JACC. Advances · 2026
FIND Lp(a) Machine Learning Model: Targeted Screening Enrichment of Elevated Lipoprotein(a) in Atherosclerotic Cardiovascular Disease.
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MacDougall Diane E, Bertsch Spencer R, Gladders Barbara, Vaze Vikrant S, Martin Seth S, Kodukula Sai S, Sperling Laurence S, Khandelwal Abha, Knowles Joshua W, Clary Julie M, Mohamed Ghada A, Harris Kristen A, Isiadinso Ijeoma, Bardach Shoshana H, Merryman Scott H, Almeida Shone, Waring Ashley A, Helm Benjamin M, Razavi Alexander C, Liotta Mark A, Wilemon Katherine
Аннотация
BACKGROUND: The Flag, Identify, Network, Deliver (FIND) lipoprotein(a) (Lp[a]) machine learning model (MLM) is part of a Family Heart Foundation collaborative quality improvement initiative intended to accelerate Lp(a) screening adoption in U.S. health care systems.
OBJECTIVES: To train, test, and characterize important features of an MLM that uses deidentified medical records to flag adults with atherosclerotic cardiovascular disease (ASCVD) with likely elevated Lp(a).
METHODS: A Light Gradient-Boosting Machine MLM was trained (90% of data) and tested (10% of data) in 344,987 adults with ASCVD in the Family Heart Database. Percentage of adults with confirmed Lp(a) ≥125 nmol/L were compared (test data set vs cohort flagged by model), including submodels (Lp[a] ≥150 nmol/L, ≥200 nmol/L). The importance of 8 feature groups to MLM performance was determined using Tree Shapley Additive exPlanations.
RESULTS: The test data set (n = 34,499) included 8,556 (24.8%) adults with confirmed Lp(a) ≥125 nmol/L. FIND Lp(a) MLM flagged 1,553 adults, 856 (55.1%) had confirmed Lp(a) ≥125 nmol/L, representing a 2.2-fold screening enrichment due to the model (55.1% vs 24.8%). Supportive ≥150 nmol/L and ≥200 nmol/L submodels had 2.3 and 2.7 fold screening enrichment, respectively. Medication use was the most important feature group followed by diagnoses and lipid labs.
CONCLUSIONS: One-half of adults with ASCVD flagged by FIND Lp(a) MLM had confirmed elevated Lp(a) ≥125 nmol/L. A 2.2-fold screening enrichment was observed (≥125 nmol/L model); enrichment was greater with ≥150 and ≥200 nmol/L submodels. Early performance indicators support the central role of FIND Lp(a) MLM within the ongoing FIND Lp(a) program.
OBJECTIVES: To train, test, and characterize important features of an MLM that uses deidentified medical records to flag adults with atherosclerotic cardiovascular disease (ASCVD) with likely elevated Lp(a).
METHODS: A Light Gradient-Boosting Machine MLM was trained (90% of data) and tested (10% of data) in 344,987 adults with ASCVD in the Family Heart Database. Percentage of adults with confirmed Lp(a) ≥125 nmol/L were compared (test data set vs cohort flagged by model), including submodels (Lp[a] ≥150 nmol/L, ≥200 nmol/L). The importance of 8 feature groups to MLM performance was determined using Tree Shapley Additive exPlanations.
RESULTS: The test data set (n = 34,499) included 8,556 (24.8%) adults with confirmed Lp(a) ≥125 nmol/L. FIND Lp(a) MLM flagged 1,553 adults, 856 (55.1%) had confirmed Lp(a) ≥125 nmol/L, representing a 2.2-fold screening enrichment due to the model (55.1% vs 24.8%). Supportive ≥150 nmol/L and ≥200 nmol/L submodels had 2.3 and 2.7 fold screening enrichment, respectively. Medication use was the most important feature group followed by diagnoses and lipid labs.
CONCLUSIONS: One-half of adults with ASCVD flagged by FIND Lp(a) MLM had confirmed elevated Lp(a) ≥125 nmol/L. A 2.2-fold screening enrichment was observed (≥125 nmol/L model); enrichment was greater with ≥150 and ≥200 nmol/L submodels. Early performance indicators support the central role of FIND Lp(a) MLM within the ongoing FIND Lp(a) program.