ArticlemedRxiv : the preprint server for health sciences2025
Nonfasting, Telehealth-Ready LDL-C Testing With Machine Learning to Improve Cardiovascular Access and Equity.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
4 authors.
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Abstract
Importance: Current LDL-C testing requires 9-12 hour fasting and in-person visits, creating an access crisis: 40% of lipid panels occur outside fasting windows (yielding unreliable results), 60% of US counties lack cardiology services, and millions of patients with diabetes cannot safely fast. Meanwhile, telehealth infrastructure expanded 38-fold post-COVID, yet lipid workflows remain anchored to 1970s protocols. This mismatch drives Objective: To demonstrate that machine learning can transform lipid testing from a fasting-dependent, clinic-based bottleneck into an accurate, equitable, telehealth-ready service-eliminating three structural barriers simultaneously: fasting requirements, in-person visits, and racial algorithmic bias. Design Setting and Participants: Cross-sectional analysis of All of Us Research Program (n=3,477; test n=696). Crucially, 40.1% were tested outside traditional fasting windows, reflecting real-world practice. We evaluated performance stratified by fasting status, telehealth feasibility (labs-only configuration), racial equity metrics, and economic impact. Main Outcomes and Measures: Primary: MAE and calibration in non-fasting states. Secondary: Labs-only non-inferiority (±0.5 mg dL Results: The ML system demonstrated paradoxical superiority in non-fasting conditions-precisely when needed most. While equations deteriorated (Friedewald MAE 29.1 vs 25.9 mg dL Conclusions and Relevance: This ML approach helps address an estimated $2 billion annual problem of repeat testing while tackling three critical quality gaps in cardiovascular prevention: delayed treatment initiation, poor monitoring adherence, and specialty access barriers. By enabling accurate non-fasting, telehealth-compatible, race-free LDL-C estimation, it transforms lipid testing from an access barrier into an access enabler-particularly for the Medicaid, Medicare Advantage, and rural populations who drive both cost and outcomes in value-based care. From a technical standpoint, implementation requires only routine labs and <100 ms computation, making deployment feasible with existing infrastructure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.