14 Clinical Implementation of the PREVENT Equations Example: How can you integrate the PREVENT equations into your workflow? Here is an example of a patient for whom LLT would be appropriate when using the PREVENT equations: Patient: 58-year-old woman Medical History: • Hypertension treated with lisinopril • Type 2 diabetes • BMI: 33 kg/m² • No prior cardiovascular disease • Nonsmoker • No chronic kidney disease Clinical Data: • Total cholesterol: 220 mg/dL • HDL cholesterol: 45 mg/dL • LDL cholesterol: 145 mg/dL • Systolic blood pressure: 138 mm Hg (treated) • Hemoglobin A1c: 7.4% • Estimated glomerular filtration rate (eGFR): 92 mL/min/1.73m2 PREVENT Risk Assessment Using the PREVENT equations, the patient’s estimated 10-year risk of ASCVD is 8.3%, placing her in an intermediate risk category. Clinical Interpretation A 10-year ASCVD risk above 7.5% suggests a meaningful likelihood of developing a cardiovascular event over the next decade. Given her: • Elevated LDL cholesterol (145 mg/dL) • Diabetes • Hypertension • Obesity • Intermediate PREVENT-estimated cardiovascular risk Initiating lipid-lowering therapy is recommended as part of a comprehensive risk-reduction strategy. Example Patient Discussion “Based on your health information, your chance of developing a problem in your heart or blood vessels over the next 10 years is 8.3%. That means out of 100 people like you, with similar risk factors, approximately 8 will likely develop ASCVD in the next 10 years. Much of this risk is related to factors we can address, including your cholesterol level. Starting a cholesterol-lowering medication, such as a statin, can help lower your risk and reduce the likelihood of future heart disease, stroke or heart failure.” The PREVENT equations are intended to support clinicianpatient discussions by estimating overall CVD risk in addition to ASCVD and HF. LLT should still be guided by current cholesterol management guidelines, incorporating: • LDL-C level • Presence of diabetes • Age • Overall PREVENT-estimated risk • Patient preferences and shared decision-making This type of patient is a good example because the PREVENT equations identify intermediate cardiovascular risk driven by traditional risk factors as well as obesity and diabetes, helping to inform a conversation about the potential benefit of LLT. Integration of PREVENT equations into the electronic health record systems is underway to support clinical workflow and risk assessment. Electronic Health Record Integration
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