Screening children for hypertension: the case against

Improving Pediatric Hypertension Screening in an Academic Primary Care Setting

Introduction

Adherence to the American Academy of Pediatrics clinical practice guidelines for screening and managing high blood pressure (BP) is low. This team sought to improve recognition and documentation of relevant diagnoses in patients aged 13-20 years who presented to general pediatric clinics.

Methods

The primary outcome measure was the proportion of office visits for patients ages 13-20 with a BP ≥ 120/80 with a visit or problem list diagnosis of hypertension or elevated BP. Secondary measures included (1) the proportion of patients who had their BP measured in the right arm, (2) the proportion of patients who had a mid-arm circumference measurement recorded, and (3) the proportion of patients who had a second BP reading measured at the visit. Interventions addressed key drivers for evidence-based high BP screening: standard BP measurement, electronic health record clinical decision support, and clinical pathway adoption. Data were collected over a twenty-seven-month period and plotted using the Laney p' chart.

Results

Provider documentation of elevated BP or hypertension improved from a baseline mean of 24% in April 2020 through January 2022 to 41% in February 2021 through June 2022. All secondary outcome measures also demonstrated significant improvement.

Conclusions

This project demonstrates the feasibility of improving adherence to best practices of BP measurement in primary care clinics through education, acquisition of resources, and implementation of electronic health record flags for abnormal values.

Development of a prediction model to target screening for high blood pressure in children

Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure ≥ 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.