HeitjanDepartment of Population & Data Sciences, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Sarah E BarlowDepartment of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.ORCID 0000-0002-1672-2126

Funding

UT Southwestern NORCP30DK127984 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI Shawn C Burgess · 2022 to 2026
$7.4M
Primary Care, Communication, & Improving Children's HealthK23HL118152 · NHLBI · UT SOUTHWESTERN MEDICAL CENTER · PI TURER, CHRISTY BOLING · 2014 to 2018
$799k
Building Evidence for High-Quality Pediatric Primary Care Weight and Comorbidity ManagementR21DK114764 · NIDDK · UT SOUTHWESTERN MEDICAL CENTER · PI TURER, CHRISTY BOLING · 2018 to 2019
$446k
Development of Single-Screen Clinical Decision Support to Increase Guideline-Based Weight Management and Comorbidity CareR03HL144811 · NHLBI · UT SOUTHWESTERN MEDICAL CENTER · PI TURER, CHRISTY BOLING · 2018 to 2019
$162k
NHLBI NIH HHS K23 HL118152NHLBI NIH HHS K23HL118152NHLBI NIH HHS R03 HL144811NHLBI NIH HHS R03HL144811NIDDK NIH HHS P30 DK127984NIDDK NIH HHS R21 DK114764NIDDK NIH HHS R21DK114764
6 · The paper itself

Abstract

BACKGROUND/

objectivesElectronic 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.

methodsWe 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.

resultsIn 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%).

conclusionsElectronic 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.

Indexed as

Diabetes MellitusFatty LiverHypertensionPrediabetic StateBody Mass IndexElectronic Health RecordsHumansLipidsObesityOverweightPhenotypePrimary Health CareLipidsbody mass indexelectronic health recordsevidence-based medicinephenotypesensitivity and specificity

Identifiers

PMID37458161
PMCPMC10825897

What Socratic holds

Textmetadata
LicenceTDM
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.