Prediction of presence and severity of coronary artery disease using prediction for atherosclerotic cardiovascular disease risk in China scoring system

BACKGROUND Coronary artery disease(CAD)is one of the leading causes of death and disease burden in China and worldwide.A practical and reliable prediction scoring system for CAD risk and severity evaluation is urgently needed for primary prevention.AIM To examine whether the prediction for atherosclerotic cardiovascular disease risk in China(China-PAR)scoring system could be used for this purpose.METHODS A total of 6813 consecutive patients who underwent diagnostic coronary angiography were enrolled.The China-PAR score was calculated for each patient and CAD severity was assessed by the Gensini score(GS).RESULTS Correlation analysis demonstrated a significant relationship between China-PAR and GS(r=0.266,P<0.001).In receiver operating characteristic curve analysis,the cut-off values of China-PAR for predicting the presence and the severity of CAD were 7.55%with a sensitivity of 55.8%and specificity of 71.8%[area under the curve(AUC)=0.693,95%confidence interval:0.681 to 0.706,P<0.001],and 7.45%with a sensitivity of 58.8%and specificity of 67.2%(AUC=0.680,95%confidence interval:0.665 to 0.694,P<0.001),respectively.CONCLUSION The China-PAR scoring system may be useful in predicting the presence and severity of CAD.

DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest.Although widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple SNPs.To address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data.Evaluations demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.