A LASSO-derived model for the prediction of nonattainment of target LDL-C reduction with PCSK9 inhibitors in patients with atherosclerotic cardiovascular disease

BackgroundProprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have demonstrated significant efficacy in lowering low-density lipoprotein cholesterol (LDL-C) levels in patients with atherosclerotic cardiovascular disease (ASCVD), but some fail to achieve the target levels. This study aimed to explore the potential risk factors associated with this nonattainment of target LDL-C reduction (NTR-LDLC) and develop a prediction model.MethodsThe population was randomly divided into derivation and verification subsets in a 7:3 ratio. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, we filtered the variables within the derivation set. Subsequently, we assessed the model's predictive accuracy for the NTR-LDLC in both subsets through the application of decision curve analysis (DCA) and the plotting of receiver operating characteristic (ROC) curves.ResultsThe study enrolled 748 patients, with 115 individuals experiencing NTR-LDLC. Using LASSO regression, five significant predictive factors associated with NTR-LDLC were identified: statin therapy, diastolic blood pressure (DBP), alanine aminotransferase (ALT), total cholesterol (TC), and LDL-C. Based on these results, a nomogram prediction model was constructed and validated, showing predictive accuracy with the area under the ROC curve (AUC) of 0.718 (95% confidence interval [CI]: 0.657 - 0.779) and 0.703 (95% CI: 0.605 - 0.801) for the derivation and validation sets, respectively.ConclusionsThis study presents a LASSO-derived predictive model that can be used to predict the risk of NTR-LDLC with PCSK9 inhibitors in patients with ASCVD.