Cardiovascular prevention in general practice: development and validation of an algorithm

Abstract

Objective: General practice visits are a unique opportunity to identify and treat individuals with a high cardiovascular (CV) risk. However, a case-finding strategy suited to the daily general practice is not provided in the CV prevention guidelines. We wanted to create, validate and test an algorithm for global CV risk assessment and management.

Methods: The algorithm was 1) developed based on evidence from epidemiological studies and clinical trials, 2) validated in a population-based cohort and 3) tested by randomly selected general practitioners (GPs) who rated its usefulness and applicability.

Results: 1) Screening for seven clinical risk factors (RF) allowed a quick classification of patients in four CV risk typologies: obvious high risk (previous CV event and/or type 2 diabetes) in 17%, obvious low risk (no RF) in 14%, smoking-related risk (single RF) in 6%, or undetermined risk (any other RF) to further evaluate in 63% patients. Inter-physician reproducibility for risk prediction was excellent. Overall, predicted risk was high, moderate and low in 25, 17 and 58% of the patients, respectively. 2) These risk predictions were validated in a cohort of 962 men followed over 10 years. 3) Most GPs reported that the algorithm was applicable and useful, while half of them started using it frequently in their daily practice.

Conclusion: This algorithm is a new, pragmatic and evidence-based strategy for systematic and global CV risk management. It was validated at the population level, and shown to be applicable and useful in the daily general practice.