Health status and cognitive function for risk stratification in chronic coronary and peripheral artery disease

Kim G Smolderen1,2, Carlos Mena-Hurtado1, John W Eikelboom3

  • 1Department of Internal Medicine, Yale University, School of Medicine, Vascular Medicine Outcomes Program, 789 Howard Avenue, New Haven, CT 06519, USA.

Insights

Assessing health status and cognitive function can improve cardiovascular risk prediction in patients with coronary artery disease (CAD) and peripheral artery disease (PAD). These factors help identify individuals at lower risk for adverse cardiovascular or limb events.

Area of Science:

  • Cardiovascular Medicine
  • Clinical Risk Assessment

Background:

  • Traditional risk prediction for coronary artery disease (CAD) and peripheral artery disease (PAD) may not fully capture patient outcomes.
  • The added value of health status and cognitive function assessments to existing biomedical frameworks is unclear.

Purpose of the Study:

  • To investigate if health status and cognitive function assessments can enhance traditional risk prediction in CAD and PAD patients.
  • To examine the association between health status, cognitive function, and subsequent adverse cardiovascular and limb events.

Main Methods:

  • Utilized data from the COMPASS trial, including 23,433 CAD and 6,899 PAD patients.
  • Assessed overall health status using the EQ-5D-3L visual analogue scale (VAS) and cognitive function with the Digit Symbol Substitution test (DSST).
  • Employed hierarchical Cox regression models, adjusting for demographics, medical history, and risk factors.

Main Results:

  • Higher EQ VAS and DSST scores were significantly associated with a lower risk of major adverse cardiovascular or limb events in both CAD and PAD cohorts.
  • Increased EQ VAS scores (per 10 units) showed hazard ratios of 0.89 for both CAD and PAD.
  • Increased DSST scores (per 5 units) showed hazard ratios of 0.95 for both CAD and PAD.

Conclusions:

  • Health status and cognitive functioning assessments can augment cardiovascular risk stratification in CAD and PAD.
  • Integrating these assessments into biomedical evaluations offers a more comprehensive approach to risk prediction.
Abstract

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