Martin, Seth S.

Importance Although the Martin-Hopkins equation is widely validated and used to estimate low-density lipoprotein cholesterol (LDL-C) for clinical care, a simplified equation could streamline implementation and enable broader adoption. Objective To develop a simplified machine learning–based alternative to the Martin-Hopkins equation using multivariate adaptive regression splines (LDL-C–MH-MARS) and compare its performance with the Friedewald (LDL-C–F), Sampson–National Institutes of Health (LDL-C–S), Modified Sampson (LDL-C–MS), and Martin-Hopkins (LDL-C–MH) equations. Design, Setting, and Participants The Very Large Database of Lipids is a cross-sectional database of clinical lipid measurements from a population-representative convenience sample of adult and pediatric patients. Lipid measurements were performed by Vertical Auto Profile ultracentrifugation between October 1, 2015, and June 30, 2019. The study included patients with complete lipid panel data who were randomly assigned to a training set and a test set. External validation against preparative ultracentrifugation-based LDL-C measurements was performed in reference laboratory datasets from the Mayo Clinic (broad LDL-C concentrations) and the Further Cardiovascular Outcomes Research With Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibition in Subjects with Elevated Risk (FOURIER) trial (observations from patients with low LDL-C concentrations while taking evolocumab). Study data were analyzed from January to October 2025. Exposures LDL-C–F, LDL-C–S, LDL-C–MS, LDL-C–MH, and LDL-C–MH-MARS equations. Main Outcomes and Measures Accuracy of estimated vs ultracentrifugation-measured LDL-C concentration based on bias, root mean square error (RMSE), and concordance by guideline-based categories. Results The study included 4 939 528 patients (mean [SD] age, 56 [16] years; 2 635 486 female [53%]) with complete lipid panel data who were randomly assigned to a training set (n = 3 292 889) and a test set (n = 1 646 639). LDL-C–MH-MARS equation demonstrated a very low median (IQR) bias of −0.1 (−2.1 to 1.8) mg/dL (to convert to millimoles per liter, multiply by 0.0259), comparable with LDL-C–MH. The median (IQR) difference between the MARS and original Martin-Hopkins equations (ie, LDL-C–MH-MARS – LDL-C–MH) was −0.5 (−1.2 to 0) mg/dL, further suggesting comparability between the 2 methods. The RMSE was smallest for LDL-C–MH-MARS (4.7 mg/dL) and LDL-C–MH (4.9 mg/dL), followed by LDL-C–S (5.8 mg/dL), LDL-C-MS (6.0 mg/dL), and LDL-C–F (7.2 mg/dL). The proportion of patients correctly classified according to clinical categories was nearly identical for LDL-C–MH-MARS (89.7%) and LDL-C–MH (89.6%) but lower for LDL-C–S (86.3%), LDL-C–MS (84.7%), and LDL-C–F (83.1%). The LDL-C–MH-MARS, LDL-C–MH, LDL-C–MS, LDL-C–S, and LDL-C–F equations underestimated LDL-C concentration in 16%, 17%, 28%, 39%, and 60%, respectively, when classifying LDL-C less than 70 mg/dL in patients with triglyceride concentrations of 200 to 399 mg/dL (to convert to millimoles per liter, multiply by 0.0113). The patterns of results were similar in external validation datasets, with the LDL-C–MH-MARS and LDL-C–MH equations demonstrating the highest accuracy. Conclusions and Relevance Study results suggest that the new machine learning–based LDL-C equation provided results comparable with those of the original Martin-Hopkins method while simplifying implementation to a single equation.

BACKGROUND: While health systems have implemented resources to improve lipid management in people with atherosclerotic cardiovascular disease, the population-level effectiveness is unclear. METHODS: Electronic health record data from patients with atherosclerotic cardiovascular disease between January 1, 2021, and December 31, 2023, across 16 US academic health systems were analyzed. Atherosclerotic cardiovascular disease (defined by diagnosis and procedure codes) was established on or before the index date defined as the date of the most recent LDL-C (low-density lipoprotein-cholesterol) or the last day of the study period for those without an LDL-C available. Systems were surveyed to identify lipid management programs (eg, prevention/lipid clinic, electronic health record–based tools, and lipid management algorithms). Sequential multilevel multivariable logistic regression modeling was used to (1) assess the system-level association between an LDL-C <70 mg/dL and the presence of lipid management programs, adjusting for patient-level demographic and clinical characteristics, and (2) whether system-level uptake of statin or nonstatin lipid-lowering therapy was associated with an LDL-C <70 mg/dL. RESULTS: We included 1 738 042 patients with atherosclerotic cardiovascular disease (median, 88 365 per system; range, 17 086–2 48 371). About 81% of the health systems had prevention or lipid clinics. The proportion of those on statins ranged from 31.0% to 60.4% across health systems (median, 46.3%). Lower proportions were observed in the use of high-intensity statins (16.2%–33.1%), nonstatins (2.6%–8.1%), and combination lipid-lowering therapy (1.7%–6.3%). Among those with an LDL-C level available (n=890 521), 42.5% had an LDL-C <70 mg/dL, ranging 35.8% to 49.4% across systems. There was no association between the odds of achieving LDL-C <70 mg/dL and health system lipid management algorithm, prevention clinics/lipid clinics, or electronic health record–based tools. Neither system-level high-intensity statin utilization nor nonstatin use was associated with an LDL-C <70 mg/dL. CONCLUSIONS: Despite variability in lipid management resources and lipid-lowering therapy use across systems, many did not reach an LDL-C <70 mg/dL, and there was low uptake of lipid-lowering therapy. Innovative implementation strategies are needed for system-level lipid management.

Background Although telehealth cardiac rehabilitation (CR) may improve access, there are concerns about its long‐term effectiveness and impact on equity as compared with in‐person CR. Our objective was to tailor a patient‐centered telehealth CR program for diverse populations. Methods CR patients and caregivers were recruited between January and September 2023 from 4 US academic medical centers. Participants engaged in human‐centered design sessions to iteratively refine a telehealth CR program. Sessions had planned topics, but there was variation across sites to account for site‐specific needs and participant feedback. Sessions were qualitatively analyzed using rapid template analysis with preselected behavioral science constructs and other emergent codes. Results The study included 21 participants (71% aged ≥60 years, 48% women, 62% non‐Hispanic White individuals; 90% CR patients, 10% CR caregivers). Participants thought that telehealth CR could be helpful for personalized support at home and convenience but recognized that technology is not always easy to use. Some expressed concerns about the safety of telehealth CR, especially at the beginning, and desired monitoring through a mobile device or video observation of exercise. Safety protocols and technology training were developed, which addressed concerns about telehealth CR. Opportunities for social support with telehealth CR were also desired. From these findings, an implementation toolkit was developed, including a graphic program description, safety plan, home exercise plan for during and after CR, and scripts for technology training and individual and group telehealth visits. Conclusions A patient‐centered telehealth CR program and implementation toolkit were systematically tailored to address the needs of diverse populations.

Background: Nationally, many ASCVD patients fail to achieve an LDL-C <70 mg/dL, and uptake of both statin and non-statin therapies is low. The degree to which this varies across health systems is less clear. Methods: A cross sectional analysis was performed where lipid levels and lipid lowering therapies (LLT) were assessed using electronic health record data in patients with a previous diagnosis of ASCVD. The data was obtained across 14 US healthcare systems between 1/1/2021-12/31/2022. Proportions of patients with an active prescription of any statin, high intensity statin, ezetimibe, PCSK9i, and combination therapy (two or more agents) within 395 days of the most recent LDL-C value (index date) was evaluated overall and by participating site. Additionally, the proportion of patients with an LDL-C <70 mg/dL at the index date was also assessed. Results: Across 14 health systems, 1,118,623 patients with ASCVD were identified (median 61,840 per health system, range 8,161-182,315). Overall, 675,776 (60.4%) had an LDL-C level in the past year (range 39.1% - 70.8%). Of those with a lipid level, achievement of LDL-C <70 mg/dL ranged from 34.6-47.2%. In total, 42.6% were on any statin, 20.1% were on a high intensity statin, 4.3% on ezetimibe, and 1.2% on a PCSK9i. Only 2.9% were on combination therapy of a statin with ezetimibe or a PCSK9i. Variability was seen across health systems in utilization of each of these therapies, however even in the highest performing health systems, LLT uptake and achievement of LDL-C < 70mg/dL remained low ( Figure). Conclusion: Variability in utilization of LLT in ASCVD patients between health systems suggests that system-level factors may impact achieving guideline-based LDL-C goals. Despite the variability, the highest proportion of patients achieving an LDL-C <70mg/dL remained under 50% indicating the need for aggressive implementation efforts.

Background: Delivering cardiac rehabilitation (CR) by telehealth may increase participation and promote equitable access to CR. Our objective was to tailor a patient-centered telehealth CR program to diverse populations. Methods: We recruited participants with varying CR experience (e.g. attended >1 session, did not attend CR, and caregivers) between January and September 2023 from four US academic medical centers. Participants engaged in human-centered design sessions on CR perspectives and a proposed telehealth CR program. Sessions were qualitatively analyzed using rapid template analysis with pre-selected behavioral science constructs and other emergent codes. Based on participant feedback, we iteratively revised the telehealth CR program and created an implementation toolkit. Results: Among 21 participants (76% ≥65 years, 50% female, 60% non-Hispanic White; 90% attended >1 session, 10% caregiver of a CR participant, 5% prefer not to answer), CR was overall regarded as valuable. Benefits included accountability, confidence, and feeling better. Social support was appreciated from family, friends, program co-participants, and CR staff. Participants emphasized the importance of early goal setting, accountability, and program individualization. Participants thought that using technology for telehealth CR could be helpful for accountability, facilitating relationships, and convenience. However, some participants had concerns about safety, especially at the start of their telehealth program, and desired checks such as a monitoring device or observation of exercise over video. Through human-centered design, safety protocols and technology training were developed, which participants thought addressed their concerns about participating in telehealth CR. The implementation toolkit informed by human-centered design included a participant journey, safety plan, home exercise plan for use during and after CR, and scripts for technology training and telehealth visits. Selected quotes are presented in Figure 1. Conclusions: We developed a patient-centered telehealth CR program and implementation toolkit. Future research will test the comparative effectiveness of in-person and telehealth CR on patient-centered outcomes.

Cardiac rehabilitation (CR) is a guideline-recommended, multidisciplinary program of exercise training, risk factor management, and psychosocial counseling for people with cardiovascular disease (CVD) that is beneficial but underused and with substantial disparities in referral, access, and participation. The emergence of new virtual and remote delivery models has the potential to improve access to and participation in CR and ultimately improve outcomes for people with CVD. Although data suggest that new delivery models for CR have safety and efficacy similar to traditional in-person CR, questions remain regarding which participants are most likely to benefit from these models, how and where such programs should be delivered, and their effect on outcomes in diverse populations. In this review, we describe important gaps in evidence, identify relevant research questions, and propose strategies for addressing them. We highlight 4 research priorities: (1) including diverse populations in all CR research; (2) leveraging implementation methodologies to enhance equitable delivery of CR; (3) clarifying which populations are most likely to benefit from virtual and remote CR; and (4) comparing traditional in-person CR with virtual and remote CR in diverse populations using multicenter studies of important clinical, psychosocial, and cost-effectiveness outcomes that are relevant to patients, caregivers, providers, health systems, and payors. By framing these important questions, we hope to advance toward a goal of delivering high-quality CR to as many people as possible to improve outcomes in those with CVD.

Background Accurate measurement of the cholesterol within lipoprotein(a) (Lp[a]‐C) and its contribution to low‐density lipoprotein cholesterol (LDL‐C) has important implications for risk assessment, diagnosis, and treatment of atherosclerotic cardiovascular disease, as well as in familial hypercholesterolemia. A method for estimating Lp(a)‐C from particle number using fixed conversion factors has been proposed (Lp[a]‐C from particle number divided by 2.4 for Lp(a) mass, multiplied by 30% for Lp[a]‐C). The accuracy of this method, which theoretically can isolate “Lp(a)‐free LDL‐C,” has not been validated. Methods and Results In 177 875 patients from the VLDbL (Very Large Database of Lipids), we compared estimated Lp(a)‐C and Lp(a)‐free LDL‐C with measured values and quantified absolute and percent error. We compared findings with an analogous data set from the Mayo Clinic Laboratory. Error in estimated Lp(a)‐C and Lp(a)‐free LDL‐C increased with higher Lp(a)‐C values. Median error for estimated Lp(a)‐C <10 mg/dL was −1.9 mg/dL (interquartile range, −4.0 to 0.2); this error increased linearly, overestimating by +30.8 mg/dL (interquartile range, 26.1–36.5) for estimated Lp(a)‐C ≥50 mg/dL. This error relationship persisted after stratification by overall high‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol subtypes. Similar findings were observed in the Mayo cohort. Absolute error for Lp(a)‐free LDL‐C was +2.4 (interquartile range, −0.6 to 5.3) for Lp(a)‐C<10 mg/dL and −31.8 (interquartile range, −37.8 to −26.5) mg/dL for Lp(a)‐C≥50 mg/dL. Conclusions Lp(a)‐C estimations using fixed conversion factors overestimated Lp(a)‐C and subsequently underestimated Lp(a)‐free LDL‐C, especially at clinically relevant Lp(a) values. Application of inaccurate Lp(a)‐C estimations to correct LDL‐C may lead to undertreatment of high‐risk patients.

Introduction: Big data techniques offer a novel opportunity to characterize individuals with extreme phenotypes. In this context, we aimed to describe the prevalence of classical Fredrickson-Levy dyslipidemia phenotypes at the extremes of HDL-C levels in a cross-sectional big data study. Methods: We examined 848,801 U.S. adults and children from the Very Large Database of Lipids 1.0 who were referred for lipoprotein testing from 2009 to 2011. We categorized patients into HDL-C percentile categories (<0.1th, 0.1th to 99th to 99.9th, and >99.9th). We examined the prevalence of Fredrickson-Levy dyslipidemia phenotypes (I, IIa, IIb, III, IV and V) within these categories. We identified those who did not meet criteria for any classical dyslipidemia phenotype as the continuum group. Results: Type I and V were mostly present at extremely low HDL-C levels. Type IIa was more prevalent in high vs. low HDL-C levels. Type III was 2-fold more prevalent in extremely low vs. high HDL-C levels. Type IV was the most prevalent classical dyslipidemia phenotype in our population, and was the most frequent at low HDL-C percentiles. About 50% of the extremely low and 90% of the extremely high HDL-C levels were classified into the continuum group. Conclusion: In our cross-sectional big data analysis, there was a significantly higher prevalence of most classical dyslipidemia phenotypes at extremely low HDL-C compared with extremely high HDL-C levels. Only types I and V were more prevalent in extreme groups than general population. To some extent, very low HDL-C levels may be determined by inheritable, highly atherogenic dyslipidemias.