Gurudu, Rachana

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.

Abstract Background Capillary self-collection (CSC) devices allow patients to collect blood samples at home, potentially reducing outpatient phlebotomy visits. This study aimed to (a) assess the patient experience with multiple commercially available CSC devices; (b) determine which laboratory tests are commonly ordered together, which could reduce the need for appointments; and (c) evaluate the analytical performance of these tests using CSC samples. Methods User experience for 3 CSC devices was evaluated. Clinical feasibility was determined by comparing test results in paired sera collected by venipuncture (VP) and CSC devices. VP samples were centrifuged within 2 h and tested immediately. CSC sera were centrifuged and tested both immediately and after delayed processing to simulate shipping temperature extremes (−20°C or 40°C). Basic metabolic panel, lipid panel, thyroid function cascade, and prostate-specific antigen were evaluated. Differences between VP and CSC collections were characterized according to their statistical and clinical differences. Results Patients reported CSC devices were easy to use and painless. Clinically significant differences between VP and CSC sera processed immediately were limited to potassium and bicarbonate. Following delayed processing of CSC serum samples (48 h at room temperature), clinically significant differences in potassium, bicarbonate, and glucose were observed. Frozen samples could not be analyzed. A 48 h delay at 40°C caused clinically significant differences in all analytes except creatinine, lipid panel, prostate-specific antigen, and the thyroid cascade. Conclusion Potassium, bicarbonate, and glucose were not stable at room temperature in CSC sera. CSC serum specimens may be a viable option but require analyte-specific evaluation and consideration of transportation conditions.