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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
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.
Objectives:
We evaluated an alternative way to implement guidelines using an automated risk calculator and risk-based decision tool to calculate patients' risk of cardiovascular disease (CVD) and recommend therapies. We compared such an approach with traditional guidelines.
Study Design:
A retrospective cohort study of 1,506,109 Kaiser Permanente Southern California members 35 years or older.
Methods:
We estimated 3-year risks of fatal and nonfatal myocardial infarction and stroke using an independently developed risk calculator, then graphically compared risks with observed outcomes. We used the area under the receiver operating characteristics curve to assess discrimination, and the Hosmer-Lemeshow statistic to test fit. We compared the characteristics and outcomes of populations identified for medication therapy by the risk-based decision tool and traditional guidelines using bivariate statistics.
Results:
A risk score was obtained in 72% (1,082,158) of members. The risk calculator was fairly good in discrimination: the area under the curve was 0.774 (95% CI, 0.770-0.779) for myocardial infarction and 0.805 (95% CI, 0.801-0.808) for stroke. Predictiveness and fit was good based on graphical analysis and Hosmer-Lemeshow P < .0001. The risk-based decision tool identified high-risk patients for treatment who were not identified by traditional guidelines (3.80% of all those identified for statins, 3.04% for antihypertensives), as well as low-risk patients who were identified by guidelines (3.80% for statins, 2.51% for antihypertensives).
Conclusions:
The risk calculator provided risk estimates in most patients and demonstrated fairly good discrimination and predictiveness. The risk-based decision tool identified high-risk patients for treatment not identified by traditional guidelines, as well as low-risk patients for whom treatment may be unnecessary.
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