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Identifying severe hypercholesterolemia in participants of ASCVD trials: Implications for trial conduct and
Abhayjit Singh1, Julie St John1, Luke Laffin1
1Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA (Singh, John, Laffin, Skoza, Breummer, Cho, Nissen, and Sarraju).
Insights
Calculating pretreatment LDL-C in clinical trials identifies severe hypercholesterolemia (SH) and undiagnosed familial hypercholesterolemia (FH). This approach enriches trials with high-risk patients and aids in screening for FH, a condition often overlooked.
Area of Science:
- Cardiology
- Clinical Trials
- Genetics
Background:
- Cardiovascular outcomes trials require sufficient event accrual for statistical power.
- Severe hypercholesterolemia (SH), defined as LDL-C ≥190 mg/dL, indicates elevated cardiovascular risk and potential undiagnosed familial hypercholesterolemia (FH).
- Lipid-lowering therapies can obscure diagnostic LDL-C levels during trial screening.
Purpose of the Study:
- To assess if calculating pretreatment LDL-C at trial enrollment can identify undiagnosed SH.
- To determine if this method facilitates opportunistic FH screening within trials.
Main Methods:
- Correction factors were applied to baseline LDL-C to calculate pretreatment LDL-C in 11,993 ACCELERATE trial participants.
- Pretreatment LDL-C ≥190 mg/dL defined SH; SH plus family history defined probable FH.
- Multivariable Cox regression analyzed the association between SH and major adverse cardiovascular events (MACE).
Main Results:
- 15.1% of ACCELERATE participants had SH, and 2.1% met criteria for probable FH, significantly higher than general population prevalences.
- Across 57 guideline-informing trials, weighted SH prevalence was 14%.
- Pretreatment LDL-C ≥190 mg/dL was linked to higher MACE incidence (aHR 1.19, P=.021).
Conclusions:
- SH and probable FH are more common in cardiovascular trial participants than in the general population.
- SH is associated with an increased risk of MACE.
- Calculating pretreatment LDL-C at enrollment can identify higher-risk individuals for trials and enable opportunistic FH screening, potentially reducing costs and identifying an underdiagnosed condition.
Background:
Cardiovascular outcomes trials depend on adequate event accrual for statistical power. Patients with severe hypercholesterolemia (SH), defined as low-density lipoprotein cholesterol (LDL-C) ≥190 mg/dL, have elevated cardiovascular risk and frequently harbor undiagnosed familial hypercholesterolemia (FH). Lipid-lowering therapies mask diagnostic LDL-C levels at screening.
Objective:
To determine whether pretreatment LDL-C calculation at trial enrollment could identify undiagnosed SH and facilitate opportunistic FH screening.
Methods:
We applied correction factors to baseline LDL-C to calculate pretreatment LDL-C in ACCELERATE (Assessment of Clinical Effects of Cholesteryl Ester Transfer Protein Inhibition with Evacetrapib in Participants at a High Risk for Vascular Outcomes) participants (n = 11,993) and across 57 trials informing the 2018 Multisociety Blood Cholesterol Guideline. SH was defined as pretreatment LDL-C ≥190 mg/dL; clinically probable FH as SH plus family history of hypercholesterolemia in a first- or second-degree relative. The association between SH and major adverse cardiovascular events (MACE) was examined using multivariable Cox regression.
Results:
In ACCELERATE, 15.1% (1809/11,993) had SH and 2.1% (255/11,993) met criteria for probable FH, 3- and 5-fold higher than population prevalences (∼5% and ∼0.4%). Across 57 guideline-informing trials, weighted SH prevalence was 14% (range 0%-77%). Pretreatment LDL-C ≥190 mg/dL was associated with higher MACE incidence among ACCELERATE participants (15.1% vs 13.5%; adjusted hazard ratio 1.19, 95% CI 1.03-1.38, P = .021).
Conclusion:
SH and probable FH are more prevalent in cardiovascular outcomes trial participants than in the general population, and SH was associated with increased MACE risk. Pretreatment LDL-C calculation at enrollment can enrich trials for higher-risk participants and enable opportunistic identification of probable FH for cascade screening. This dual approach may reduce trial costs whilst identifying a critically underdiagnosed condition.
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