Suppr超能文献

电子表型可区分儿科初级保健中临床医生对高体重指数、高血压、血脂紊乱、脂肪肝和糖尿病的关注:与盲法综合图表回顾相比,电子表型的诊断准确性。

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.

机构信息

Department of Pediatrics, University of Texas Southwestern (UTSW), Dallas, Texas, USA.

Department of Medicine, University of Texas Southwestern (UTSW), Dallas, Texas, USA.

出版信息

Pediatr Obes. 2023 Oct;18(10):e13066. doi: 10.1111/ijpo.13066. Epub 2023 Jul 17.

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.

摘要

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。

方法

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。

结果

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%)。

结论

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。

相似文献

8
Association of Clinician Behaviors and Weight Change in School-Aged Children.临床医生行为与学龄儿童体重变化的关联。
Am J Prev Med. 2019 Sep;57(3):384-393. doi: 10.1016/j.amepre.2019.04.029. Epub 2019 Aug 1.

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验