Potential of risk-based population guidelines to reduce cardiovascular risk in a large integrated health system

Galina Inzhakova, Hui Zhou, Macdonald Morris

  • 1Department of Research and Evaluation, Kaiser Permanente Southern California, 100 S Los Robles, Pasadena, CA 91101.

Insights

An automated cardiovascular disease risk calculator accurately identified high-risk patients for therapy, including those missed by traditional guidelines. This tool also identified low-risk patients who may not require medication, improving treatment decisions.

Area of Science:

  • Cardiovascular Medicine
  • Health Informatics
  • Clinical Decision Support

Background:

  • Traditional guidelines for cardiovascular disease (CVD) risk assessment can be complex.
  • Automated tools may offer a more efficient and accurate method for risk stratification.
  • Implementing evidence-based guidelines is crucial for effective CVD prevention.

Purpose of the Study:

  • To evaluate an automated risk calculator and decision tool for cardiovascular disease (CVD) risk assessment.
  • To compare the performance of this automated approach against traditional guideline implementation.
  • To determine if the automated tool can better identify patients for therapy.

Main Methods:

  • A retrospective cohort study of over 1.5 million adult members was conducted.
  • An independently developed risk calculator estimated 3-year risks of myocardial infarction and stroke.
  • Discrimination and fit were assessed using receiver operating characteristic curves and the Hosmer-Lemeshow statistic.

Main Results:

  • The risk calculator achieved good discrimination for myocardial infarction (AUC 0.774) and stroke (AUC 0.805).
  • The automated tool identified high-risk patients missed by traditional guidelines for statin and antihypertensive therapy.
  • It also identified low-risk patients for whom guideline-based treatment might be unnecessary.

Conclusions:

  • The automated risk calculator demonstrated good performance in estimating CVD risk.
  • The risk-based decision tool effectively identified both high-risk and low-risk patients, optimizing therapeutic recommendations.
  • This automated approach offers a promising alternative for implementing CVD guidelines in clinical practice.
Abstract

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