Department of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Department of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.
Pediatr Obes. 2023 Oct;18(10):e13066. doi: 10.1111/ijpo.13066. Epub 2023 Jul 17.
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.
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.
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%).
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.
背景/目的：电子表型是一种使用电子健康记录（EHR）数据来自动识别具有感兴趣特征的患者/人群的方法。本研究旨在确定使用超重/肥胖儿童的 EHR 数据来电子表型临床医生对高体重指数（BMI）和四种不同合并症的关注的有效性。
我们构建了五个电子表型，通过电子/健康记录中明确关注高 BMI、高血压、血脂异常、脂肪肝和糖尿病前期/糖尿病的证据，对 2-18 岁超重/肥胖儿童进行分类（n=17397）。我们回顾、选择和交叉检查随机图表，以定义临床医生在 EHR 中选择的项目来构建问题列表，并为超重/肥胖和每种合并症开出处方、实验室检查和转诊，以电子方式对关注程度进行分类。通过比较审查员对电子分类不知情的综合图表审查，确定每个临床医生关注表型的操作特征，以确定临床医生对高 BMI 和每种合并症的关注证据。
在对 817 次就诊记录的随机样本进行审查/编码中，每个电子表型的特异性为 99%-100%（阳性预测值范围从糖尿病前期/糖尿病的 96.8%到血脂异常和高血压的 100%）。关注分类的敏感性范围从高血压的 69%（NPV，98.9%）到高 BMI 关注的 84.7%（NPV，92.3%）。
针对临床医生对超重/肥胖和不同合并症的关注的电子表型具有高度特异性，敏感性中等（BMI）至适度（每种合并症）。高特异性支持使用表型来识别之前有高 BMI/合并症关注的儿童。
