Fig 1.
Principal model structure.
Table 1.
Model comparators and risk classes.
Table 2.
Model input values and assumptions.
Table 3.
Baseline characteristics of the model cohort.
If not otherwise stated, continuous variables are presented as median, 25th percentile, and 75th percentile. Binary variables are described as absolute and relative frequencies.
Table 4.
Management and CVD related outcomes.
Table 5.
The 95% confidence intervals for the ICER were estimated from 2.5th and 97.5th percentiles.
Fig 2.
Incremental cost-effectiveness scatter plot from probabilistic sensitivity analyses.
Probabilistic sensitivity analysis (PSA) with 500 iterations of microsimulations with 20,000 samples. Each dot is a single iteration. Dashed line represents the willingness-to pay threshold of 50,000 € per QALY. Incr.: Incremental.
Fig 3.
Cost-effectiveness acceptability curve of the alternative strategy S-SCORE.
At a willingness-to-pay threshold (WTP) of 50,000 € per QALY gained, the S-SCORE strategy had a probability of 80% of being cost-effective.
Fig 4.
Sensitivity analyses tornado diagram assessing the influence of variables on the incremental cost-effectiveness ratio.
Independent analyses (100 repeated microsimulation runs with 20,000 bootstrapped samples) for each parameter value varied between the lower and higher bound. Incremental cost-effectiveness ratio (ICER) for S-SCORE vs. SCORE. The base-case analysis is indicated by the vertical line at 27,440 € per QALY gained. The diagram shows variables with a difference in ICER compared to the base-case analysis of at least 10% in any direction.
Fig 5.
Proportion of subjects with a change in management in S-SCORE compared to SCORE and resulting number needed to screen.
Base-case (◼); Female* (○); Male* (●); Age 40–70* (+); Moderate risk* (◊); Moderate & high risk* (◆); Age 40–70, moderate and high risk* (Δ); Age 40–70, high risk* (▲); Derived Management scenario (□). All analyses followed the base-case sampling approach except *, in which subgroup analyses were derived from a microsimulation including 250,000 individuals. NNS: Number needed to screen to prevent one event.