Researchers at Johns Hopkins University have developed a machine learning-based version of the widely used Martin-Hopkins equation that simplifies the calculation of low-density lipoprotein cholesterol (LDL-C) without compromising accuracy.
The new approach, which is published in JAMA Cardiology, could make it easier for laboratories to estimate LDL-C, improving treatment decisions for patients at risk of cardiovascular disease.
“We’ve optimized the calculation of LDL cholesterol and made this equation accessible and easier for all labs to implement,” said Seth Martin, MD, MHS, senior study author and director of the Advanced Lipid Disorders Program and Digital Health Lab at the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease. “Our goal is to enable clinicians and patients to make better decisions about starting treatments that prevent heart attacks and strokes, and save lives.”
LDL-C is a major cause of atherosclerotic cardiovascular disease (ASCVD) and a primary treatment target. Current guidelines recommend using LDL-C cut offs, such as 70 mg/dL or 55 mg/dL (to convert to mmol/L multiply by 0.0259) in patients with ASCVD, to guide clinical lipid management.
The gold standard for measuring LDL-C concentration is preparative ultracentrifugation but this method is expensive and time-consuming. LDL-C concentrations are therefore usually estimated in routine practice.
One of the most accurate ways to estimate LDL-C concentration is the Martin-Hopkins method, which is recommended for clinical use in the U.S., Europe, and South America. However, implementation can be difficult because it requires users to look up an adjustable factor in a large table that is based on the patient’s triglyceride and non-high-density lipoprotein cholesterol levels.
“A lipid profile with low cholesterol and high triglycerides is the ultimate stress test of the LDL cholesterol calculation,” said Martin. He explains that a 5, 10 or 20 mg/dL difference, based on various equations, could change a person’s eligibility for treatment, such as with PCSK9 inhibitors, which have been shown to significantly lower LDL cholesterol levels. “It’s these types of on-the-cusp examples that benefit most from more accurate results,” he added.
To overcome this barrier and facilitate implementation, Martin and team used a transparent machine learning approach—multivariate adaptive regression splines—to create a simplified, formula-based LDL-C equation.
They trained and tested the tool on data from 4,939,528 adults and children (mean age, 56 years; 53% women) with complete lipid panel test results. These samples, which are representative of the U.S. population, had a median LDL cholesterol level of 114 mg/dL and came from the Very Large Database of Lipids.
The researchers report in JAMA Cardiology that the machine-learning version of the Martin-Hopkins equation estimated LDL-C concentrations that were similar to the original equation, with a minimal difference of 0.5 mg/dL.
Both Martin-Hopkins equations classified 90% of samples within the correct treatment category. Among other commonly used tools for LDL-C estimation, the Sampson-NIH equation correctly classified 86%, the modified Sampson-NIH equation classified 85%, and the Friedewald equation classified 83% in the correct category.
Importantly, said Martin, the investigators found that the Martin-Hopkins equations were the most accurate for classifying high-risk patients with lower ranges of LDL cholesterol levels.
When it came to assessing people who had triglyceride levels between 200 mg/dL and 399 mg/dL and LDL cholesterol levels less than 70 mg/dL, the Martin-Hopkins machine learning equation accurately classified 84% of high-risk samples, the original Martin-Hopkins equation classified 83%, the modified Sampson-NIH equation classified 72%, the Sampson-NIH equation classified 61%, and the Friedewald equation classified 40%.
Martin and co-authors conclude: “Given its high accuracy and straightforward implementation as a single line of code in laboratory information systems, [the Martin-Hopkins machine learning equation] is an alternative option to consider implementing in practice.”
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