Nomogram based on clinical-ultrasonic features for predicting short-term prognosis of acute ischemic stroke
10.13929/j.issn.1672-8475.2024.06.006
- VernacularTitle:临床-超声列线图预测急性缺血性脑卒中短期预后
- Author:
Hang SU
1
;
Zhigang ZHOU
;
Jing LI
Author Information
1. 郑州大学第一附属医院超声科,河南 郑州 450052
- Keywords:
stroke;
ultrasonography;
prognosis
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(6):338-342
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of nomogram based on clinical-ultrasonic features for predicting short-term prognosis of acute ischemic stroke(AIS).Methods Totally 168 patients with AIS who underwent intravascular interventional therapies were retrospectively enrolled.The patients were divided into good prognosis group(n=134)and poor prognosis group(n=34)according to the score of modified Rankin scale 3 months after treatments.Clinical and ultrasonic data were compared between groups.Least absolute shrinkage and selection operator(LASSO)algorithm and Cox regression analysis were used to screen the independent impact factors for predicting short-term prognosis of AIS,and then clinical,ultrasonic and clinical-ultrasonic nomogram models were constructed based on the above independent impact factors,respectively.Receiver operating characteristic curves were drawn,and the areas under the curves(AUC)were calculated to evaluate the efficacy of each model for predicting short-term prognosis of AIS.Results The time span from onset to admission,National Institute of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),time-intensity curve-mean(TIC-M),time-intensity curve-peak(TIC-P)and the AUC of gamma curve were all independent impact factors for predicting short-term prognosis of AIS(all P＜0.05).The AUC of clinical,ultrasonic and clinical-ultrasonic nomogram model was 0.888,0.758 and 0.921,respectively,among which clinical-ultrasonic nomogram model had the highest predictive efficacy(both P＜0.05).Conclusion Clinical-ultrasonic nomogram could be used to effectively predict short-term prognosis of AIS.
