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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Related Experiment Video

Updated: Jul 15, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Performance of the ACC/AHA Pooled Cohort Cardiovascular Risk Equations in Clinical Practice.

Jose R Medina-Inojosa1, Virend K Somers2, Mariana Garcia3

  • 1Department of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, Minnesota, USA; Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.

Journal of the American College of Cardiology
|October 4, 2023
PubMed
Summary
This summary is machine-generated.

The American College of Cardiology/American Heart Association pooled cohort equation (PCE) accurately predicts atherosclerotic cardiovascular disease (ASCVD) risk in real-world settings. Its performance remains robust even with values outside the standard range or with statin use.

Keywords:
atherosclerotic cardiovascular diseasecardiovascular outcomescardiovascular risk assessmentpooled cohort equationspreventive cardiology

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Area of Science:

  • Cardiology
  • Public Health
  • Epidemiology

Background:

  • The performance of the American College of Cardiology/American Heart Association pooled cohort equation (PCE) for atherosclerotic cardiovascular disease (ASCVD) risk prediction in real-world clinical practice is not extensively evaluated.
  • Real-world validation is crucial for understanding the applicability of cardiovascular risk prediction models.

Purpose of the Study:

  • To evaluate the predictive performance of the PCE for ASCVD risk in a community-based cohort.
  • To determine if including individuals with out-of-range values (age, blood pressure, cholesterol) or statin initiation affects PCE's predictive capabilities.

Main Methods:

  • Validation of PCE using a community-based cohort (1997-2016) from Olmsted County, Minnesota.
  • Ascertainment of patient information via the Rochester Epidemiology Project record linkage system.
  • Duplicate validation of ASCVD events (myocardial infarction, ischemic stroke) and comparison of calculated vs. observed risks and c-statistics.

Main Results:

  • The study included 30,042 adults with a median follow-up of 16.5 years (truncated to 10 years).
  • The PCE demonstrated good overall performance (c-statistic 0.78), with variations across subgroups.
  • Out-of-range values and statin initiation did not significantly impact the model's predictive performance.

Conclusions:

  • The PCE performs well in a real-world clinical practice setting.
  • Current ASCVD prevention strategies based on PCE are applicable even for patients with values outside the standard range or on statin therapy.
  • Findings support the utility of PCE in diverse patient populations for ASCVD risk assessment.