Nomogram to Predict the Number of Thrombectomy Device Passes for Acute Ischemic Stroke with Endovascular Thrombectomy

This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT).

We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool.

In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005-1.172; p = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115-0.486; p < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028-1.155; p = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860-0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did "treat all" or "treat none" strategies.

The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT.