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Risk Factors and a Nomogram for Predicting Intracranial Hemorrhage in Stroke Patients Undergoing Thrombolysis

Authors Zhou Z, Yin X, Niu Q, Liang S, Mu C, Zhang Y

Received 20 February 2020

Accepted for publication 20 April 2020

Published 11 May 2020 Volume 2020:16 Pages 1189—1197


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Yuping Ning

Zheren Zhou,1,* Xiaoyan Yin,2,3,* Qiuwen Niu,2 Simin Liang,2,4 Chunying Mu,2 Yurong Zhang2

1University Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 3Department of Neurology, Wuqi People’s Hospital, Yan’an, Shaanxi, People’s Republic of China; 4Department of Neurology, The First Affiliated Hospital of Xi’an Medical College, Xi’an, Shaanxi, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yurong Zhang Email

Purpose: Identifying stroke patients at risk of postthrombolysis intracranial hemorrhage (ICH) in the clinical setting is essential. We aimed to develop and evaluate a nomogram for predicting the probability of ICH in acute ischemic stroke patients undergoing thrombolysis.
Patients and Methods: A retrospective observational study was conducted using data from 345 patients at a single center. The patients were randomly dichotomized into training (2/3; n=233) and validation (1/3; n=112) sets. A prediction model was developed by using a multivariable logistic regression analysis.
Results: The nomogram comprised three variables: the presence of atrial fibrillation (odds ratio [OR]: 4.92, 95% confidence interval [CI]: 2.09– 11.57), the National Institutes of Health Stroke Scale (NIHSS) score (OR: 1.11, 95% CI: 1.04– 1.18) and the glucose level on admission (OR: 1.27, 95% CI: 1.08– 1.50). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.828 (0.753– 0.903) and 0.801 (0.690– 0.911), respectively. The Hosmer–Lemeshow test revealed good calibration in both the training and validation sets (P = 0.509 and P = 0.342, respectively). The calibration plot also demonstrated good agreement. A decision curve analysis demonstrated that the nomogram was clinically useful.
Conclusion: We developed an easy-to-use nomogram model to predict ICH, and the nomogram may provide risk assessments for subsequent treatment in stroke patients undergoing thrombolysis.

Keywords: stroke, intracranial hemorrhage, thrombolysis, nomogram, prognosis

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