Abstract
BackgroundLDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations.MethodsThe final analysis included participants from two independent population-based cohorts: 129,930 from the Gangnam Severance Health Check-up (GSHC) and 46,470 participants from the Korean Initiatives on Coronary Artery Calcification registry (KOICA). The DNN model was derived from the GSHC dataset and validated in the KOICA dataset. We measured our proposed model's performance according to bias, root mean-square error (RMSE), proportion (P)10–P20, and concordance. P was defined as the percentage of patients whose LDL was within ±10–20% of the measured LDL. We further determined the RMSE scores of each LDL equation according to Pooled cohort equation intervals.ResultsOur DNN method has lower bias and root mean-square error than Friedewald's, Martin's, and NIH equations, showing a high agreement with LDL-C measured by homogenous assay. The DNN method offers more precise LDL estimation in all pooled cohort equation strata.ConclusionThis method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.
Highlights
Cardiovascular disease (CVD) risk assessment is the first step in managing and preventing major vascular events and all-cause mortality [1]
We aimed to develop a deep neural network (DNN)-based Low-density lipoprotein cholesterol (LDL-C) estimating model (LDL-CDNN ) and compare the performance of this DNN model with that of previous formulas for LDLC calculation using two large independent datasets of Korean populations
This study used the data of two independent populationbased cohorts: Gangnam Severance Health Check-up (GSHC) dataset and Korean Initiatives on Coronary Artery Calcification (KOICA) registry
Summary
Cardiovascular disease (CVD) risk assessment is the first step in managing and preventing major vascular events and all-cause mortality [1]. Providers need a method that guides LDL-C management strategies based on a patient’s risk of CVD [3, 4]. Many working groups recommend setting individualized targets for LDL-C based on a patient’s total CVD risk level to manage CVD [2, 3]. In 2013, the ACC/AHA developed pooled cohort equations (PCEs) to predict the 10year risk for atherosclerotic cardiovascular disease (ASCVD) events and recommended the use of these PCEs in treatment for blood cholesterol [5]. LDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations
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