Optimal risk assessment intervals for primary prevention of cardiovascular disease: a population-based two-stage landmarking study.

Abstract

Introduction: The recommended assessment intervals for primary prevention of cardiovascular disease (CVD) differ in major international guidelines. We aimed to provide empirical evidence on the optimal frequency of CVD risk assessment to inform future guidelines.

Methods: We estimated the expected time to cross the 10-year CVD risk treatment threshold of 10% using extended two-stage landmarking for more than 2 million people using UK primary care electronic health records between April 2004 and May 2019 from the Clinical Practice Research Datalink GOLD Database (CPRD GOLD), which was linked to hospital admissions data from the Hospital Episodes Statistics (HES) dataset and national mortality records from the Office for National Statistics (ONS). We grouped people based on their sex, initial risk level and age, and computed various percentiles of the expected crossing times per group. Based on the percentiles, optimal assessment intervals were identified and their performance was evaluated comparing to the current recommended intervals in the UK.

Results: Our results showed that the expected crossing times for people with lower initial risk were much longer than those with higher initial risk. Within each initial risk group, expected time to crossing the risk treatment thresholds was shorter in people aged ≥65 years. Based on the median expected crossing times, our recommended intervals for women with initial 10-year risk of 7.5%-10%, 5%-7.5%, 2.5%-5% or<2.5% are 3 (1 if ≥65 years old), 7 (4), 10 (6) and 10 (10) years, respectively; intervals for men are 2 (1), 5 (5), 9 (9) and 10 (10) years. These intervals outperformed the 5-yearly risk reassessment for all individuals currently recommended in the UK.

Conclusions: Our evidence suggests that CVD risk assessment intervals for primary prevention should be stratified by sex, initial risk level and age. For the UK population, our method found risk assessment intervals that reduce the number of assessments required while shortening the waiting time to the next assessment for those most in need.