Christy B. Turer, Jenny J. Park, Olga T. Gupta, Charina Ramirez, Mujeeb A. Basit, Daniel F. Heitjan, Sarah E. Barlow
{"title":"电子表型在儿科初级保健中区分临床医生对高体重指数、高血压、脂质紊乱、脂肪肝和糖尿病的关注:电子表型与蒙面综合图表比较的诊断准确性","authors":"Christy B. Turer, Jenny J. Park, Olga T. Gupta, Charina Ramirez, Mujeeb A. Basit, Daniel F. Heitjan, Sarah E. Barlow","doi":"10.1111/ijpo.13066","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background/Objectives</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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%).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":217,"journal":{"name":"Pediatric Obesity","volume":"18 10","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"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\",\"authors\":\"Christy B. Turer, Jenny J. Park, Olga T. Gupta, Charina Ramirez, Mujeeb A. Basit, Daniel F. Heitjan, Sarah E. Barlow\",\"doi\":\"10.1111/ijpo.13066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background/Objectives</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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%).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Electronic phenotypes for clinician attention to overweight/obesity and distinct comorbidities are highly specific, with moderate (BMI) to modest (each comorbidity) sensitivity. 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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
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.
期刊介绍:
Pediatric Obesity is a peer-reviewed, monthly journal devoted to research into obesity during childhood and adolescence. The topic is currently at the centre of intense interest in the scientific community, and is of increasing concern to health policy-makers and the public at large.
Pediatric Obesity has established itself as the leading journal for high quality papers in this field, including, but not limited to, the following:
Genetic, molecular, biochemical and physiological aspects of obesity – basic, applied and clinical studies relating to mechanisms of the development of obesity throughout the life course and the consequent effects of obesity on health outcomes
Metabolic consequences of child and adolescent obesity
Epidemiological and population-based studies of child and adolescent overweight and obesity
Measurement and diagnostic issues in assessing child and adolescent adiposity, physical activity and nutrition
Clinical management of children and adolescents with obesity including studies of treatment and prevention
Co-morbidities linked to child and adolescent obesity – mechanisms, assessment, and treatment
Life-cycle factors eg familial, intrauterine and developmental aspects of child and adolescent obesity
Nutrition security and the "double burden" of obesity and malnutrition
Health promotion strategies around the issues of obesity, nutrition and physical activity in children and adolescents
Community and public health measures to prevent overweight and obesity in children and adolescents.