{"doi":"10.1101/2025.10.27.25338909","title":"Nonfasting, Telehealth-Ready LDL-C Testing With Machine Learning to Improve Cardiovascular Access and Equity","abstract":"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 ~ 20 million unnecessary repeat visits annually, disproportionately burdening Medicaid populations, essential workers, and rural communities. 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 −1 margin); racial equity (Black-White performance gap); economic savings from eliminated repeat visits; and classification accuracy at treatment thresholds (70, 100, 130 mg dL −1 ). 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 −1 fasting, slopes 0.58–0.61), ML maintained accuracy (24.0 vs 23.2 mg dL −1 , slopes 0.99–1.07), achieving 17.2% improvement over Friedewald when non-fasting vs 10.4% fasting. Labs-only configuration proved non-inferior (MAE=-0.12, p<0.001), enabling national retail-pharmacy and home-testing workflows. The system achieved racial equity without race input (Black-White gap −0.19 mg dL −1 , CI includes zero) while providing greatest improvement for Black patients (19% vs 11% for White). Economically, eliminating 4,000 repeat visits per 10,000 tests helps address an estimated $2 billion annual repeat-testing cost burden and yields $815,000 total savings per 10,000 tests ($245,000 direct healthcare, $570,000 patient costs), with break-even at just 750 tests. 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.","journal":"medRxiv","year":2025,"id":579671,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9482,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1489293,"name":"Nana Yaw Osafo","orcid":null,"position":1,"is_corresponding":false},{"id":368966,"name":"John Kwagyan","orcid":"0000-0003-2711-1969","position":2,"is_corresponding":false},{"id":803476,"name":"William M. Southerland","orcid":null,"position":3,"is_corresponding":false},{"id":1488848,"name":"Ronald Doku","orcid":"0000-0003-4471-8420","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":null,"created_at":"2026-07-19T02:58:34.718602Z","pmid":"41282840","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}