Pediatric hypertension (HTN), high blood pressure in children, is a condition that has rapidly increased in prevalence over the last few decades and now  affects up to 4.5% of children in the US and worldwide. Pediatric HTN diagnosis guidelines are hard to follow, as interpreting blood pressure (BP) readings requires the child's sex, height and age to classify them as normal, elevated, or hypertensive (stage 1/2), instead  of flat cut-offs. Because of this, pediatric HTN often goes unrecognized, and thus undiagnosed and untreated. This is a major issue as almost 10% of adult HTN could be prevented if childhood HTN was recognized and treated. A potential solution is a risk score model which can identify patients at risk of developing HTN using  multiple pieces of clinical data to assess the likelihood of having a given condition. As preparatory work, we sought to describe certain clinical and demographic factors that differentiate patients who are and are not diagnosed with HTN. Our objective was to determine the clinical and demographic characteristics of pediatric patients with elevated BP readings to understand the factors most important to receiving a diagnosis of HTN. For this study, we used an 8 step attrition table to create our cohort from all the patents in the PEDSnet database. We then compiled clinical and demographic data for each patient. We split the cohort into 2 groups, those with hypertension and those without, and used statistical analysis to compare the data for the two groups. The overall cohort with at least one elevated BP was 605,737, and 5.5% had a diagnosis of HTN. Some notable differences between the 2 sub groups were that The race distribution was significantly different, with black patients under-represented in the HTN group. Diabetes (Type 1 and 2) was significantly more likely in the HTN Group. Age at first elevated BP was also significantly higher in the HTN diagnosis group. Some next steps for this project would be to explore additional variables such as steroid use, number of hypertensive BPs before HTN diagnosis, differences in use of other classes of medication, and to use of linear regression or machine learning approaches to help develop a potential risk score for development of HTN.