Using an EMR to assess pediatric blood pressure: Challenges and opportunities in a nephrology cohort
Bibliographic record
Abstract
Abstract Background Hypertension is a prevalent condition in the pediatric population. Diagnosis and management can be challenging due to difficulties with accurate measurement techniques and complex diagnostic criteria. The widespread adoption of electronic medical records (EMRs) has revealed their potential for improving patient care and research. This study aims to assess the clinical utility of using EMR data to enhance the identification and evaluation of children with hypertension. Objectives The primary objectives of this research project were to utilize the EMR to extract anthropometric, demographic, and blood pressure-related data from patients seen in the nephrology clinic as well as describe and evaluate trends in hypertension assessment and treatment while also identifying areas for improvement. Design We performed a single center, retrospective cohort study using EMR data. Setting Children who had their initial visit at the nephrology clinic between January 1st, 2018, and January 1st, 2022, were included in the cohort. Methods Outpatients were identified using ICD-10 codes related to nephrology diseases. The EMR was reviewed to extract anthropometric, biochemical, and blood pressure data. A blood pressure (BP) index was calculated using systolic and diastolic BP values and the 2017 American Academy of Pediatrics (AAP) hypertension guidelines. The primary analysis categorized BP phenotypes. A secondary analysis using EMR and chart review, assessed whether elevated BP was appropriately managed, including scheduling follow-up visits, diagnosing white-coat hypertension, or initiating pharmacological or non-pharmacological interventions. Results A total of 1,469 children aged 1–18 (median age 9.8 years) were newly referred to the nephrology clinic with complete data for BP index calculation. Many children were initially diagnosed as hypertensive, but across multiple visits were normotensive. Despite being hypertensive across multiple visits, we observed that many children had missing data following EMR extraction (∼20%). Furthermore, despite meeting criteria at visit one for hypertension, many children did not have follow up visits (∼20-30%). We identified that those children presenting with isolated elevated diastolic blood pressure elevations were more likely to have fewer BP measurements and were less likely to have BP-related follow up, likely reflecting the perceived benign nature of this phenomenon. Limitations This study’s retrospective, non-randomized design limits generalizability. Conclusions This study underscores the challenges in studying pediatric hypertension using an EMR, particularly highlighting missing values and decreased measurements as problematic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".