Pharmacoepidemiology (Mar 2024)

Pioneering Arterial Hypertension Phenotyping on Nationally Aggregated Electronic Health Records

  • Jing Wei Neo,
  • Qihuang Xie,
  • Pei San Ang,
  • Hui Xing Tan,
  • Belinda Foo,
  • Yen Ling Koon,
  • Amelia Ng,
  • Siew Har Tan,
  • Desmond Teo,
  • Mun Yee Tham,
  • Aaron Yap,
  • Nicholas Ng,
  • Celine Wei Ping Loke,
  • Li Fung Peck,
  • Huilin Huang,
  • Sreemanee Raaj Dorajoo

DOI
https://doi.org/10.3390/pharma3010010
Journal volume & issue
Vol. 3, no. 1
pp. 169 – 182

Abstract

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Background: Hypertension is frequently studied in epidemiological studies that have been conducted using retrospective observational data, either as an outcome or a variable. However, there are few validation studies investigating the accuracy of hypertension phenotyping algorithms in aggregated electronic health record (EHR) data. Methods: Utilizing a centralized repository of inpatient EHR data from Singapore for the period of 2019–2020, a new algorithm that incorporates both diagnostic codes and medication details (Diag+Med) was devised. This algorithm was intended to supplement and improve the diagnostic code-only model (Diag-Only) for the classification of hypertension. We computed various metrics (sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) to assess the algorithm’s effectiveness in identifying hypertension on 2813 chart-reviewed records. This pool was composed of two patient cohorts: a random sampling of all inpatient admissions (Random Cohort) and a targeted group with atrial fibrillation diagnoses (AF Cohort). Results: The Diag+Med algorithm was more sensitive at detecting hypertension patients in both cohorts compared to the Diag-Only algorithm (83.8 and 87.6% vs. 68.2 and 66.5% in the Random and AF Cohorts, respectively). These improvements in sensitivity came at minimal costs in terms of PPV reductions (88.2 and 90.3% vs. 91.4 and 94.2%, respectively). Conclusion: The combined use of diagnosis codes and specific antihypertension medication exposure patterns facilitates a more accurate capture of patients with hypertension in a database of aggregated EHRs from diverse healthcare institutions in Singapore. The results presented here allow for the bias correction of risk estimates derived from observational studies involving hypertension.

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