Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients

Abstract

BackgroundFamilial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD.Materials and MethodsIn total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features.ResultsAfter hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method (AUC_class[94.85 ± 0.47], AUC_prob[98.66 ± 0.27]). The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH.ConclusionThe HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.

Applications of machine learning in familial hypercholesterolemia

“…Aiming to develop a risk assessment method based on Chinese patients with ASCVD, Wang L et al developed and evaluated a hybrid FH risk assessment tool (HFHRAT), a combination of three FH risk assessment tools and stacking models ( 43 ). To develop this tool, two risk assessment tools, modified DLCN for China (mDLCN) criteria and the Taiwan (TW) criteria ( Supplementary Material ), had the best performance among the 10 tools (the mDLCN criteria had a higher sensitivity and specificity of 97.22% and 92.90%, respectively, and the Taiwan criteria had the highest specificity of 100%) using the DLCN criteria as the reference ( 44 , 45 ).…”

“…In the interpretation of HFHRAT, it was suggested from the individual conditional expectation (ICE) and partial dependence plot (PDP) that individuals aged <75 years with LDL-c >4 mmol/L were more likely to exhibit FH. In addition, comparing the predictive characteristics of the five tools, HFHRTA could adjust the position of the median of the data, resulting in a lower false negative rate than existing tools, indicating that this hybrid tool has a higher ability to predict high-risk FH patients ( 43 ). The research and improvement of such risk assessment tools will also be able to play a role in the screening and diagnosis of FH, effectively improving the prognosis of the disease.…”

Risk Factors and Modifiers for Cardiovascular Disease Assessment of Patients with Heterozygous Familial Hypercholesterolaemia

“…Ultimately, the quantification of the dynamic interplay between these domains may yield a more refined FH risk assessment and, therefore, personalised treatment optimisation. Given the multiple inputs and complexity of this assessment, artificial intelligence may have a role to play in appropriately stratifying risk in HeFH patients [30].…”