Key result
Novel LASSO-derived nomogram predicts failure to achieve target LDL-C reduction with ~0.72 AUC.
Why the study?
Some patients with ASCVD treated with PCSK9 inhibitors fail to achieve target LDL-C reduction, and risk factors for this nonattainment are not well defined.
Can a LASSO-derived model predict nonattainment of target LDL-C reduction in ASCVD patients treated with PCSK9 inhibitors?
Cohort (n=748)
No
Can a LASSO-derived model predict nonattainment of target LDL-C reduction in ASCVD patients treated with PCSK9 inhibitors?
Effect estimate: AUC 0.718 (95% CI 0.657-0.779)
A novel LASSO-derived nomogram incorporating statin therapy, DBP, ALT, TC, and baseline LDL-C provides moderate predictive accuracy for identifying ASCVD patients likely to fall short of LDL-C reduction targets while on PCSK9 inhibitors.
May identify ASCVD patients at risk of missing LDL-C targets on PCSK9 inhibitors; leaves open need for prospective validation before clinical use.
BACKGROUND: Proprotein 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: This 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.
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Zhang et al. (2025) conducted a cohort in Atherosclerotic cardiovascular disease (ASCVD) (n=748). Clinical risk factors (statin therapy, DBP, ALT, TC, LDL-C) during PCSK9 inhibitor treatment vs. Patients achieving LDL-C target reduction was evaluated on Model predictive accuracy for nonattainment of target LDL-C reduction (AUC) (AUC 0.718, 95% CI 0.657-0.779). A LASSO-derived nomogram incorporating statin therapy, diastolic blood pressure, ALT, total cholesterol, and LDL-C predicted nonattainment of LDL-C targets with an AUC of 0.718.
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