Optimization of time interval for the measurement of plasma lipids for cardiovascular disease risk assessment
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Lipid testing for atherosclerotic cardiovascular disease (ASCVD) risk is often performed every 4–6 years, but we hypothesized that the optimum time interval may vary depending on baseline risk. Using lipid values and other risk factors from the National Health and Nutrition Examination Survey (NHANES) (<i>n</i> = 9,704), we calculated a 10-year risk score with the pooled-cohort equations. Future risk scores were predicted by increasing age and projecting systolic blood pressure (SBP) and lipid changes, using the mean-percentile age group change in NHANES for SBP (<i>n</i> = 17,329) and the Lifelines Cohort study for lipids (<i>n</i> = 133,540). The crossing of high and intermediate-risk thresholds were calculated by time to determine optimum intervals for lipid testing. Time to crossing risk thresholds depends on baseline risk, but the mean increase in the risk score plateaus at 1% per year for those with a baseline 10-year risk greater than 15%. Based on these findings, we recommend the following maximum time intervals for lipid testing: baseline risk < 15%: 5-years, 16%: 4-years, 17%: 3-years, 18%: 2-years, and 19%: ≤1-year. Testing patients for lipids who have a higher baseline risk more often could identify high-risk patients sooner, allowing for earlier and more effective therapeutic intervention.
动脉粥样硬化性心血管疾病(atherosclerotic cardiovascular disease, ASCVD)风险的血脂检测通常每4~6年开展一次,但我们假设最佳检测间隔可能随基线风险水平而异。本研究借助美国国家健康与营养检查调查(National Health and Nutrition Examination Survey, NHANES,样本量n=9704)中的血脂指标与其他风险因素,采用合并队列方程计算10年风险评分。通过结合NHANES中收缩压(systolic blood pressure, SBP)按年龄组平均百分位变化数据(样本量n=17329)与生命线队列研究(Lifelines Cohort study,样本量n=133540)的血脂变化数据,基于年龄增长、收缩压与血脂变化预测未来风险评分。通过计算高风险与中风险阈值的跨越时间,确定血脂检测的最佳间隔。风险阈值的跨越时间取决于基线风险水平,但对于基线10年风险>15%的人群,风险评分的年均增幅趋于平稳,约为1%。基于上述研究结果,我们建议血脂检测的最长间隔如下:基线风险<15%时为5年,16%时为4年,17%时为3年,18%时为2年,19%及以上时≤1年。对基线风险较高的患者更频繁地开展血脂检测,可更早识别高危人群,从而实现更早且更有效的治疗干预。
提供机构:
Taylor & Francis
创建时间:
2024-01-22



