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Individualized Prediction Of Stroke-Associated Pneumonia For Patients With Acute Ischemic Stroke

Authors Huang GQ, Lin YT, Wu YM, Cheng QQ, Cheng HR, Wang Z

Received 28 July 2019

Accepted for publication 15 October 2019

Published 7 November 2019 Volume 2019:14 Pages 1951—1962

DOI https://doi.org/10.2147/CIA.S225039

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Dr Zhi-Ying Wu


Gui-Qian Huang,1,* Yu-Ting Lin,2,* Yue-Min Wu,1 Qian-Qian Cheng,3 Hao-Ran Cheng,1 Zhen Wang1

1Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People’s Republic of China; 2Department of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People’s Republic of China; 3School of Mental Health, Wenzhou Medical University, Wenzhou 325000, Zhejiang, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Zhen Wang
Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, People’s Republic of China
Tel +86 577-555780166
Fax +86 577-55578033
Email wangzhen_wenzhou@163.com

Background: Stroke-associated pneumonia (SAP) is a serious and common complication in stroke patients.
Purpose: We aimed to develop and validate an easy-to-use model for predicting the risk of SAP in acute ischemic stroke (AIS) patients.
Patients and methods: The nomogram was established by univariate and multivariate binary logistic analyses in a training cohort of 643 AIS patients. The prediction performance was determined based on the receiver operating characteristic curve (ROC) and calibration plots in a validation cohort (N=340). Individualized clinical decision-making was conducted by weighing the net benefit in each AIS patient by decision curve analysis (DCA).
Results: Seven predictors, including age, NIHSS score on admission, atrial fibrillation, nasogastric tube intervention, mechanical ventilation, fibrinogen, and leukocyte count were incorporated to construct the nomogram model. The nomogram showed good predictive performance in ROC analysis [AUROC of 0.845 (95% CI: 0.814–0.872) in training cohort, and 0.897 (95% CI: 0.860–0.927) in validation cohort], and was superior to the A2DS2, ISAN, and PANTHERIS scores. Furthermore, the calibration plots showed good agreement between actual and nomogram-predicted SAP probabilities, in both training and validation cohorts. The DCA confirmed that the SAP nomogram was clinically useful.
Conclusion: Our nomogram may provide clinicians with a simple and reliable tool for predicting SAP based on routinely available data. It may also assist clinicians with respect to individualized treatment decision-making for patients differing in risk level.

Keywords: stroke-associated pneumonia, acute ischemic stroke, nomogram, prediction

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