Abstract
Background and purpose
Mechanical thrombectomy (MT) is a standard care for most acute ischemic stroke (AIS) patients. For AIS patients underwent MT, predicting the patients at high risk of unfavorable outcome and adjusting therapeutic strategies accordingly can greatly improve patient outcomes. We aimed to develop and validate a nomogram for individualized prediction of Chinese AIS patients underwent MT.
Methods
We conducted a multicenter prospective study including 238 AIS patients who underwent MT from January 2014 to December 2018. The main outcome measure was three-month unfavorable outcome (modified Rankin Scale 3–6). A nomogram was generated based on multivariate logistic model. We assessed the discriminative performance by using the area under the receiver-operating characteristic curve and calibration of risk prediction model by using the Hosmer–Lemeshow test.
Results
In NAC nomogram, NIHSS (National Institutes of Health Stroke Scale) score on admission (OR: 1.193, p < 0.0001), Age (OR: 1.025, p = 0.037) and Creatinine (OR: 1.028, p < 0.0001) remained independent predictors of 3-month unfavorable outcome in Chinese AIS patients treated with MT. The NAC nomogram exhibited an area under the curve of 0.816 for predicting functional impairment. Calibration was good (p = 0.560 for the Hosmer–Lemeshow test).
Conclusions
The NAC nomogram is the first nomogram developed and validated in Chinese AIS patients treated with MT and it may be used to predict 3 months unfavorable outcome for these patients.
Disclosure statement
The authors have indicated that they have no conflicts of interest regarding the content of this article. X. Li, Y. Zou and J. Hu contributed equally to this work. Z.-H. Zhao and J.-J. Zou concepted, designed, and supervised the study. X.-M. Li, Y.-K. Liu, C.-P. Huang, Y.-J. Shan, X.-D. Pan, C. Liu, Z. Zhen, and B.-L. Song acquired the data. X. Li, Y. Zou, and J.-J. Zou analyzed and interpreted the data, provided statistical analysis, had full access to all of the data in the study, and are responsible for the integrity of the data and the accuracy of the data analysis. X. Li, L. Nyame, M. Ibrahim and C. Sun drafted the manuscript, J.-J. Zou and Z.-H. Zhao critically revised the manuscript for important intellectual content.