# LASSO-Nomogram model for ultrasound-atherosclerosis correlation and diagnostic verification in anterior circulation

## TL;DR

This study developed a LASSO-Nomogram model to predict the severity of anterior circulation cerebral atherosclerosis using ultrasound and Doppler parameters.

## Contribution

A novel LASSO-Nomogram model was created for individualized risk assessment of cerebral atherosclerosis using ultrasound data.

## Key findings

- The model showed excellent discrimination with a C-index of 0.850 in training and 0.796 in validation.
- Key predictors included lipid profiles, carotid IMT, plaque stability, and MCA hemodynamics.
- HDL and stable plaques were protective factors, while LDL, IMT, and MCA parameters were risk factors.

## Abstract

This study aimed to investigate the relationship between cervical vascular ultrasound/transcranial Doppler ultrasound (TCD) parameters and anterior circulation cerebral atherosclerosis severity, and to develop a LASSO-Nomogram predictive model for clinical assessment.

We retrospectively analyzed 350 patients with anterior circulation atherosclerosis, randomly divided into training (n = 245) and validation (n = 105) sets. Collected data included: (1) demographics and medical history; (2) lipid profiles; (3) ultrasound parameters [carotid intima-media thickness (IMT), plaque stability, internal carotid artery stenosis rate, middle cerebral artery peak systolic velocity (MCA-PSV), end-diastolic velocity (MCA-EDV), pulsatility index (PI), resistance index (RI)]. Patients were stratified by atherosclerosis severity (mild-moderate vs. severe). LASSO regression identified key predictors for nomogram prediction model construction, with model performance rigorously evaluated.

Baseline characteristics were balanced between training set and validation set (p > 0.05). Univariate analysis identified 10 significant factors (all p < 0.05). LASSO regression selected 9 key predictors: age, high-density lipoprotein (HDL), low-density lipoprotein (LDL), carotid IMT, plaque stability, stenosis rate, and MCA hemodynamics (PSV, EDV, RI). Multivariate analysis showed HDL (OR = 7.410) and stable plaques (OR = 3.987) as protective factors of arteriosclerosis, while LDL (OR = 0.621), carotid IMT (OR = 0.038), MCA-PSV (OR = 0.978), MCA-EDV (OR = 0.960), and RI (OR = 0.010) were risk factors of arteriosclerosis (all p < 0.05). The model demonstrated excellent discrimination (training C-index = 0.850; validation = 0.796) with AUCs of 0.849 (95% CI: 0.792–0.907) and 0.801 (95% CI: 0.698–0.904), respectively. Decision curve analysis confirmed clinical utility across threshold probabilities of 10–80%.

Cervical vascular ultrasound and TCD parameters effectively reflect anterior circulation atherosclerosis severity. Our LASSO-Nomogram model provides clinicians with a reliable, visualized tool for individualized risk assessment, potentially improving patient management.