CLN Daily

“Implementation Stories Contest” winner finds Martin equation leads to better LDL-C estimation

Jen A. Miller

This year, the Academy of Diagnostics & Laboratory Medicine held its first “Implementation Stories Contest” to recognize lab medicine professionals who have made a positive impact at their institutions by implementing an Academy recommendation. The winners, who will be highlighted at ADLM 2025 (formerly the AACC Annual Scientific Meeting & Clinical Lab Expo), are members of the clinical core laboratory (clinical chemistry) group at the University of Michigan in Ann Arbor.

The team won for successfully switching from using the Friedewald equation to the Martin equation for measuring low-density lipoprotein cholesterol (LDL-C) for patients at low risk for cardiovascular disease (CVD), in accordance with the 2024 “ADLM Guidance Document on the Measurement and Reporting of Lipids and Lipoproteins.” This switch led to improved classification of patients with dyslipidemia into LDL-C–based treatment groups. Out of 9,306 patients, total treatment group misclassification dropped from 50.66% of patients with the Friedewald equation to only 30.03% with the Martin equation.

“We recognized that laboratory medicine as a field is evolving. We need to update and make changes to existing processes even when we have done things a certain way for a very long time,” said Carmen Gherasim, PhD, section director of the clinical core laboratory and director of the clinical chemistry, toxicology, and emergency department laboratories.

In a special poster presentation during the Academy’s Networking Coffee break at ADLM 2025, Gherasim will share with attendees how her team navigated this shift in assessing patients’ cholesterol levels. Right now, most laboratories estimate LDL-C using the results from a lipid panel, which includes total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and triglycerides (TG). Those results are then put into the Friedewald equation to estimate LDL-C.

Although this method has been used for decades, it is prone to errors that often require follow-up testing to directly measure patients’ LDL-C. To avoid that extra step, Gherasim’s University of Michigan Clinical Chemistry lab implemented the Martin equation, which accounts for variations in non-HDL-C and TG to produce a better estimate of LDL-C and reduce the number of direct LDL-C tests that must be performed. 

Gherasim will describe that process, which included evaluating both the Martin equation and Sampson equation, since “the ADLM guidance has not specifically stated which equation laboratories should implement,” Gherasim said. The Sampson equation would have been easier to implement from a logistics point of view, but after a thorough evaluation, Gherasim and her group went with Martin instead. “While it required more work, we were fortunate to have the support of information specialists and our middleware vendor,” she said. 

The team started talking about changing their approach in 2021 and made the switch in July 2024. So far, in addition to the improvement in patient classification, they have also seen a reduction in direct LDL-C testing. 

“We like to be early adopters, even if that involves taking calculated risks,” she said. “But there is always some sense of safety knowing that you’re not going to be the only laboratory reporting using a certain equation. So we were looking for more guidance, and the ADLM guidance came really at the right time to help us.” 

She’s hoping to speak to attendees who are also evaluating which equation they use to determine LDL-C. “I’m looking forward to learning more about whether laboratories have started to change,” she said.

She’ll also discuss the next steps for their laboratory, including tracking how making this change in equation has affected patient care. “We are also working with the clinical team to look at the changes in prescriptions,” particularly in statins, she said.

Jen A. Miller is a freelance journalist who lives in Audubon, New Jersey. +Bluesky: @byjenamiller.bsky.social

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