Automating Clinical Decision Support to Improve Pediatric Hypertension Identification: Evaluation of Diagnostic Validity and Computational Time of Two Algorithms
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Hypertension (HTN) in children is underdiagnosed. This is concerning. Rates of HTN are increasing with the rise in childhood obesity. In children, diagnosis of HTN requires three discrete elevations in historical blood-pressure (BP) percentiles (%). Calculating BP% requires valid heights and ages that change from visit to visit, and BP%s are neither calculated nor stored in electronic health records (EHRs). Current methods used by providers to circumvent these challenges may contribute to under diagnosis of pediatric HTN. EHR-enabled algorithms may improve rates of HTN diagnosis by automating discovery of historical BP% elevations. The study aim was to determine whether an EHR-enabled algorithm to diagnose HTN is more diagnostically valid than methods currently used by providers. Data were from a retrospective cohort of 52,828 3-18 year-old children followed up to 6 years in primary care. The 1st algorithm, termed current-observation-carried backwards (COCB), identified the BP% threshold for HTN using current-visit height/age, and applied that threshold to historical visits to determine HTN (defined as 3 elevations in BP% ≥95). COCB imitated mental processes currently used by providers, because retrospectively calculating visit-specific thresholds during patient visits is not feasible. The 2nd algorithm, termed “smart data elements” (SDEs), exported historical-visit ages, heights, and BPs from the EHR into a database to calculate historical BP% thresholds for HTN—thus providing visit-specific BP%s. The 2nd algorithm is the ideal way to determine HTN. The study hypothesis was that the second/SDE algorithm would be more diagnostically valid than the first/COCB. Diagnostic validity for determining HTN was determined by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Descriptive statistics summarized diagnostic validity of the algorithms. Variation in diagnostic validity by height and body-mass-index percentile (BMI%) were explored. The sensitivity of COCB (vs. SDE) for detecting HTN was 82.2%. COCB’s specificity surpassed 99.8%. PPV and NPV were >95%. Diagnostic validity differed by height and BMI%: as height and BMI% increased, sensitivity increased and PPV decreased. Study data indicate that the current method used by many pediatricians to diagnose HTN (COCB) misses up to 1 in 5 children with HTN. Automating identification of three discrete BP% elevations using the SDE algorithm may improve rates of pediatric HTN diagnosis.
The 55th Annual Medical Student Research Forum at UT Southwestern Medical Center (Monday, January 17, 2017, 2-5 p.m., D1.600)
SubjectClinical Research and Case Reports
Decision Support Systems, Clinical
Doney, A., Bowen, M., Adamson, B., Sanders, J., Vertilus, S., Menzies, C., . . . Turer, C. B. (2017, January 17). Automating clinical decision support to improve pediatric hypertension identification: evaluation of diagnostic validity and computational time of two algorithms. Poster session presented at the 55th Annual Medical Student Research Forum, Dallas, TX. Retrieved from https://hdl.handle.net/2152.5/4018