Reducing Lipid Panel Error Allowances to Improve the Accuracy of Cardiovascular Risk Stratification

Outcome-based analytical performance specifications: current status and future challenges

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

Optimization of time interval for the measurement of plasma lipids for cardiovascular disease risk assessment

BACKGROUND

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.

RESEARCH DESIGN AND METHODS

Using lipid values and other risk factors from the National Health and Nutrition Examination Survey (NHANES) (n = 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 (n = 17,329) and the Lifelines Cohort study for lipids (n = 133,540). The crossing of high and intermediate-risk thresholds were calculated by time to determine optimum intervals for lipid testing.

RESULTS

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.

CONCLUSIONS

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.