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Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: An exploratory

Da-Ya Yang1,2, Zhi-Qiang Nie3, Li-Zhen Liao4

  • 1Department of Cardiology, First Affiliated Hospital of Sun Yat-Sen University, China.

European Journal of Preventive Cardiology
|June 20, 2019
PubMed

Insights

Hypertension patient subgroups identified through data analysis show varied cardiovascular risks and treatment responses. Intensive treatment benefits some groups, while others face increased adverse effects, guiding personalized care.

Area of Science:

  • Cardiology
  • Hypertension Research
  • Data Science in Medicine

Background:

  • Hypertensive patients exhibit significant heterogeneity in cardiovascular prognosis and treatment response.
  • A refined classification system is needed to identify high-risk individuals and guide personalized antihypertensive therapy.

Purpose of the Study:

  • To identify distinct patient subgroups within the Systolic Blood Pressure Intervention Trial (SPRINT) cohort using unsupervised, data-driven cluster analysis.
  • To investigate differences in cardiovascular outcomes and responses to intensive antihypertensive treatment among these subgroups.

Main Methods:

  • Unsupervised cluster analysis of baseline variables from SPRINT participants.
  • Cox regression analysis to determine hazard ratios (HRs) and 95% confidence intervals (CIs) for cardiovascular outcomes.
  • Comparison of intensive antihypertensive treatment effects across identified clusters.

Main Results:

  • Four distinct patient clusters were identified: index hypertensives, chronic kidney disease hypertensives, obese hypertensives, and extra-risk hypertensives.
  • Cluster 4 (extra risky hypertensives) exhibited the highest risk for primary outcomes.
  • Intensive treatment benefited clusters 4 and 1, but increased adverse effects in cluster 2 (chronic kidney disease hypertensives).

Conclusions:

  • Data-driven phenomapping stratifies SPRINT participants into four subgroups with differential cardiovascular prognoses and treatment responses.
  • These findings suggest a hypothesis for personalized antihypertensive treatment strategies that requires prospective validation.
Abstract

Keywords:
Hypertensionblood pressurecardiovascular researchdata-driven cluster analysis

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