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The Role of Cardiovascular Risk Prediction Model Selection in Primary Prevention: An Observational Study of Statin
Petras Navickas1,2, Sigita Glaveckaitė1, Laura Lukavičiūtė-Navickienė1
1Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, LT-03101 Vilnius, Lithuania.
Insights
Choosing a cardiovascular risk prediction model significantly impacts statin eligibility in primary prevention. Statin recommendations varied widely across nine models, highlighting the need for careful selection and threshold consideration.
Area of Science:
- Cardiology
- Preventive Medicine
- Biostatistics
Background:
- Cardiovascular risk prediction models (RPMs) are crucial for guiding statin initiation in primary prevention.
- The concordance of treatment decisions across different RPMs in the same population is not well understood.
Purpose of the Study:
- To compare statin eligibility across nine common RPMs in a primary prevention cohort.
- To assess the agreement and concordance of treatment recommendations among these models.
Main Methods:
- Cross-sectional analysis of 11,174 adults aged 40-65 with metabolic syndrome.
- Evaluation using nine RPMs (SCORE2, PREVENT, PCE, ASSIGN, FRS-hCHD, AusCVDRisk, MESA, QRISK3, RRS) with guideline-mapped thresholds.
- Pairwise agreement assessed using Cohen's κ, Gwet's AC1, PPA/NPA, Jaccard index, and McNemar testing.
Main Results:
- Statin eligibility varied significantly, from 3.03% (AusCVDRisk) to 67.39% (SCORE2).
- Overall pairwise agreement was modest (median κ = 0.38), with stronger agreement for non-eligibility.
- Consensus eligibility decreased sharply with increasing stringency (k=1 to k=9).
Conclusions:
- Statin eligibility in primary prevention is highly dependent on the chosen RPM and its thresholds.
- Modest inter-model concordance suggests substantial variation in treatment decisions.
- Careful RPM selection and threshold setting are critical for consistent statin treatment recommendations.
Abstract:
Background and Objectives: Cardiovascular risk prediction models (RPMs) are widely used to guide statin initiation in primary prevention, yet the extent to which different models produce concordant treatment decisions in the same population remains insufficiently characterized. We compared statin eligibility across nine commonly used RPMs: SCORE2, PREVENT, PCE, ASSIGN, FRS-hCHD, AusCVDRisk, MESA, QRISK3, and RRS. Materials and Methods: We performed a cross-sectional analysis of 11,174 adults aged 40-65 years with metabolic syndrome enrolled in the Lithuanian High Cardiovascular Risk primary prevention program (LitHiR) and evaluated them at a single tertiary center during 2006-2023. Statin eligibility was determined for each RPM using guideline-mapped treatment thresholds. Pairwise agreement was assessed using Cohen's κ, Gwet's AC1, Positive and Negative Percent Agreement (PPA/NPA), the Jaccard index, and McNemar testing. Analyses were repeated by sex. Consensus eligibility was defined as treatment recommended by at least k of nine models. Results: Eligibility varied more than twenty-fold, from 67.39% (7530/11,174) with SCORE2 to 3.03% (339/11,174) with AusCVDRisk; intermediate estimates included PREVENT at 44.83%, QRISK3 at 39.00%, and PCE at 37.97%. Overall pairwise agreement was modest: κ ranged from 0.03 (SCORE2 vs. AusCVDRisk) to 0.67 (QRISK3 vs. ASSIGN), with a median κ of 0.38 (IQR: 0.19-0.51). Median AC1 was 0.58 (IQR 0.37-0.68). Agreement was stronger for non-eligibility than for eligibility (median NPA: 0.82 vs. median PPA: 0.53). Consensus eligibility declined from 73.5% at k = 1 to 45.1% at k = 3, 30.0% at k = 5, and 1.87% at k = 9, with the greatest sex divergence at intermediate stringency. Conclusions: In this real-world cohort with elevated cardiometabolic risk, statin eligibility was highly dependent on RPM choice and showed only modest inter-model concordance. Increasing consensus stringency rapidly reduced eligibility, indicating that RPM selection and embedded thresholds substantially influence statin treatment decisions in primary prevention.
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