Algorithm to detect pediatric provider attention to high BMI and associated medical risk

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.

Gender medicine: Lessons from COVID-19 and other medical conditions for designing health policy

Gender-specific differences in the prevalence, incidence, comorbidities, prognosis, severity, risk factors, drug-related aspects and outcomes of various medical conditions are well documented. We present a literature review on the extent to which research in this field has developed over the years, and reveal gaps in gender-sensitive awareness between the clinical portrayal and the translation into gender-specific treatment regimens, guidelines and into gender-oriented preventive strategies and health policies. Subsequently, through the lens of gender, we describe these domains in detail for four selected medical conditions: Asthma, obesity and overweight, chronic kidney disease and coronavirus disease 2019. As some of the key gender differences become more apparent during adolescence, we focus on this developmental stage. Finally, we propose a model which is based on three influential issues: (1) Investigating gender-specific medical profiles of related health conditions, rather than a single disease; (2) The dynamics of gender disparities across developmental stages; and (3) An integrative approach which takes into account additional risk factors (ethnicity, socio-demographic variables, minorities, lifestyle habits etc.). Increasing the awareness of gender-specific medicine in daily practice and in tailored guidelines, already among adolescents, may reduce inequities, facilitate the prediction of future trends and properly address the characteristics and needs of certain subpopulations within each gender.

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

Pediatrician Communication About High Blood Pressure in Children With Overweight/Obesity During Well-Child Visits

OBJECTIVE

Despite recognition that hypertension is associated with childhood obesity, data suggest that high blood pressure (BP) is infrequently diagnosed in children. This study sought to examine provider communication with overweight school-age children regarding BP measurements that were high at well-child visits.

METHODS

Cross-sectional mixed-methods analysis of audio-recorded communication from well-child visits with overweight 6-12-year-olds. Data from the subset of children with elevated BPs were used for this study. Three BP measures obtained at the audio-recorded visit were averaged, paired with historical BPs stored in the health record, and compared to contemporary/Fourth-Report thresholds to determine if children had elevated/hypertensive BPs only at the audio-recorded visit or met hypertension-diagnostic criteria (hypertension-level BP ≥3 separate visits). Two reviewers used visit transcripts to categorize communication about BP as "absent," "unclear," or "direct." Provider use of a billing diagnosis for elevated BP/hypertension in visits with direct-BP-communication was explored.

RESULTS

In 36 of 126 (29%) visit-audio-recordings, children had elevated/hypertensive BPs. Thirty-three of the 36 eligible (92%) had intelligible audio-recordings. Of these, 9 (25%) were overweight and 24 (75%) had obesity. Seventeen (52%) had elevated BPs, and 16 (48%) hypertensive BPs. Ten (30%) met criteria for hypertension diagnosis. BP communication was absent in 20 visits (61%), unclear in 8 (24%), and direct in 5 visits (15%). Billing diagnoses for elevated BP/hypertension were entered at 4 of 5 (80%) visits with direct-BP communication.

CONCLUSIONS

Most overweight children with elevated BPs did not receive communication that BP is high at well-child visits. Relevant billing diagnoses may indicate direct-BP communication.

Association of Clinician Behaviors and Weight Change in School-Aged Children

INTRODUCTION

This study uses clinical practice data to determine whether recommended weight management clinician behaviors are associated with weight status improvement in children aged 6-12 years who are overweight or obese.

METHODS

Electronic health record data (2009-2014) from 52 clinics were used. Weight status was examined from 1 visit to the next as dichotomous improvement (versus worsening or no change) and change in percentage overweight (over sex/age-specific BMI95). The primary predictor was a clinician behavior variable denoting attention to high BMI alone or with assessment of medical risk/comorbidities and was defined using combinations of diagnostic codes and electronic health record orders. Covariates included time between visits and medications associated with weight gain or loss. Adjusted multilevel regression models examined the association of the clinician behavior variable with weight status improvement. Analyses were conducted from 2015 to 2018.

RESULTS

Children (n=7,205) had a mean age of 8.9 years; 45.5% were overweight, 54.5% obese, and 81.1% publicly insured. For 62% of overweight children, and 38%, 21%, and 11% of those in obesity classes 1-3, respectively, no attention to high BMI/medical risk assessment at any visit was identified. Children with evidence of clinician attention to high BMI alone and who underwent a medical risk assessment had significantly greater AOR of improvement in percentage of BMI95 and percentage of BMI95 change: BMI alone, AOR=1.2 (p<0.001) and β= -0.3 (p>0.05); BMI/medical risk, AOR=1.2 and β= -0.5 (both p<0.001). Other factors associated with weight status improvement included prescription medications (1 or more prescriptions associated with either weight loss or none associated with weight gain) and fewer months between visits.

CONCLUSIONS

This is the first study to use electronic health record data to demonstrate that widely recommended clinician behaviors are associated with weight status improvement in children aged 6-12 years who are overweight or obese.