A simplified machine learning version of the Martin-Hopkins equation for assessing LDL cholesterol levels has been shown in a study to "match the accuracy of the original."

These findings may stand to make the equation's results "broadly accessible" and easier for labs to implement. Today's guidelines recommend treating LDL to "lower levels to reduce cardiovascular risks," but some equations can underestimate risk, leading to missed treatment opportunities. The Martin-Hopkins equation provides the most accurate results.

The 2013 equation "may require some laboratories to take extra steps to implement," which is why the researchers set out to make a streamlined version of the code.

The researchers found that "the machine-learning version of the Martin-Hopkins equation was similar to the original equation with a minimal difference of 0.5 mg/dL." Additionally, they confirmed the equations' superior accuracy for "classifying high-risk patients with lower ranges of LDL cholesterol levels."

Further testing the calculation and "ensuring its accuracy and reliability in populations outside of the group used to develop the model supports its ability to be generalized and widely used for clinical implementation." The open-access calculation can "improve implementation of the 2026 national dyslipidemia guideline," as well.